Downloaded from jhs.mazums.ac.ir at 9: on Monday September 17th 2018 [ DOI: /acadpub.jhs ]
|
|
- Claud Jenkins
- 5 years ago
- Views:
Transcription
1 Iranian journal of health sciences 013; 1(): htt://jhs.mazums.ac.ir Original Article Comaring Two Formulas of Samle Size Determination for Prevalence Studies Hamed Tabesh 1 *Azadeh Saki Fatemeh Pourmotahari 3 1-Assistant Professor of Biostatistics, Deartment of Biostatistics and Eidemiology, School of Health, Ahvaz Jundishaur University of Medical Sciences, Ahvaz, Iran -Assistant Professor of Biostatistics, Deartment of Biostatistics and Eidemiology, School of Health, Ahvaz Jundishaur University of Medical Sciences, Ahvaz, Iran 3- MSc Student of Biostatistics, Deartment of Biostatistics and Eidemiology, School of Health, Ahvaz Jundishaur University of Medical Sciences,Ahvaz,Iran Abstract * azadehsaki@yahoo.com Background and urose: Samle size and its determination is one of the most imortant roblems in health researches. Calculating samle size for revalence studies is one of the common questions of samle size toics. Minimum samle size with least comlexity is desirable in order to achieve the basic goal of these studies. This study aims to comare two formulas of samle size calculation for revalence researches and finally, to use the simlest formula to get the most aroriate samle size. Materials and Methods: Samle size for roortions: 0.9, 0.95, 0.99, candidates of close to 1 roortions; 10-5, 10-4, 10-3, 10 -, 0.05, 0.1 candidates of close to 0, and roortions 0.3, 0.4, 0.5, 0.6, 0.7 candidates of close to 0.5 were calculated. For comaring n 1, n ; φ = n 1 n, it was comuted by R ackage (.10.1). Results: Comuted samle size by (f ) is lightly greater than samle size comuted by (f 1 ) and maximum value of φ index for comaring the two formulas equals 1. Conclusion: Results show that the calculated samle size by (f 1 ) is similar to what was obtained by (f ), though, according to its interretation and easy comutation,it is suggested for all values of. [Tabesh H. * Saki A. Pourmotahari F. Comaring two Formulas of Samle Size Determination for revalence studies. IJHS 013; 1():56-60] htt://jhs.mazums.ac.ir ey words: Samle Size Calculation, Prevalence Study, Calculation Procedure. 56
2 1. Introduction If observational and exerimental studies are designed effectively, valuable results would be obtained. Good lanning has many asects such as exact definition of roblem and method and enough samle size due to the goals. Enough samle size for a research is determined based on the tye and object of a study, statistical methods for analyzing and interreting, available data, validity and reliability for the generalized results by two general techniques: confidence interval and Bayesian methods (1). In simle term, samle size estimation means to estimate the minimum number of the samle for a study by using statistical methods based on secific situation, basic information and recision requirement, and under the remise of guaranteeing the reliability of the conclusion.() Calculated samle size should be large enough so that an effort of such magnitude to be of scientific significance & also be statistically significant. It is just as imortant, however, that the study not to be too large, where an effect of little scientific imortance is nevertheless statistically detectable (3). Although, for such an imortant issue, there is not a large amount of ublished literature, there are several aroaches for samle size. For examle, one can secify the desired width of a confidence interval and determine the samle size that achieves that goal or Bayesian aroach can be used where we otimize some utility function. For conducting samle size, statistical inference is based on estimating a arameter or testing a hyothesis which demand one tye of the study. When estimating an infinite oulation arameter such as revalence, incidence and chance of illness recurrence is desirable. Several text books (4-7) recommended using (f 1 ) and (f ). n 1 = Zα (1 ) (f d 1 ) n = Z α sin 1 d ( ) (1 ) (f ) Where n 1, n are the estimated minimum samle size d and d are the desired level of recision and estimated roortion of an attribute resent in the oulation. It is clear that z is the abscissa of the normal curve that cuts off an area α at the fails. (1- α equals the desired confidence level). Simlicity is one of the greatest roerties of each statistical aroach. Denominator of (f 1 ) equals a half width of confidence interval which shows the estimation recision. So it is easy to understand and interret. Also calculating samle size by (f 1 ) is simle and does not have any comlexity. But unlike the denominator of (f ), to the best of our knowledge, there is not any interretation and comuting samle size by (f ) which is not as convenient as comuting by (f 1 ). some literatures recommended using (f 1 ) when is close to 0.5 and (f ) when is close to 0 or 1(6,8-9). In this study, comaring results of using (f 1 ) and (f ) in the same situation was desirable so if ossible, an alternative formula for (f ) could be suggested. IJHS 013; 1(): 57
3 . Materials and Methods As mentioned before, several literatures recommended using (f ) when crude estimation of revalence, incidence, and success roortion or illness recurrence robability is close to 0 or 1. In this study, to comare the oututs of (f 1 ) and (f ) samle size for roortions: 0.9, 0.95, 0.99, as candidates of close to 1 and, 10-5, 10-4, 10-3, 10 -, 0.05, 0.1 as candidates of close to 0 and 0.3, 0.4, 0.5, 0.6, 0.7 as candidates of close to 0.5 considered. Since d < *min {, 1- } where < ¼. So k equal to 10%, 15%, 0%, and 5% have been considered. Whereas softwares for samle size determination such as PASS, UnifyPow and Power and Precision determine samle size based on (f 1 ) so for comaring (f ),ackage R (.10.1) was used and φ index (φ = n 1 n ) was comuted (10-1). As the most common confidence intervals in medical research cases are 95% and 99%, samle size was comuted for these confidence intervals. 3. Results Samle size for different estimated roortions in oulation,, and distinct values of k, was comuted by (f 1 ) and (f ). N 1, n are estimated minimum samle size by (f 1 ) and (f ) resectively. For comaring n 1, n, index φ = n 1 n was calculated. Comuted φfor close to 0, 1 and 0.5 with 0.95 confidence limit are shown in tables (1), () and (3) resectively. The findings of this study show that n 1 and n were very close to each other when estimated roortion of an attribute resent in the oulation was very small. Table1. Calculated φ index by (f 1 ) and (f ) for very small values of when confidence limit is 95% % % % % Table. Calculated φ index by (f 1 ) and (f ) for very large values of when confidence limit is 95% % % % % IJHS 013; 1(): 58
4 Table3. Calculated φ index by (f 1 ) and (f ) for mid- values of when confidence limit is 95% When was close to 1, φ index was aroximately equal to 1 which means that n 1 and n were similar. For mid-values of, similar conclusion could be drawn. The results revealed that both formulas, (f 1 ) and (f ), would erform similarly when the estimated roortion of an attribute resent in the oulation had very small, medium and very large values. The comutations were done for 99% confidence limit, and the results were closely similar. 4. Discussion In medical research, it is imortant to determine size sufficiently enough to ensure reliable conclusions. On the other hand, revalence studies are interesting research cases in medical sciences and consequently, adequate samle size for these research cases would be interesting, too. The most common formulas for revalence or incidence studies are (f1) and (f). (5-7). (f1) has simle structure made u of (f) so it has more ublic % % % % interest than (f). But some literatures limited using (f1). They believe that (f1) could be useful when is not to be close to 1 or 0. This study showed that the estimated samle size by (f1) is aroximately similar to the estimated samle size by (f). On the other hand, comuting samle size by (f1) is easy. Therefore, using (f1) is recommended for estimating samle size of revalence or incidence studies for infinite oulation References 1. Lenth RV. Some ractical Guidelines Effective samle size Determination. The American Statistical, 001; 55(3): HU LP, Bao XL, Zhou SG, Guas X, Estimation of samle size and testing ower (art1). J chin Integr Med. 011;9(10): Sathian B, sreedharan y, Baboo NS, Sharan k, Abhilash ES, Rajesh E. Relevance of samle size Determination in Medical Researche., Neal Journal of Eidemiology, 010;1(1): IJHS 013; 1(): 59
5 4. Daniel WW. Biostatistics: A Foundation for Analysis in the Health Sciences.7th edition. NewYork: John Wiley & Sons. 5. Lwanga sk and Lemeshow S. Samle size determination in Health Studies: A ractical Manual. Genevai World Health Organization Cochran WG. Samling Techniques, 3 rd edition. NewYork: John Wiley & Sons Naing L,Winn T and Rusli BN. Samle size calculator for revalence studies. Archives of Orofacial Sciences, 006;1: reamer, H.C, and Thiemann, S. How many subjects & statistical ower Analysis in Research, Newbury ark, CA: Sage Publications Desu MM, Raghavao D. samle size methodology. Boston, MA: Academic ress, Inc Hintze, J. PASS 000, aysville, UT: Number Cruncher Statistical System, Software for MS-DOS systems O Brien R.G. Unifyow.sas: version , Deartment of Biostatistics and Eidemiology, Cleveland Clinic Foundation, Cleveland, OH Borenstein, M., Rothstein, H., and Cohen, J. Power and Precision, Biostat, Teaneck, NY: software for MS-DOS systems IJHS 013; 1(): 60
4. Score normalization technical details We now discuss the technical details of the score normalization method.
SMT SCORING SYSTEM This document describes the scoring system for the Stanford Math Tournament We begin by giving an overview of the changes to scoring and a non-technical descrition of the scoring rules
More informationIntroduction to Probability and Statistics
Introduction to Probability and Statistics Chater 8 Ammar M. Sarhan, asarhan@mathstat.dal.ca Deartment of Mathematics and Statistics, Dalhousie University Fall Semester 28 Chater 8 Tests of Hyotheses Based
More informationTests for Two Proportions in a Stratified Design (Cochran/Mantel-Haenszel Test)
Chater 225 Tests for Two Proortions in a Stratified Design (Cochran/Mantel-Haenszel Test) Introduction In a stratified design, the subects are selected from two or more strata which are formed from imortant
More informationMODELING THE RELIABILITY OF C4ISR SYSTEMS HARDWARE/SOFTWARE COMPONENTS USING AN IMPROVED MARKOV MODEL
Technical Sciences and Alied Mathematics MODELING THE RELIABILITY OF CISR SYSTEMS HARDWARE/SOFTWARE COMPONENTS USING AN IMPROVED MARKOV MODEL Cezar VASILESCU Regional Deartment of Defense Resources Management
More information7.2 Inference for comparing means of two populations where the samples are independent
Objectives 7.2 Inference for comaring means of two oulations where the samles are indeendent Two-samle t significance test (we give three examles) Two-samle t confidence interval htt://onlinestatbook.com/2/tests_of_means/difference_means.ht
More informationOn split sample and randomized confidence intervals for binomial proportions
On slit samle and randomized confidence intervals for binomial roortions Måns Thulin Deartment of Mathematics, Usala University arxiv:1402.6536v1 [stat.me] 26 Feb 2014 Abstract Slit samle methods have
More information¼ ¼ 6:0. sum of all sample means in ð8þ 25
1. Samling Distribution of means. A oulation consists of the five numbers 2, 3, 6, 8, and 11. Consider all ossible samles of size 2 that can be drawn with relacement from this oulation. Find the mean of
More informationRadial Basis Function Networks: Algorithms
Radial Basis Function Networks: Algorithms Introduction to Neural Networks : Lecture 13 John A. Bullinaria, 2004 1. The RBF Maing 2. The RBF Network Architecture 3. Comutational Power of RBF Networks 4.
More informationHotelling s Two- Sample T 2
Chater 600 Hotelling s Two- Samle T Introduction This module calculates ower for the Hotelling s two-grou, T-squared (T) test statistic. Hotelling s T is an extension of the univariate two-samle t-test
More informationCHAPTER 5 STATISTICAL INFERENCE. 1.0 Hypothesis Testing. 2.0 Decision Errors. 3.0 How a Hypothesis is Tested. 4.0 Test for Goodness of Fit
Chater 5 Statistical Inference 69 CHAPTER 5 STATISTICAL INFERENCE.0 Hyothesis Testing.0 Decision Errors 3.0 How a Hyothesis is Tested 4.0 Test for Goodness of Fit 5.0 Inferences about Two Means It ain't
More informationEcon 3790: Business and Economics Statistics. Instructor: Yogesh Uppal
Econ 379: Business and Economics Statistics Instructor: Yogesh Ual Email: yual@ysu.edu Chater 9, Part A: Hyothesis Tests Develoing Null and Alternative Hyotheses Tye I and Tye II Errors Poulation Mean:
More informationA Comparison between Biased and Unbiased Estimators in Ordinary Least Squares Regression
Journal of Modern Alied Statistical Methods Volume Issue Article 7 --03 A Comarison between Biased and Unbiased Estimators in Ordinary Least Squares Regression Ghadban Khalaf King Khalid University, Saudi
More informationEstimation of the large covariance matrix with two-step monotone missing data
Estimation of the large covariance matrix with two-ste monotone missing data Masashi Hyodo, Nobumichi Shutoh 2, Takashi Seo, and Tatjana Pavlenko 3 Deartment of Mathematical Information Science, Tokyo
More informationHypothesis Test-Confidence Interval connection
Hyothesis Test-Confidence Interval connection Hyothesis tests for mean Tell whether observed data are consistent with μ = μ. More secifically An hyothesis test with significance level α will reject the
More informationThe Binomial Approach for Probability of Detection
Vol. No. (Mar 5) - The e-journal of Nondestructive Testing - ISSN 45-494 www.ndt.net/?id=7498 The Binomial Aroach for of Detection Carlos Correia Gruo Endalloy C.A. - Caracas - Venezuela www.endalloy.net
More informationGeneral Linear Model Introduction, Classes of Linear models and Estimation
Stat 740 General Linear Model Introduction, Classes of Linear models and Estimation An aim of scientific enquiry: To describe or to discover relationshis among events (variables) in the controlled (laboratory)
More informationMULTIVARIATE SHEWHART QUALITY CONTROL FOR STANDARD DEVIATION
MULTIVARIATE SHEWHART QUALITY CONTROL FOR STANDARD DEVIATION M. Jabbari Nooghabi, Deartment of Statistics, Faculty of Mathematical Sciences, Ferdowsi University of Mashhad, Mashhad-Iran. and H. Jabbari
More informationRatio Estimators in Simple Random Sampling Using Information on Auxiliary Attribute
ajesh Singh, ankaj Chauhan, Nirmala Sawan School of Statistics, DAVV, Indore (M.., India Florentin Smarandache Universit of New Mexico, USA atio Estimators in Simle andom Samling Using Information on Auxiliar
More informationA Bound on the Error of Cross Validation Using the Approximation and Estimation Rates, with Consequences for the Training-Test Split
A Bound on the Error of Cross Validation Using the Aroximation and Estimation Rates, with Consequences for the Training-Test Slit Michael Kearns AT&T Bell Laboratories Murray Hill, NJ 7974 mkearns@research.att.com
More informationSAS for Bayesian Mediation Analysis
Paer 1569-2014 SAS for Bayesian Mediation Analysis Miočević Milica, Arizona State University; David P. MacKinnon, Arizona State University ABSTRACT Recent statistical mediation analysis research focuses
More informationSTK4900/ Lecture 7. Program
STK4900/9900 - Lecture 7 Program 1. Logistic regression with one redictor 2. Maximum likelihood estimation 3. Logistic regression with several redictors 4. Deviance and likelihood ratio tests 5. A comment
More informationLower Confidence Bound for Process-Yield Index S pk with Autocorrelated Process Data
Quality Technology & Quantitative Management Vol. 1, No.,. 51-65, 15 QTQM IAQM 15 Lower onfidence Bound for Process-Yield Index with Autocorrelated Process Data Fu-Kwun Wang * and Yeneneh Tamirat Deartment
More informationEcon 3790: Business and Economics Statistics. Instructor: Yogesh Uppal
Econ 379: Business and Economics Statistics Instructor: Yogesh Ual Email: yual@ysu.edu Chater 9, Part A: Hyothesis Tests Develoing Null and Alternative Hyotheses Tye I and Tye II Errors Poulation Mean:
More informationRobustness of multiple comparisons against variance heterogeneity Dijkstra, J.B.
Robustness of multile comarisons against variance heterogeneity Dijkstra, J.B. Published: 01/01/1983 Document Version Publisher s PDF, also known as Version of Record (includes final age, issue and volume
More informationAn Analysis of Reliable Classifiers through ROC Isometrics
An Analysis of Reliable Classifiers through ROC Isometrics Stijn Vanderlooy s.vanderlooy@cs.unimaas.nl Ida G. Srinkhuizen-Kuyer kuyer@cs.unimaas.nl Evgueni N. Smirnov smirnov@cs.unimaas.nl MICC-IKAT, Universiteit
More informationCombining Logistic Regression with Kriging for Mapping the Risk of Occurrence of Unexploded Ordnance (UXO)
Combining Logistic Regression with Kriging for Maing the Risk of Occurrence of Unexloded Ordnance (UXO) H. Saito (), P. Goovaerts (), S. A. McKenna (2) Environmental and Water Resources Engineering, Deartment
More informationSTA 250: Statistics. Notes 7. Bayesian Approach to Statistics. Book chapters: 7.2
STA 25: Statistics Notes 7. Bayesian Aroach to Statistics Book chaters: 7.2 1 From calibrating a rocedure to quantifying uncertainty We saw that the central idea of classical testing is to rovide a rigorous
More informationarxiv:cond-mat/ v2 25 Sep 2002
Energy fluctuations at the multicritical oint in two-dimensional sin glasses arxiv:cond-mat/0207694 v2 25 Se 2002 1. Introduction Hidetoshi Nishimori, Cyril Falvo and Yukiyasu Ozeki Deartment of Physics,
More informationFinite Mixture EFA in Mplus
Finite Mixture EFA in Mlus November 16, 2007 In this document we describe the Mixture EFA model estimated in Mlus. Four tyes of deendent variables are ossible in this model: normally distributed, ordered
More informationObjectives. 6.1, 7.1 Estimating with confidence (CIS: Chapter 10) CI)
Objectives 6.1, 7.1 Estimating with confidence (CIS: Chater 10) Statistical confidence (CIS gives a good exlanation of a 95% CI) Confidence intervals. Further reading htt://onlinestatbook.com/2/estimation/confidence.html
More informationChapter 7 Sampling and Sampling Distributions. Introduction. Selecting a Sample. Introduction. Sampling from a Finite Population
Chater 7 and s Selecting a Samle Point Estimation Introduction to s of Proerties of Point Estimators Other Methods Introduction An element is the entity on which data are collected. A oulation is a collection
More informationUsing the Divergence Information Criterion for the Determination of the Order of an Autoregressive Process
Using the Divergence Information Criterion for the Determination of the Order of an Autoregressive Process P. Mantalos a1, K. Mattheou b, A. Karagrigoriou b a.deartment of Statistics University of Lund
More informationOne-way ANOVA Inference for one-way ANOVA
One-way ANOVA Inference for one-way ANOVA IPS Chater 12.1 2009 W.H. Freeman and Comany Objectives (IPS Chater 12.1) Inference for one-way ANOVA Comaring means The two-samle t statistic An overview of ANOVA
More informationSTART Selected Topics in Assurance
START Selected Toics in Assurance Related Technologies Table of Contents Introduction Statistical Models for Simle Systems (U/Down) and Interretation Markov Models for Simle Systems (U/Down) and Interretation
More informationarxiv: v3 [physics.data-an] 23 May 2011
Date: October, 8 arxiv:.7v [hysics.data-an] May -values for Model Evaluation F. Beaujean, A. Caldwell, D. Kollár, K. Kröninger Max-Planck-Institut für Physik, München, Germany CERN, Geneva, Switzerland
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 informationEvaluating Process Capability Indices for some Quality Characteristics of a Manufacturing Process
Journal of Statistical and Econometric Methods, vol., no.3, 013, 105-114 ISSN: 051-5057 (rint version), 051-5065(online) Scienress Ltd, 013 Evaluating Process aability Indices for some Quality haracteristics
More informationAI*IA 2003 Fusion of Multiple Pattern Classifiers PART III
AI*IA 23 Fusion of Multile Pattern Classifiers PART III AI*IA 23 Tutorial on Fusion of Multile Pattern Classifiers by F. Roli 49 Methods for fusing multile classifiers Methods for fusing multile classifiers
More informationThe one-sample t test for a population mean
Objectives Constructing and assessing hyotheses The t-statistic and the P-value Statistical significance The one-samle t test for a oulation mean One-sided versus two-sided tests Further reading: OS3,
More informationCHAPTER-II Control Charts for Fraction Nonconforming using m-of-m Runs Rules
CHAPTER-II Control Charts for Fraction Nonconforming using m-of-m Runs Rules. Introduction: The is widely used in industry to monitor the number of fraction nonconforming units. A nonconforming unit is
More informationCompletely Randomized Design
CHAPTER 4 Comletely Randomized Design 4.1 Descrition of the Design Chaters 1 to 3 introduced some basic concets and statistical tools that are used in exerimental design. In this and the following chaters,
More informationLinear diophantine equations for discrete tomography
Journal of X-Ray Science and Technology 10 001 59 66 59 IOS Press Linear diohantine euations for discrete tomograhy Yangbo Ye a,gewang b and Jiehua Zhu a a Deartment of Mathematics, The University of Iowa,
More informationPlotting the Wilson distribution
, Survey of English Usage, University College London Setember 018 1 1. Introduction We have discussed the Wilson score interval at length elsewhere (Wallis 013a, b). Given an observed Binomial roortion
More informationarxiv: v1 [physics.data-an] 26 Oct 2012
Constraints on Yield Parameters in Extended Maximum Likelihood Fits Till Moritz Karbach a, Maximilian Schlu b a TU Dortmund, Germany, moritz.karbach@cern.ch b TU Dortmund, Germany, maximilian.schlu@cern.ch
More informationThe Mathematics of Winning Streaks
The Mathematics of Winning Streaks Erik Leffler lefflererik@gmail.com under the direction of Prof. Henrik Eriksson Deartment of Comuter Science and Communications Royal Institute of Technology Research
More informationGENERATING FUZZY RULES FOR PROTEIN CLASSIFICATION E. G. MANSOORI, M. J. ZOLGHADRI, S. D. KATEBI, H. MOHABATKAR, R. BOOSTANI AND M. H.
Iranian Journal of Fuzzy Systems Vol. 5, No. 2, (2008). 21-33 GENERATING FUZZY RULES FOR PROTEIN CLASSIFICATION E. G. MANSOORI, M. J. ZOLGHADRI, S. D. KATEBI, H. MOHABATKAR, R. BOOSTANI AND M. H. SADREDDINI
More informationModeling and Estimation of Full-Chip Leakage Current Considering Within-Die Correlation
6.3 Modeling and Estimation of Full-Chi Leaage Current Considering Within-Die Correlation Khaled R. eloue, Navid Azizi, Farid N. Najm Deartment of ECE, University of Toronto,Toronto, Ontario, Canada {haled,nazizi,najm}@eecg.utoronto.ca
More informationOn the Estimation Of Population Mean Under Systematic Sampling Using Auxiliary Attributes
Oriental Journal of Physical Sciences Vol 1 (1 & ) 17 (016) On the Estimation Of Poulation Mean Under Systematic Samling Using Auxiliary Attributes Usman Shahzad Deartment of Mathematics Statistics PMAS
More informationObjectives. Estimating with confidence Confidence intervals.
Objectives Estimating with confidence Confidence intervals. Sections 6.1 and 7.1 in IPS. Page 174-180 OS3. Choosing the samle size t distributions. Further reading htt://onlinestatbook.com/2/estimation/t_distribution.html
More information3.4 Design Methods for Fractional Delay Allpass Filters
Chater 3. Fractional Delay Filters 15 3.4 Design Methods for Fractional Delay Allass Filters Above we have studied the design of FIR filters for fractional delay aroximation. ow we show how recursive or
More informationCryptanalysis of Pseudorandom Generators
CSE 206A: Lattice Algorithms and Alications Fall 2017 Crytanalysis of Pseudorandom Generators Instructor: Daniele Micciancio UCSD CSE As a motivating alication for the study of lattice in crytograhy we
More informationOne step ahead prediction using Fuzzy Boolean Neural Networks 1
One ste ahead rediction using Fuzzy Boolean eural etworks 1 José A. B. Tomé IESC-ID, IST Rua Alves Redol, 9 1000 Lisboa jose.tome@inesc-id.t João Paulo Carvalho IESC-ID, IST Rua Alves Redol, 9 1000 Lisboa
More informationAN OPTIMAL CONTROL CHART FOR NON-NORMAL PROCESSES
AN OPTIMAL CONTROL CHART FOR NON-NORMAL PROCESSES Emmanuel Duclos, Maurice Pillet To cite this version: Emmanuel Duclos, Maurice Pillet. AN OPTIMAL CONTROL CHART FOR NON-NORMAL PRO- CESSES. st IFAC Worsho
More informationMonte Carlo Studies. Monte Carlo Studies. Sampling Distribution
Monte Carlo Studies Do not let yourself be intimidated by the material in this lecture This lecture involves more theory but is meant to imrove your understanding of: Samling distributions and tests of
More informationOn Optimization of Power Coefficient of HAWT
Journal of Power and Energy Engineering, 14,, 198- Published Online Aril 14 in Scies htt://wwwscirorg/journal/jee htt://dxdoiorg/1436/jee1448 On Otimization of Power Coefficient of HAWT Marat Z Dosaev
More informationPretest (Optional) Use as an additional pacing tool to guide instruction. August 21
Trimester 1 Pretest (Otional) Use as an additional acing tool to guide instruction. August 21 Beyond the Basic Facts In Trimester 1, Grade 8 focus on multilication. Daily Unit 1: Rational vs. Irrational
More informationA MIXED CONTROL CHART ADAPTED TO THE TRUNCATED LIFE TEST BASED ON THE WEIBULL DISTRIBUTION
O P E R A T I O N S R E S E A R C H A N D D E C I S I O N S No. 27 DOI:.5277/ord73 Nasrullah KHAN Muhammad ASLAM 2 Kyung-Jun KIM 3 Chi-Hyuck JUN 4 A MIXED CONTROL CHART ADAPTED TO THE TRUNCATED LIFE TEST
More informationBayesian Spatially Varying Coefficient Models in the Presence of Collinearity
Bayesian Satially Varying Coefficient Models in the Presence of Collinearity David C. Wheeler 1, Catherine A. Calder 1 he Ohio State University 1 Abstract he belief that relationshis between exlanatory
More informationUncorrelated Multilinear Principal Component Analysis for Unsupervised Multilinear Subspace Learning
TNN-2009-P-1186.R2 1 Uncorrelated Multilinear Princial Comonent Analysis for Unsuervised Multilinear Subsace Learning Haiing Lu, K. N. Plataniotis and A. N. Venetsanooulos The Edward S. Rogers Sr. Deartment
More informationVIBRATION ANALYSIS OF BEAMS WITH MULTIPLE CONSTRAINED LAYER DAMPING PATCHES
Journal of Sound and Vibration (998) 22(5), 78 85 VIBRATION ANALYSIS OF BEAMS WITH MULTIPLE CONSTRAINED LAYER DAMPING PATCHES Acoustics and Dynamics Laboratory, Deartment of Mechanical Engineering, The
More informationAdaptive estimation with change detection for streaming data
Adative estimation with change detection for streaming data A thesis resented for the degree of Doctor of Philosohy of the University of London and the Diloma of Imerial College by Dean Adam Bodenham Deartment
More informationThe Poisson Regression Model
The Poisson Regression Model The Poisson regression model aims at modeling a counting variable Y, counting the number of times that a certain event occurs during a given time eriod. We observe a samle
More informationNotes on Instrumental Variables Methods
Notes on Instrumental Variables Methods Michele Pellizzari IGIER-Bocconi, IZA and frdb 1 The Instrumental Variable Estimator Instrumental variable estimation is the classical solution to the roblem of
More informationCONTROL SYSTEMS, ROBOTICS, AND AUTOMATION Vol. III Stability Theory - Peter C. Müller
STABILITY THEORY Peter C. Müller University of Wuertal, Germany Keywords: Asymtotic stability, Eonential stability, Linearization, Linear systems, Lyaunov equation, Lyaunov function, Lyaunov stability,
More informationINTRODUCTION. Please write to us at if you have any comments or ideas. We love to hear from you.
Casio FX-570ES One-Page Wonder INTRODUCTION Welcome to the world of Casio s Natural Dislay scientific calculators. Our exeriences of working with eole have us understand more about obstacles eole face
More informationAn Investigation on the Numerical Ill-conditioning of Hybrid State Estimators
An Investigation on the Numerical Ill-conditioning of Hybrid State Estimators S. K. Mallik, Student Member, IEEE, S. Chakrabarti, Senior Member, IEEE, S. N. Singh, Senior Member, IEEE Deartment of Electrical
More informationAnalysis of M/M/n/K Queue with Multiple Priorities
Analysis of M/M/n/K Queue with Multile Priorities Coyright, Sanjay K. Bose For a P-riority system, class P of highest riority Indeendent, Poisson arrival rocesses for each class with i as average arrival
More informationOutline. Markov Chains and Markov Models. Outline. Markov Chains. Markov Chains Definitions Huizhen Yu
and Markov Models Huizhen Yu janey.yu@cs.helsinki.fi Det. Comuter Science, Univ. of Helsinki Some Proerties of Probabilistic Models, Sring, 200 Huizhen Yu (U.H.) and Markov Models Jan. 2 / 32 Huizhen Yu
More informationResearch Note REGRESSION ANALYSIS IN MARKOV CHAIN * A. Y. ALAMUTI AND M. R. MESHKANI **
Iranian Journal of Science & Technology, Transaction A, Vol 3, No A3 Printed in The Islamic Reublic of Iran, 26 Shiraz University Research Note REGRESSION ANALYSIS IN MARKOV HAIN * A Y ALAMUTI AND M R
More informationYixi Shi. Jose Blanchet. IEOR Department Columbia University New York, NY 10027, USA. IEOR Department Columbia University New York, NY 10027, USA
Proceedings of the 2011 Winter Simulation Conference S. Jain, R. R. Creasey, J. Himmelsach, K. P. White, and M. Fu, eds. EFFICIENT RARE EVENT SIMULATION FOR HEAVY-TAILED SYSTEMS VIA CROSS ENTROPY Jose
More informationDeriving Indicator Direct and Cross Variograms from a Normal Scores Variogram Model (bigaus-full) David F. Machuca Mory and Clayton V.
Deriving ndicator Direct and Cross Variograms from a Normal Scores Variogram Model (bigaus-full) David F. Machuca Mory and Clayton V. Deutsch Centre for Comutational Geostatistics Deartment of Civil &
More informationMathematical Efficiency Modeling of Static Power Converters
Fabrício Hoff Duont Regional Integrated University of Uer Uruguai and Missions (URI Av. Assis Brasil, 9, 980 000 Frederico Westhalen, RS, Brazil Contact: fhd@ieee.org Mathematical Efficiency Modeling of
More informationUniversal Finite Memory Coding of Binary Sequences
Deartment of Electrical Engineering Systems Universal Finite Memory Coding of Binary Sequences Thesis submitted towards the degree of Master of Science in Electrical and Electronic Engineering in Tel-Aviv
More informationSlides Prepared by JOHN S. LOUCKS St. Edward s s University Thomson/South-Western. Slide
s Preared by JOHN S. LOUCKS St. Edward s s University 1 Chater 11 Comarisons Involving Proortions and a Test of Indeendence Inferences About the Difference Between Two Poulation Proortions Hyothesis Test
More informationSupplementary Materials for Robust Estimation of the False Discovery Rate
Sulementary Materials for Robust Estimation of the False Discovery Rate Stan Pounds and Cheng Cheng This sulemental contains roofs regarding theoretical roerties of the roosed method (Section S1), rovides
More informationMonopolist s mark-up and the elasticity of substitution
Croatian Oerational Research Review 377 CRORR 8(7), 377 39 Monoolist s mark-u and the elasticity of substitution Ilko Vrankić, Mira Kran, and Tomislav Herceg Deartment of Economic Theory, Faculty of Economics
More informationGenetic Algorithms, Selection Schemes, and the Varying Eects of Noise. IlliGAL Report No November Department of General Engineering
Genetic Algorithms, Selection Schemes, and the Varying Eects of Noise Brad L. Miller Det. of Comuter Science University of Illinois at Urbana-Chamaign David E. Goldberg Det. of General Engineering University
More informationConvex Optimization methods for Computing Channel Capacity
Convex Otimization methods for Comuting Channel Caacity Abhishek Sinha Laboratory for Information and Decision Systems (LIDS), MIT sinhaa@mit.edu May 15, 2014 We consider a classical comutational roblem
More informationOn Wald-Type Optimal Stopping for Brownian Motion
J Al Probab Vol 34, No 1, 1997, (66-73) Prerint Ser No 1, 1994, Math Inst Aarhus On Wald-Tye Otimal Stoing for Brownian Motion S RAVRSN and PSKIR The solution is resented to all otimal stoing roblems of
More informationDETC2003/DAC AN EFFICIENT ALGORITHM FOR CONSTRUCTING OPTIMAL DESIGN OF COMPUTER EXPERIMENTS
Proceedings of DETC 03 ASME 003 Design Engineering Technical Conferences and Comuters and Information in Engineering Conference Chicago, Illinois USA, Setember -6, 003 DETC003/DAC-48760 AN EFFICIENT ALGORITHM
More informationSoci Data Analysis in Sociological Research. Homework 4 Computer Handout. Chapter 19 Confidence Intervals for Proportions
University of North Carolina Chael Hill Soci252-002 Data Analysis in Sociological Research Sring 2013 Professor François Nielsen Homework 4 Comuter Handout Readings This handout covers comuter issues related
More informationOn the Chvatál-Complexity of Knapsack Problems
R u t c o r Research R e o r t On the Chvatál-Comlexity of Knasack Problems Gergely Kovács a Béla Vizvári b RRR 5-08, October 008 RUTCOR Rutgers Center for Oerations Research Rutgers University 640 Bartholomew
More informationUniform Law on the Unit Sphere of a Banach Space
Uniform Law on the Unit Shere of a Banach Sace by Bernard Beauzamy Société de Calcul Mathématique SA Faubourg Saint Honoré 75008 Paris France Setember 008 Abstract We investigate the construction of a
More informationSpectral Analysis by Stationary Time Series Modeling
Chater 6 Sectral Analysis by Stationary Time Series Modeling Choosing a arametric model among all the existing models is by itself a difficult roblem. Generally, this is a riori information about the signal
More informationApplied Mathematics and Computation
Alied Mathematics and Comutation 217 (2010) 1887 1895 Contents lists available at ScienceDirect Alied Mathematics and Comutation journal homeage: www.elsevier.com/locate/amc Derivative free two-oint methods
More informationEstimation of Separable Representations in Psychophysical Experiments
Estimation of Searable Reresentations in Psychohysical Exeriments Michele Bernasconi (mbernasconi@eco.uninsubria.it) Christine Choirat (cchoirat@eco.uninsubria.it) Raffaello Seri (rseri@eco.uninsubria.it)
More informationEffective conductivity in a lattice model for binary disordered media with complex distributions of grain sizes
hys. stat. sol. b 36, 65-633 003 Effective conductivity in a lattice model for binary disordered media with comlex distributions of grain sizes R. PIASECKI Institute of Chemistry, University of Oole, Oleska
More informationOn Wrapping of Exponentiated Inverted Weibull Distribution
IJIRST International Journal for Innovative Research in Science & Technology Volume 3 Issue 11 Aril 217 ISSN (online): 2349-61 On Wraing of Exonentiated Inverted Weibull Distribution P.Srinivasa Subrahmanyam
More informationAsymptotic Properties of the Markov Chain Model method of finding Markov chains Generators of..
IOSR Journal of Mathematics (IOSR-JM) e-issn: 78-578, -ISSN: 319-765X. Volume 1, Issue 4 Ver. III (Jul. - Aug.016), PP 53-60 www.iosrournals.org Asymtotic Proerties of the Markov Chain Model method of
More informationChurilova Maria Saint-Petersburg State Polytechnical University Department of Applied Mathematics
Churilova Maria Saint-Petersburg State Polytechnical University Deartment of Alied Mathematics Technology of EHIS (staming) alied to roduction of automotive arts The roblem described in this reort originated
More informationBackground. GLM with clustered data. The problem. Solutions. A fixed effects approach
Background GLM with clustered data A fixed effects aroach Göran Broström Poisson or Binomial data with the following roerties A large data set, artitioned into many relatively small grous, and where members
More informationPERFORMANCE BASED DESIGN SYSTEM FOR CONCRETE MIXTURE WITH MULTI-OPTIMIZING GENETIC ALGORITHM
PERFORMANCE BASED DESIGN SYSTEM FOR CONCRETE MIXTURE WITH MULTI-OPTIMIZING GENETIC ALGORITHM Takafumi Noguchi 1, Iei Maruyama 1 and Manabu Kanematsu 1 1 Deartment of Architecture, University of Tokyo,
More informationMorten Frydenberg Section for Biostatistics Version :Friday, 05 September 2014
Morten Frydenberg Section for Biostatistics Version :Friday, 05 Setember 204 All models are aroximations! The best model does not exist! Comlicated models needs a lot of data. lower your ambitions or get
More informationHomogeneous and Inhomogeneous Model for Flow and Heat Transfer in Porous Materials as High Temperature Solar Air Receivers
Excert from the roceedings of the COMSOL Conference 1 aris Homogeneous and Inhomogeneous Model for Flow and Heat ransfer in orous Materials as High emerature Solar Air Receivers Olena Smirnova 1 *, homas
More informationKeywords: pile, liquefaction, lateral spreading, analysis ABSTRACT
Key arameters in seudo-static analysis of iles in liquefying sand Misko Cubrinovski Deartment of Civil Engineering, University of Canterbury, Christchurch 814, New Zealand Keywords: ile, liquefaction,
More informationSupport Vector Machines versus Artificial Neural Networks for wood Dielectric Loss Factor estimation
Suort Vector Machines versus Artificial Neural Networks for wood Dielectric Loss Factor estimation Lazaros Iliadis 1*, Stavros Tachos, Stavros Avramidis 3 Shawn Mansfield 3 1 Democritus University of Thrace,
More informationShadow Computing: An Energy-Aware Fault Tolerant Computing Model
Shadow Comuting: An Energy-Aware Fault Tolerant Comuting Model Bryan Mills, Taieb Znati, Rami Melhem Deartment of Comuter Science University of Pittsburgh (bmills, znati, melhem)@cs.itt.edu Index Terms
More informationState Estimation with ARMarkov Models
Deartment of Mechanical and Aerosace Engineering Technical Reort No. 3046, October 1998. Princeton University, Princeton, NJ. State Estimation with ARMarkov Models Ryoung K. Lim 1 Columbia University,
More informationEstimation of component redundancy in optimal age maintenance
EURO MAINTENANCE 2012, Belgrade 14-16 May 2012 Proceedings of the 21 st International Congress on Maintenance and Asset Management Estimation of comonent redundancy in otimal age maintenance Jorge ioa
More informationOn parameter estimation in deformable models
Downloaded from orbitdtudk on: Dec 7, 07 On arameter estimation in deformable models Fisker, Rune; Carstensen, Jens Michael Published in: Proceedings of the 4th International Conference on Pattern Recognition
More information