On Efficiency of Midzuno-Sen Strategy under Two-phase Sampling
|
|
- Matthew Young
- 6 years ago
- Views:
Transcription
1 International Journal of Statistics and Analysis. ISSN Volume 7, Number 1 (2017), pp Research India Publications On Efficiency of Midzuno-Sen Strategy under Two-phase Sampling P. A. Patel 1 and Shraddha Bhatt 2 Department of Statistics, Sardar Patel University, Vallabh Vidyanagar Abstract This article deals with the ratio estimation of the finite population total under the Midzuno-Sen sampling scheme when complete auxiliary information is not available but another auxiliary variable, closely related to first auxiliary variable but remotely related to the study variable, is available. A chain ratio estimator in two-phase sampling is suggested. An empirical study is carried out to investigate the performance of the suggested estimator relative to the convention two-phase estimator. key words: Two-phase Sampling, Midzuno-Sen sampling, Ratio estimator, Regression estimator. MS Classification: 62D05: 62F10 1. INTRODUCTION Consider a finite population U = {1,, i, N}. Let y and x be the study variable and auxiliary variable taking the values y i and x i respectively for the population unit i. When the two variables are strongly related but no information is available on the population total X = U x i (or mean X), we wish to estimate the population total Y = U y i of y from a sample s, obtained through a two-phase selection. Scheme I: The first-phase sample s of fixed size n is drawn using simple random sampling without replacement (srswor) to observe only x in order to estimate X. Given
2 20 P. A. Patel and Shraddha Bhatt s, the second-phase sample s of fixed size n is drawn again using srswor to observe y only. The two-phase sampling ratio estimator is defined as Y Rd = Nx y x = N x s y s n x s (1) where x (x s ) is the mean (total) of x for the sample s, y (y s ), x (x s ) are means (totals) of y and x for the sample s. Suppose that the population mean Z of another auxiliary variable z closely related to x but compare to x remotely related to y is available. For instance, y could be acres of corn harvested for grain in 1964, x acres under corn in 1964 and z acres under corn in Chand (1975), Kiregyera (1980, 1984), Srivastava et al. (1990) used a second auxiliary variable z closely related to x to suggest different improved estimators under Scheme I. Raj (1965) suggests the following two-phase sample selection procedure. Scheme II: The initial sample s of size n is selected with probability proportional to z with replacement and the second phase sample s of size n is a subsample of s, selected with equal probabilities without replacement. An unbiased estimator of Y is provided by Y DRd = (y i np i s ) + k [ (x i n p i s ) (x i np i )] (2) s where k is a suitably chosen scalar. To compute this estimator it is necessary to assess the value of k. Often this is difficult. Motivated by this Srivenkataramana and Tracy (1989) have modified the sampling scheme. Scheme III: Select the first phase sample s of size n with probability proportional to z and select a subsample s of size n units with probability proportional to x z, with replacement. Under this scheme an unbiased estimator of Y is Y STd = Z (y i nx i ) (x i n z i ) (3) s s Experience shows that of all the unequal probability sampling without replacement schemes available in literature those due to Midzuno (1952) and Sen (1953), and Rao et al. (1962) have wider practical applicability since these procedures are quite simple and involve less computations at estimation stage compared to others. In this article to estimate the population total we will use the following two-phase sampling procedure.
3 On Efficiency of Midzuno-Sen Strategy under Two-phase Sampling 21 Scheme IV: A first-phase sample s, of size n, is drawn from U according to a design srswor. Given s, a second-phase sample s, of size n, is drawn from s according to the Midzuno-Sen (MS) sampling. The MS scheme of sampling describes the selection of the first unit with probability proportional to a given size measure (x) and remaining (n-1) units of the sample by srswor. The size of the measure is an auxiliary variable and is positively correlated with the study variable. Under this scheme the ordinary ratio estimator is known to be unbiased for the population total. The variance of this estimator is not available in literature in a meaningful form. Rao and Vijayan (1977) investigated the problem of estimating the variance of the ratio estimator, given at (1), under the MS sampling. The same problem addressed by Patel and Patel (2008, 2010a, 2010b). In Section 2 we suggest a chain ratio estimator using two auxiliary variables. To study the performance of the suggested estimator a small scale simulation is presented in Section 3 and a comparison of strategy is given in Section 4. The conclusion is given in Section THE SUGGESTED ESTIMATOR In this section a chain ratio estimator Y Rd is suggested. = N(x Z z )(y x) (4) Let S uv (s uv ) denote the population (sample) covariance between u and v variables, C u = S u U, the coefficient of variation of u variable and ρ uv = S uv S u S v, the correlation coefficient of u and v (u, v = y, x, z). Let B( ) and Var( ) denote respectively the bias and variance of an estimator under a given design. Then, under srswor, for large sample approximations: y = Y(1 + δ 1 ), x = X(1 + δ 2 ), z = Z(1 + δ 3 ) such that E(δ i ) = 0 for i = 1,2,3 we have the following results. Here we assume that δ i < 1 i. Theorem. Under Scheme IV, approximate bias and variance, too(n 1 ), of Y Rd are given by B(Y Rd ) = 1 f n Y(C 2 z ρ yz C y C z ) (5) Var(Y Rd ) = 1 f n Y 2 (C 2 y + C 2 z 2ρ yz C y C z ) + V [ f n C 2 z 2( ZV) ] (6) ZV
4 22 P. A. Patel and Shraddha Bhatt where = 1 N [[ a iiy 2 i z i + a ( a ii y 2 i z j + 2 a ij y i y j z j ) i U i j U i j U + b a ij y i y j z k ] i j k U with a ij sand V are defined below and a = (n 1), b = (n 1)(n 2) and (N 1) (N 1)(N 2) f = n N. Proof. The expectation of Y Rd is evaluated using the conditional argument E( ) = E 1 E 2 ( s ) as E(Y Rd ) = NE 1 [(Z z )E 2 (x y x)] = NE 1 [(Z z )y ] and consequently using the formula for approximate bias of the ordinary ratio estimator (see, Cochran, 1977) of the population total under SRSWOR we obtain the bias of Y Rd as given in (5). Next, the formula for variance of Y Rd is The first component is readily seen to be Var(Y Rd ) = V 1 E 2 (Y Rd ) + E 1 V 2 (Y Rd ) (7) V 1 E 2 (Y Rd ) = V 1 (Ny Z z ) 1 f n Y 2 (C y 2 + C z 2 2ρ yz C y C z ) (8) Note that at second phase the ratio estimator x y x is unbiased for y under MS sampling design with variance (see, Rao, 1972) where a ii = 2 v = a ii y i + a ij y i y j s s X n 1 ) 1 1 and a S i x ij = X s ( N 1 n 1 ) 1 1 S i&j x s ( N 1 Also, note that v is unbiased for V = n N a 2 iiy i + n (n 1) U N(N 1) a ij y i y j
5 On Efficiency of Midzuno-Sen Strategy under Two-phase Sampling 23 at first phase (i.e., under srswor). The second component is written as E 1 V 2 (Y Rd ) = E 1 [(Z z ) 2 V 2 (x y x) ] = E 1 [( Z 2 z ) v ] Inserting v = V(1 + δ 4 ), δ 4 < 1, and using the expansion (1 + δ 3 ) 2 = 1 2δ 3 + 3δ O(n 1 ), after omitting the terms of δ s having power greater than two, we obtain E 1 V 2 (Y Rd ) V[1 + 3Var(δ 3 ) 2Cov(δ 3, δ 4 )] (9) Noting that Var(δ 3 ) = 1 f 2 C n z Cov(δ 3, δ 4 ) = [E(z v ) ZV] ZV = [ ZV] ZV (9) is written as E 1 V 2 (Y Rd ) V [ f C 2 2( ZV) n z ] (10) ZV Inserting (8) and (10) in (7) we obtain (6). 3. SIMULATION STUDY The preceding estimators were compared empirically on 3 natural populations given below. For comparison of the estimators,a two-phase sample was drawn using srswor and the M-S sampling scheme from each of the populations and these estimators were computed. This procedure was repeated M = 5000 times. For each estimator Y d, the relative percentage bias RB(Y d) = 100 (Y Y) Y and the relative efficiency ascompared to Y were calculated, where RE(Y d) = MSE sim (Y ) MSE sim ( Y d) Y = M j=1 Y dj M M and MSE sim (Y d) = (Y dj Y) 2 j=1 (M 1)
6 24 P. A. Patel and Shraddha Bhatt Data set I: Murthy (1967) y: No of cultivators x : Area in square miles z : No. of households Data set II: Murthy (1967) y: workers at household industry x : Cultivated area (in acres) z : No. of households Data set III: Fisher data (combined all three data sets) y: Patel width x : Sepal length z : Patel length The following table shows corresponding relative bias and MSE for the data sets. Data Set n n N Estimator RB% MSE sim RE% I Y Rd E+08 - Y Rd E II Y Rd E+07 - Y Rd E III Y Rd E+03 - Y Rd E The above simulation reveals that (1) the absolute RBs % of both the estimators are in reasonable range and (2) the efficiency of the suggested estimator is substantial as compared to the conventional estimator. 4. COMPARISON OF STRATEGIES Here, we compare empirically the suggested strategy (mean a pair of design and estimator) given at (4) with the conventional strategy given at (1), denoted respectively by H 2 (srswor MS sampling,, Y Rd ) and H 1 (srswor srswor, Y Rd ). For the comparison of two strategies H 1 (p 1, Y 1) and H 2 (p 2, Y 2) we have computed the percentage gain in efficiency of H 2 over H 1 as Gain (H 2, H 1 ) = ( V p 1 (Y 1) 1) 100% V p2 (Y 2)
7 On Efficiency of Midzuno-Sen Strategy under Two-phase Sampling 25 The following table presents gain due to MS sampling scheme over srswor in twophase sampling. Data Set I II III Gain(H 2, H 1 ) 33% -4% 26% Remark. We have compared empirically Des Raj Strategy, Srivenkataramana and Tracy strategy and suggested strategy given at (2), (3) and (4) with the conventional strategy given at (1), for many real data. It has been observed that for most for the populations considered under simulation study Des Raj strategy performed very well. For sake of brevity the simulated results are not presented here. 5. CONCLUSION It can be concluded that there may be situations in which it is considered important to select the sample units with probability proportionate to some measure of size x, information on which is not readily available but could be collected at moderate cost for a fairly large sample and if the population mean Z of another auxiliary variable z closely related to x but compare to x remotely related to y is available then incorporating this extra auxiliary information at the estimation stage improves the convention estimator. However, pps without replacement schemes for general n are not easy to implement but the MS scheme is easy to execute. REFERENCES [1] Chand, L. (1975). Some ratio-type estimator based on two or more auxiliary variables. Unpublished Ph. D. dissertation, Iowa State University, Iowa. [2] Cochran, W. G. (1977). Sampling Techniques 3 rd edn (New York, John Wiley) [3] Fisher, R. A. (1936). The use of multiple measurements in taxonomic problem, Annals of Eugenics, 7, [4] Patel, J.S. and Patel, P.A. (2008). A Monte Carlo comparison of some estimators of finite population Total under Midzuno sampling, Journal of the Indian Statistical Association, 46, 2, [5] Patel, J.S. and Patel, P.A. (2010a). On Non-negative and Improved variance estimation for ratio estimator under Midzuno-Sen sampling scheme, Statistics in Transition-new series, 10(3), [6] Patel, J.S. and Patel, P.A. (2010b). Comparison of some variance estimators of the ratio estimator in presence of two auxiliary variables, Interstat.
8 26 P. A. Patel and Shraddha Bhatt [7] Kiregyera, B. (1980). A chain ratio-type estimator in finite population double sampling using two auxiliary variables, Metrika 27, [8] Kiregyera, B. (1984). Regression-type estimators using two auxiliary variables and model of double sampling from finite populations, Metrika 31, [9] Midzuno, H.(1952). On the sampling system with probability proportionate to sum of sizes. Ann. Inst. Statist. Math., Tokyo, 3, [10] Murthy, M. N. (1967) Sampling Theory and Methods, Calcutta: Statistical Publishing Society. [11] Raj, D. (1964). On double sampling for pps estimation, Ann. Math. Statist. 35 (2), [12] Raj, D. (1965). On sampling over two occasions with probability proportional to size. Ann. Math. Statisti. 36, [13] Rao, J.N.K., Hartley, H.O. and Cochran, W.G. (1962). On a simple procedure ofunequal probability sampling without replacement. J. Roy. Statist. Soc. B24, [14] Rao, J.N.K. and Vijayan, K. (1977). On estimating the variance in sampling with probability proportional to aggregate size. Journal of the American Statistical Association, 72, [15] Sen, A. R. (1953). On estimate of the variance in sampling with varying probabilities, J. Ind. Soc. Statist., 7, [16] Srivastava, R. S., Srivastava, S. R., Khare, B. B. (1990). A generalized chain ratio estimator for mean of finite population. J. Ind. Soc. Agricult. Statist. 42, [17] Srivenkataramana, T. and Tracy, D. S. (1989). Two-phase sampling for selection with probability proportional to size in sample surveys. Biometrika 76 (4),
A CHAIN RATIO EXPONENTIAL TYPE ESTIMATOR IN TWO- PHASE SAMPLING USING AUXILIARY INFORMATION
STATISTICA, anno LXXIII, n. 2, 2013 A CHAIN RATIO EXPONENTIAL TYPE ESTIMATOR IN TWO- PHASE SAMPLING USING AUXILIARY INFORMATION Rohini Yadav Department of Applied Mathematics, Indian School of Mines, Dhanbad,
More informationEfficient and Unbiased Estimation Procedure of Population Mean in Two-Phase Sampling
Journal of Modern Applied Statistical Methods Volume 5 Issue Article --06 Efficient and Unbiased Estimation Procedure of Population Mean in Two-Phase Sampling Reba Maji Indian School of Mines, Dhanbad,
More informationA Chain Regression Estimator in Two Phase Sampling Using Multi-auxiliary Information
BULLETI of the Malaysian Mathematical Sciences Society http://math.usm.my/bulletin Bull. Malays. Math. Sci. Soc. () 8(1) (005), 81 86 A Chain Regression Estimator in Two Phase Sampling Using Multi-auxiliary
More informationAdmissible Estimation of a Finite Population Total under PPS Sampling
Research Journal of Mathematical and Statistical Sciences E-ISSN 2320-6047 Admissible Estimation of a Finite Population Total under PPS Sampling Abstract P.A. Patel 1* and Shradha Bhatt 2 1 Department
More informationEXPONENTIAL CHAIN DUAL TO RATIO AND REGRESSION TYPE ESTIMATORS OF POPULATION MEAN IN TWO-PHASE SAMPLING
STATISTICA, anno LXXV, n. 4, 015 EXPONENTIAL CHAIN DUAL TO RATIO AND REGRESSION TYPE ESTIMATORS OF POPULATION MEAN IN TWO-PHASE SAMPLING Garib Nath Singh Department of Applied mathematics, Indian School
More informationA note on a difference type estimator for population mean under two phase sampling design
Khan and Al Hossain SpringerPlus 0165:73 DOI 10.1186/s40064-016-368-1 RESEARCH Open Access A note on a dference type estimator for population mean under two phase sampling design Mursala Khan 1* and Abdullah
More informationEffective estimation of population mean, ratio and product in two-phase sampling in presence of random non-response
Effective estimation of population mean, ratio and product in two-phase sampling in presence of random non-response G. N. Singh, A. K. Sharma, A.Bandyopadhyay and C.P aul Keywords Abstract This paper presents
More informationEective estimation of population mean, ratio and product in two-phase sampling in presence of random non-response
Hacettepe Journal of Mathematics and Statistics Volume 47 1 018, 79 95 Eective estimation of population mean, ratio and product in two-phase sampling in presence of random non-response G. N. Singh, A.
More informationInternational Journal of Scientific and Research Publications, Volume 5, Issue 6, June ISSN
International Journal of Scientific and Research Publications, Volume 5, Issue 6, June 2015 1 Combination of Two Exponential Ratio Type Estimators for Estimating Population Mean Using Auxiliary Variable
More informationOn The Class of Double Sampling Ratio Estimators Using Auxiliary Information on an Attribute and an Auxiliary Variable
International Journal of Statistics and Systems ISSN 0973-675 Volume, Number (07), pp. 5-3 Research India Publications http://www.ripublication.com On The Class of Double Sampling Ratio Estimators Using
More informationImprovement in Estimating the Finite Population Mean Under Maximum and Minimum Values in Double Sampling Scheme
J. Stat. Appl. Pro. Lett. 2, No. 2, 115-121 (2015) 115 Journal of Statistics Applications & Probability Letters An International Journal http://dx.doi.org/10.12785/jsapl/020203 Improvement in Estimating
More informationOn The Class of Double Sampling Exponential Ratio Type Estimator Using Auxiliary Information on an Attribute and an Auxiliary Variable
International Journal of Computational and Applied Mathematics. ISSN 89-966 Volume, Number (07), pp. -0 Research India ublications http://www.ripublication.com On The Class of Double Sampling Exponential
More informationImproved General Class of Ratio Type Estimators
[Volume 5 issue 8 August 2017] Page No.1790-1796 ISSN :2320-7167 INTERNATIONAL JOURNAL OF MATHEMATICS AND COMPUTER RESEARCH Improved General Class of Ratio Type Estimators 1 Banti Kumar, 2 Manish Sharma,
More informationSongklanakarin Journal of Science and Technology SJST R3 LAWSON
Songklanakarin Journal of Science and Technology SJST-0-00.R LAWSON Ratio Estimators of Population Means Using Quartile Function of Auxiliary Variable in Double Sampling Journal: Songklanakarin Journal
More informationUnequal Probability Designs
Unequal Probability Designs Department of Statistics University of British Columbia This is prepares for Stat 344, 2014 Section 7.11 and 7.12 Probability Sampling Designs: A quick review A probability
More informationO.O. DAWODU & A.A. Adewara Department of Statistics, University of Ilorin, Ilorin, Nigeria
Efficiency of Alodat Sample Selection Procedure over Sen - Midzuno and Yates - Grundy Draw by Draw under Unequal Probability Sampling without Replacement Sample Size 2 O.O. DAWODU & A.A. Adewara Department
More informationAsymptotic Normality under Two-Phase Sampling Designs
Asymptotic Normality under Two-Phase Sampling Designs Jiahua Chen and J. N. K. Rao University of Waterloo and University of Carleton Abstract Large sample properties of statistical inferences in the context
More informationNew Modified Ratio Estimator for Estimation of Population Mean when Median of the Auxiliary Variable is Known
New Modified Ratio Estimator for Estimation of Population Mean when Median of the Auxiliary Variable is Known J. Subramani Department of Statistics Ramanujan School of Mathematical Sciences Pondicherry
More informationA New Class of Generalized Exponential Ratio and Product Type Estimators for Population Mean using Variance of an Auxiliary Variable
ISSN 1684-8403 Journal of Statistics Volume 21, 2014. pp. 206-214 A New Class of Generalized Exponential Ratio and Product Type Abstract Hina Khan 1 and Ahmad Faisal Siddiqi 2 In this paper, a general
More informationResearch Article A Generalized Class of Exponential Type Estimators for Population Mean under Systematic Sampling Using Two Auxiliary Variables
Probability and Statistics Volume 2016, Article ID 2374837, 6 pages http://dx.doi.org/10.1155/2016/2374837 Research Article A Generalized Class of Exponential Type Estimators for Population Mean under
More informationSOME IMPROVED ESTIMATORS IN MULTIPHASE SAMPLING ABSTRACT KEYWORDS
Pak. J. Statist. 010, Vol. 61 195-0 SOME IMPROVED ESTIMATORS IN MULTIPHASE SAMPLING Muhammad Hanif 1, Muhammad Qaiser Shahbaz, and Zahoor Ahmad 3 1 Lahore University of Management Sciences, Lahore, Pakistan
More informationResearch Article An Improved Class of Chain Ratio-Product Type Estimators in Two-Phase Sampling Using Two Auxiliary Variables
robability and Statistics, Article ID 939701, 6 pages http://dx.doi.org/10.1155/2014/939701 esearch Article An Improved Class of Chain atio-roduct Type Estimators in Two-hase Sampling Using Two Auxiliary
More informationA GENERAL FAMILY OF ESTIMATORS FOR ESTIMATING POPULATION MEAN USING KNOWN VALUE OF SOME POPULATION PARAMETER(S)
A GENERAL FAMILY OF ESTIMATORS FOR ESTIMATING POPULATION MEAN USING KNOWN VALUE OF SOME POPULATION PARAMETER(S) Dr. M. Khoshnevisan GBS, Giffith Universit, Australia-(m.khoshnevisan@griffith.edu.au) Dr.
More informationA Generalized Class of Double Sampling Estimators Using Auxiliary Information on an Attribute and an Auxiliary Variable
International Journal of Computational Intellience Research ISSN 0973-873 Volume 3, Number (07), pp. 3-33 Research India Publications http://www.ripublication.com A Generalized Class of Double Samplin
More informationSEARCH OF GOOD ROTATION PATTERNS THROUGH EXPONENTIAL TYPE REGRESSION ESTIMATOR IN SUCCESSIVE SAMPLING OVER TWO OCCASIONS
italian journal of pure and applied mathematics n. 36 2016 (567 582) 567 SEARCH OF GOOD ROTATION PATTERNS THROUGH EXPONENTIAL TYPE REGRESSION ESTIMATOR IN SUCCESSIVE SAMPLING OVER TWO OCCASIONS Housila
More informationA GENERAL FAMILY OF ESTIMATORS FOR ESTIMATING POPULATION MEAN USING KNOWN VALUE OF SOME POPULATION PARAMETER(S)
A GENERAL FAMILY OF ESTIMATORS FOR ESTIMATING POPULATION MEAN USING KNOWN VALUE OF SOME POPULATION PARAMETER(S) Dr. M. Khoshnevisan, GBS, Griffith Universit, Australia (m.khoshnevisan@griffith.edu.au)
More informationASYMPTOTIC NORMALITY UNDER TWO-PHASE SAMPLING DESIGNS
Statistica Sinica 17(2007), 1047-1064 ASYMPTOTIC NORMALITY UNDER TWO-PHASE SAMPLING DESIGNS Jiahua Chen and J. N. K. Rao University of British Columbia and Carleton University Abstract: Large sample properties
More informationDual To Ratio Cum Product Estimator In Stratified Random Sampling
Dual To Ratio Cum Product Estimator In Stratified Random Sampling Rajesh Singh, Mukesh Kumar and Manoj K. Chaudhary Department of Statistics, B.H.U., Varanasi (U.P.),India Cem Kadilar Department of Statistics,
More informationSome Modified Unbiased Estimators of Population Mean
International Journal of Scientific and Research Publications, Volume 4, Issue 1, January 2014 1 Some Modified Unbiased Estimators of Population Mean *Shashi Bahl, **Bhawna Mehta * Professor, Deptt. of
More informationUse of Auxiliary Variables and Asymptotically Optimum Estimator to Increase Efficiency of Estimators in Double Sampling
Mathematics and Statistics: Open Access Received: Jan 19, 016, Accepted: Mar 31, 016, Published: Apr 04, 016 Math Stat, Volume, Issue http://crescopublications.org/pdf/msoa/msoa--010.pdf Article umber:
More informationResearch Article Some Improved Multivariate-Ratio-Type Estimators Using Geometric and Harmonic Means in Stratified Random Sampling
International Scholarly Research Network ISRN Probability and Statistics Volume 01, Article ID 509186, 7 pages doi:10.540/01/509186 Research Article Some Improved Multivariate-Ratio-Type Estimators Using
More informationModi ed Ratio Estimators in Strati ed Random Sampling
Original Modi ed Ratio Estimators in Strati ed Random Sampling Prayad Sangngam 1*, Sasiprapa Hiriote 2 Received: 18 February 2013 Accepted: 15 June 2013 Abstract This paper considers two modi ed ratio
More informationLINEAR COMBINATION OF ESTIMATORS IN PROBABILITY PROPORTIONAL TO SIZES SAMPLING TO ESTIMATE THE POPULATION MEAN AND ITS ROBUSTNESS TO OPTIMUM VALUE
STATISTICA, anno LXIX, n., 009 LINEAR COMBINATION OF ESTIMATORS IN PROBABILITY PROPORTIONAL TO SIZES SAMPLING TO ESTIMATE THE POPULATION MEAN AND ITS ROBUSTNESS TO OPTIMUM VALUE. INTRODUCTION To reduce
More informationSampling from Finite Populations Jill M. Montaquila and Graham Kalton Westat 1600 Research Blvd., Rockville, MD 20850, U.S.A.
Sampling from Finite Populations Jill M. Montaquila and Graham Kalton Westat 1600 Research Blvd., Rockville, MD 20850, U.S.A. Keywords: Survey sampling, finite populations, simple random sampling, systematic
More informationBootstrapping Heteroskedasticity Consistent Covariance Matrix Estimator
Bootstrapping Heteroskedasticity Consistent Covariance Matrix Estimator by Emmanuel Flachaire Eurequa, University Paris I Panthéon-Sorbonne December 2001 Abstract Recent results of Cribari-Neto and Zarkos
More informationSUCCESSIVE SAMPLING OF TWO OVERLAPPING FRAMES
SUCCESSIVE SAMPLING OF TWO OVERLAPPING FRAMES Chapman P. Gleason and Robert D, Tortora Statistical Research Division Economics, Statistics, and Cooperatives Service U.S. Department of Agriculture i. Introduction
More informationIMPROVED CLASSES OF ESTIMATORS FOR POPULATION MEAN IN PRESENCE OF NON-RESPONSE
Pak. J. Statist. 014 Vol. 30(1), 83-100 IMPROVED CLASSES OF ESTIMATORS FOR POPULATION MEAN IN PRESENCE OF NON-RESPONSE Saba Riaz 1, Giancarlo Diana and Javid Shabbir 1 1 Department of Statistics, Quaid-i-Azam
More informationApplication of Parametric Homogeneity of Variances Tests under Violation of Classical Assumption
Application of Parametric Homogeneity of Variances Tests under Violation of Classical Assumption Alisa A. Gorbunova and Boris Yu. Lemeshko Novosibirsk State Technical University Department of Applied Mathematics,
More informationFractional Hot Deck Imputation for Robust Inference Under Item Nonresponse in Survey Sampling
Fractional Hot Deck Imputation for Robust Inference Under Item Nonresponse in Survey Sampling Jae-Kwang Kim 1 Iowa State University June 26, 2013 1 Joint work with Shu Yang Introduction 1 Introduction
More informationAn Alternate approach to Multi-Stage Sampling: UV Cubical Circular Systematic Sampling Method
International Journal of Statistics and Applications 0, (: -0 DOI: 0./j.statistics.000.0 An Alternate approach to Multi-Stage Sampling: N. Uthayakumaran *, S. Venkatasubramanian National Institute of Epidemiology,
More informationAn improved class of estimators for nite population variance
Hacettepe Journal of Mathematics Statistics Volume 45 5 016 1641 1660 An improved class of estimators for nite population variance Mazhar Yaqub Javid Shabbir Abstract We propose an improved class of estimators
More informationEstimation of the Parameters of Bivariate Normal Distribution with Equal Coefficient of Variation Using Concomitants of Order Statistics
International Journal of Statistics and Probability; Vol., No. 3; 013 ISSN 197-703 E-ISSN 197-7040 Published by Canadian Center of Science and Education Estimation of the Parameters of Bivariate Normal
More informationSampling Theory. A New Ratio Estimator in Stratified Random Sampling
Communications in Statistics Theory and Methods, 34: 597 602, 2005 Copyright Taylor & Francis, Inc. ISSN: 0361-0926 print/1532-415x online DOI: 10.1081/STA-200052156 Sampling Theory A New Ratio Estimator
More information(3) (S) THE BIAS AND STABILITY OF JACK -KNIFE VARIANCE ESTIMATOR IN RATIO ESTIMATION
THE BIAS AND STABILITY OF JACK -KNIFE VARIANCE ESTIMATOR IN RATIO ESTIMATION R.P. Chakrabarty and J.N.K. Rao University of Georgia and Texas A M University Summary The Jack -Knife variance estimator v(r)
More informationESTIMATION OF FINITE POPULATION MEAN USING KNOWN CORRELATION COEFFICIENT BETWEEN AUXILIARY CHARACTERS
STATISTICA, anno LXV, n. 4, 005 ESTIMATION OF FINITE POPULATION MEAN USING KNOWN CORRELATION COEFFICIENT BETWEEN AUXILIAR CHARACTERS. INTRODUCTION Let U { U, U,..., U N } be a finite population of N units.
More informationof being selected and varying such probability across strata under optimal allocation leads to increased accuracy.
5 Sampling with Unequal Probabilities Simple random sampling and systematic sampling are schemes where every unit in the population has the same chance of being selected We will now consider unequal probability
More informationVARIANCE ESTIMATION FOR COMBINED RATIO ESTIMATOR
Sankyā : Te Indian Journal of Statistics 1995, Volume 57, Series B, Pt. 1, pp. 85-92 VARIANCE ESTIMATION FOR COMBINED RATIO ESTIMATOR By SANJAY KUMAR SAXENA Central Soil and Water Conservation Researc
More informationHISTORICAL PERSPECTIVE OF SURVEY SAMPLING
HISTORICAL PERSPECTIVE OF SURVEY SAMPLING A.K. Srivastava Former Joint Director, I.A.S.R.I., New Delhi -110012 1. Introduction The purpose of this article is to provide an overview of developments in sampling
More informationISSN X A Modified Procedure for Estimating the Population Mean in Two-occasion Successive Samplings
Afrika Statistika Vol. 1 3, 017, pages 1347 1365. DOI: http://dx.doi.org/10.1699/as/017.1347.108 Afrika Statistika ISSN 316-090X A Modified Procedure for Estimating the Population Mean in Two-occasion
More informationVARIANCE ESTIMATION IN PRESENCE OF RANDOM NON-RESPONSE
Journal of Reliability and Statistical Studies; ISSN (Print): 0974-8024, (Online):2229-5666 Vol. 7, Issue 2 (2014): 65-70 VARIANCE ESTIMATION IN PRESENCE OF RANDOM NON-RESPONSE Sunil Kumar Alliance School
More informationSome Practical Aspects in Multi-Phase Sampling
Working Paper Series, N. 2, February 2005 Some Practical Aspects in Multi-Phase Sampling Giancarlo Diana, Paolo Preo Department of Statistical Sciences University of Padua Italy Chiara Tommasi Department
More informationCombining data from two independent surveys: model-assisted approach
Combining data from two independent surveys: model-assisted approach Jae Kwang Kim 1 Iowa State University January 20, 2012 1 Joint work with J.N.K. Rao, Carleton University Reference Kim, J.K. and Rao,
More informationA Monte-Carlo study of asymptotically robust tests for correlation coefficients
Biometrika (1973), 6, 3, p. 661 551 Printed in Great Britain A Monte-Carlo study of asymptotically robust tests for correlation coefficients BY G. T. DUNCAN AND M. W. J. LAYAKD University of California,
More informationREMAINDER LINEAR SYSTEMATIC SAMPLING
Sankhyā : The Indian Journal of Statistics 2000, Volume 62, Series B, Pt. 2, pp. 249 256 REMAINDER LINEAR SYSTEMATIC SAMPLING By HORNG-JINH CHANG and KUO-CHUNG HUANG Tamkang University, Taipei SUMMARY.
More informationResearch Article Ratio Type Exponential Estimator for the Estimation of Finite Population Variance under Two-stage Sampling
Research Journal of Applied Sciences, Engineering and Technology 7(19): 4095-4099, 2014 DOI:10.19026/rjaset.7.772 ISSN: 2040-7459; e-issn: 2040-7467 2014 Maxwell Scientific Publication Corp. Submitted:
More informationEstimators for the binomial distribution that dominate the MLE in terms of Kullback Leibler risk
Ann Inst Stat Math (0) 64:359 37 DOI 0.007/s0463-00-036-3 Estimators for the binomial distribution that dominate the MLE in terms of Kullback Leibler risk Paul Vos Qiang Wu Received: 3 June 009 / Revised:
More informationAdaptive two-stage sequential double sampling
Adaptive two-stage sequential double sampling Bardia Panahbehagh Afshin Parvardeh Babak Mohammadi March 4, 208 arxiv:803.04484v [math.st] 2 Mar 208 Abstract In many surveys inexpensive auxiliary variables
More informationAll variances and covariances appearing in this formula are understood to be defined in the usual manner for finite populations; example
155 By: UBIASED COMPOET RATIO ESTIMATIO1 D. S. Robson and Chitra Vitbayasai, Cornell University ITRODUCTIO The precision of a ratio -type estimator such as can sometimes be substantially increased the
More informationBEST LINEAR UNBIASED ESTIMATORS OF POPULATION MEAN ON CURRENT OCCASION IN TWO-OCCASION ROTATION PATTERNS
STATISTICS IN TRANSITIONnew series, Spring 57 STATISTICS IN TRANSITIONnew series, Spring Vol., No., pp. 57 7 BST LINAR UNBIASD STIMATORS OF POPULATION MAN ON CURRNT OCCASION IN TWOOCCASION ROTATION PATTRNS
More informationCOMSATS Institute of Information and Technology, Lahore Pakistan 2
Pak. J. Stati. 015 Vol. 31(1), 71-94 GENERAIZED EXPONENTIA-TPE RATIO-CUM-RATIO AND PRODUCT-CUM-PRODUCT ESTIMATORS FOR POPUATION MEAN IN THE PRESENCE OF NON-RESPONSE UNDER STRATIFIED TWO-PHASE RANDOM SAMPING
More informationMA 575 Linear Models: Cedric E. Ginestet, Boston University Non-parametric Inference, Polynomial Regression Week 9, Lecture 2
MA 575 Linear Models: Cedric E. Ginestet, Boston University Non-parametric Inference, Polynomial Regression Week 9, Lecture 2 1 Bootstrapped Bias and CIs Given a multiple regression model with mean and
More informationResearch Article Efficient Estimators Using Auxiliary Variable under Second Order Approximation in Simple Random Sampling and Two-Phase Sampling
Advances in Statistics, Article ID 974604, 9 pages http://dx.doi.org/0.55/204/974604 Research Article Efficient Estimators Using Auxiliary Variable under Second Order Approximation in Simple Random Sampling
More informationAn Improved Generalized Estimation Procedure of Current Population Mean in Two-Occasion Successive Sampling
Journal of Modern Alied Statistical Methods Volume 15 Issue Article 14 11-1-016 An Imroved Generalized Estimation Procedure of Current Poulation Mean in Two-Occasion Successive Samling G. N. Singh Indian
More informationSaadia Masood, Javid Shabbir. Abstract
Hacettepe Journal of Mathematics and Statistics Volume, 1 Generalized Multi-Phase Regression-type Estimators Under the Effect of Measuemnent Error to Estimate the Population Variance Saadia Masood, Javid
More informationDesign and Estimation for Split Questionnaire Surveys
University of Wollongong Research Online Centre for Statistical & Survey Methodology Working Paper Series Faculty of Engineering and Information Sciences 2008 Design and Estimation for Split Questionnaire
More informationSIMULTANEOUS CONFIDENCE INTERVALS AMONG k MEAN VECTORS IN REPEATED MEASURES WITH MISSING DATA
SIMULTANEOUS CONFIDENCE INTERVALS AMONG k MEAN VECTORS IN REPEATED MEASURES WITH MISSING DATA Kazuyuki Koizumi Department of Mathematics, Graduate School of Science Tokyo University of Science 1-3, Kagurazaka,
More informationCOS513: FOUNDATIONS OF PROBABILISTIC MODELS LECTURE 9: LINEAR REGRESSION
COS513: FOUNDATIONS OF PROBABILISTIC MODELS LECTURE 9: LINEAR REGRESSION SEAN GERRISH AND CHONG WANG 1. WAYS OF ORGANIZING MODELS In probabilistic modeling, there are several ways of organizing models:
More informationThe New sampling Procedure for Unequal Probability Sampling of Sample Size 2.
. The New sampling Procedure for Unequal Probability Sampling of Sample Size. Introduction :- It is a well known fact that in simple random sampling, the probability selecting the unit at any given draw
More informationMulticollinearity and A Ridge Parameter Estimation Approach
Journal of Modern Applied Statistical Methods Volume 15 Issue Article 5 11-1-016 Multicollinearity and A Ridge Parameter Estimation Approach Ghadban Khalaf King Khalid University, albadran50@yahoo.com
More informationImproved ratio-type estimators using maximum and minimum values under simple random sampling scheme
Improved ratio-type estimators using maximum and minimum values under simple random sampling scheme Mursala Khan Saif Ullah Abdullah. Al-Hossain and Neelam Bashir Abstract This paper presents a class of
More informationCombining Biased and Unbiased Estimators in High Dimensions. (joint work with Ed Green, Rutgers University)
Combining Biased and Unbiased Estimators in High Dimensions Bill Strawderman Rutgers University (joint work with Ed Green, Rutgers University) OUTLINE: I. Introduction II. Some remarks on Shrinkage Estimators
More informationResearch Article A Class of Estimators for Finite Population Mean in Double Sampling under Nonresponse Using Fractional Raw Moments
Applied Mathematics, Article ID 282065, 11 pages http://dx.doi.org/10.1155/2014/282065 Research Article A Class of Estimators for Finite Population Mean in Double Sampling under Nonresponse Using Fractional
More informationEfficiency of NNBD and NNBIBD using autoregressive model
2018; 3(3): 133-138 ISSN: 2456-1452 Maths 2018; 3(3): 133-138 2018 Stats & Maths www.mathsjournal.com Received: 17-03-2018 Accepted: 18-04-2018 S Saalini Department of Statistics, Loyola College, Chennai,
More informationAN EFFICIENT CLASS OF CHAIN ESTIMATORS OF POPULATION VARIANCE UNDER SUB-SAMPLING SCHEME
J. Japan Statist. Soc. Vol. 35 No. 005 73 86 AN EFFICIENT CLASS OF CHAIN ESTIMATORS OF POPULATION VARIANCE UNDER SUB-SAMPLING SCHEME H. S. Jhajj*, M. K. Shara* and Lovleen Kuar Grover** For estiating the
More informationHigh-dimensional asymptotic expansions for the distributions of canonical correlations
Journal of Multivariate Analysis 100 2009) 231 242 Contents lists available at ScienceDirect Journal of Multivariate Analysis journal homepage: www.elsevier.com/locate/jmva High-dimensional asymptotic
More informationA better way to bootstrap pairs
A better way to bootstrap pairs Emmanuel Flachaire GREQAM - Université de la Méditerranée CORE - Université Catholique de Louvain April 999 Abstract In this paper we are interested in heteroskedastic regression
More informationA Practical Guide for Creating Monte Carlo Simulation Studies Using R
International Journal of Mathematics and Computational Science Vol. 4, No. 1, 2018, pp. 18-33 http://www.aiscience.org/journal/ijmcs ISSN: 2381-7011 (Print); ISSN: 2381-702X (Online) A Practical Guide
More informationAlmost unbiased estimation procedures of population mean in two-occasion successive sampling
Hacettepe Journal of Mathematics Statistics Volume 47 (5) (018), 168 180 Almost unbiased estimation procedures of population mean in two-occasion successive sampling G. N. Singh, A. K. Singh Cem Kadilar
More informationREPLICATION VARIANCE ESTIMATION FOR TWO-PHASE SAMPLES
Statistica Sinica 8(1998), 1153-1164 REPLICATION VARIANCE ESTIMATION FOR TWO-PHASE SAMPLES Wayne A. Fuller Iowa State University Abstract: The estimation of the variance of the regression estimator for
More informationImpact of serial correlation structures on random effect misspecification with the linear mixed model.
Impact of serial correlation structures on random effect misspecification with the linear mixed model. Brandon LeBeau University of Iowa file:///c:/users/bleb/onedrive%20 %20University%20of%20Iowa%201/JournalArticlesInProgress/Diss/Study2/Pres/pres.html#(2)
More informationResearch Article Estimation of Population Mean in Chain Ratio-Type Estimator under Systematic Sampling
Probability and Statistics Volume 2015 Article ID 2837 5 pages http://dx.doi.org/10.1155/2015/2837 Research Article Estimation of Population Mean in Chain Ratio-Type Estimator under Systematic Sampling
More informationA bias improved estimator of the concordance correlation coefficient
The 22 nd Annual Meeting in Mathematics (AMM 217) Department of Mathematics, Faculty of Science Chiang Mai University, Chiang Mai, Thailand A bias improved estimator of the concordance correlation coefficient
More informationOptimal Calibration Estimators Under Two-Phase Sampling
Journal of Of cial Statistics, Vol. 19, No. 2, 2003, pp. 119±131 Optimal Calibration Estimators Under Two-Phase Sampling Changbao Wu 1 and Ying Luan 2 Optimal calibration estimators require in general
More informationThe Use of Survey Weights in Regression Modelling
The Use of Survey Weights in Regression Modelling Chris Skinner London School of Economics and Political Science (with Jae-Kwang Kim, Iowa State University) Colorado State University, June 2013 1 Weighting
More informationSimple Linear Regression Analysis
LINEAR REGRESSION ANALYSIS MODULE II Lecture - 6 Simple Linear Regression Analysis Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur Prediction of values of study
More informationMantel-Haenszel Test Statistics. for Correlated Binary Data. Department of Statistics, North Carolina State University. Raleigh, NC
Mantel-Haenszel Test Statistics for Correlated Binary Data by Jie Zhang and Dennis D. Boos Department of Statistics, North Carolina State University Raleigh, NC 27695-8203 tel: (919) 515-1918 fax: (919)
More informationTesting Some Covariance Structures under a Growth Curve Model in High Dimension
Department of Mathematics Testing Some Covariance Structures under a Growth Curve Model in High Dimension Muni S. Srivastava and Martin Singull LiTH-MAT-R--2015/03--SE Department of Mathematics Linköping
More informationA General Class of Estimators of Population Median Using Two Auxiliary Variables in Double Sampling
ohammad Khoshnevisan School o Accountin and inance riith University Australia Housila P. Sinh School o Studies in Statistics ikram University Ujjain - 56. P. India Sarjinder Sinh Departament o athematics
More informationAn Unbiased Estimator For Population Mean Using An Attribute And An Auxiliary Variable
Advance in Dynamical Sytem and Application. ISS 0973-53, Volume, umber, (07 pp. 9-39 Reearch India Publication http://www.ripublication.com An Unbiaed Etimator For Population Mean Uing An Attribute And
More informationCluster Sampling 2. Chapter Introduction
Chapter 7 Cluster Sampling 7.1 Introduction In this chapter, we consider two-stage cluster sampling where the sample clusters are selected in the first stage and the sample elements are selected in the
More informationSampling Theory. Improvement in Variance Estimation in Simple Random Sampling
Communications in Statistics Theory and Methods, 36: 075 081, 007 Copyright Taylor & Francis Group, LLC ISS: 0361-096 print/153-415x online DOI: 10.1080/0361090601144046 Sampling Theory Improvement in
More informationOn the Use of Compromised Imputation for Missing data using Factor-Type Estimators
J. Stat. Appl. Pro. Lett. 2, No. 2, 105-113 (2015) 105 Journal of Statistics Applications & Probability Letters An International Journal http://dx.doi.org/10.12785/jsapl/020202 On the Use of Compromised
More informationEstimation Tasks. Short Course on Image Quality. Matthew A. Kupinski. Introduction
Estimation Tasks Short Course on Image Quality Matthew A. Kupinski Introduction Section 13.3 in B&M Keep in mind the similarities between estimation and classification Image-quality is a statistical concept
More informationINSTRUMENTAL-VARIABLE CALIBRATION ESTIMATION IN SURVEY SAMPLING
Statistica Sinica 24 (2014), 1001-1015 doi:http://dx.doi.org/10.5705/ss.2013.038 INSTRUMENTAL-VARIABLE CALIBRATION ESTIMATION IN SURVEY SAMPLING Seunghwan Park and Jae Kwang Kim Seoul National Univeristy
More informationBayesian inference for sample surveys. Roderick Little Module 2: Bayesian models for simple random samples
Bayesian inference for sample surveys Roderick Little Module : Bayesian models for simple random samples Superpopulation Modeling: Estimating parameters Various principles: least squares, method of moments,
More informationPARAMETER ESTIMATION FOR FRACTIONAL BROWNIAN SURFACES
Statistica Sinica 2(2002), 863-883 PARAMETER ESTIMATION FOR FRACTIONAL BROWNIAN SURFACES Zhengyuan Zhu and Michael L. Stein University of Chicago Abstract: We study the use of increments to estimate the
More informationIntroduction to Sample Survey
Introduction to Sample Survey Girish Kumar Jha gjha_eco@iari.res.in Indian Agricultural Research Institute, New Delhi-12 INTRODUCTION Statistics is defined as a science which deals with collection, compilation,
More informationREPLICATION VARIANCE ESTIMATION FOR THE NATIONAL RESOURCES INVENTORY
REPLICATION VARIANCE ESTIMATION FOR THE NATIONAL RESOURCES INVENTORY J.D. Opsomer, W.A. Fuller and X. Li Iowa State University, Ames, IA 50011, USA 1. Introduction Replication methods are often used in
More informationSHOTA KATAYAMA AND YUTAKA KANO. Graduate School of Engineering Science, Osaka University, 1-3 Machikaneyama, Toyonaka, Osaka , Japan
A New Test on High-Dimensional Mean Vector Without Any Assumption on Population Covariance Matrix SHOTA KATAYAMA AND YUTAKA KANO Graduate School of Engineering Science, Osaka University, 1-3 Machikaneyama,
More information