Spurious Significance of Treatment Effects in Overfitted Fixed Effect Models Albrecht Ritschl 1 LSE and CEPR. March 2009
|
|
- Antony Dalton
- 5 years ago
- Views:
Transcription
1 Spurious Significance of reatment Effects in Overfitte Fixe Effect Moels Albrecht Ritschl LSE an CEPR March 2009 Introuction Evaluating subsample means across groups an time perios is common in panel stuies that evaluate the treatment effects of training programs, labor market policies, currency unions etc. Comparison of means between treate an non-treate groups may occur along the time axis (fixe effects, FE), the cross section (poole OLS, IV) or in a combination of the two (ifference in ifferences, DiD, see Ashenfelter (978), Ashenfelter an Car (985)), epening on the choice of ientifying assumptions about selectivity an common trens (see Heckman, Lalone, an Smith (999)). Despite their wiesprea use in evaluation stuies, FE an DiD estimators have acquire a reputation for generating spuriously low stanar errors on the estimate treatment effect. Bertran, Duflo, an Mullainathan (2004) survey empirical applications of the DiD estimator, an fin that much of this phenomenon can be attribute to autocorrelation. In a simulate ataset, they show that many stanar methos for ealing with autocorrelation yiel ownwar biase stanar errors on the treatment effect coefficient. he following note argues that spurious significance of treatment effects in panels may also occur in the absence of autocorrelation. his phenomenon arises in overfitte FE an DiD moeling of within-group comparisons. Overfitting in such moels occurs if observation-specific iniviual fixe effects (IFE) are specifie, although the comparison woul be ientifie by group-specific fixe effects. In evaluation stuies, ientifying the average treatment effect on the treate through a withingroup estimator woul require a group fixe effect on the treate (FE), see e.g. Angrist an Pischke (2009). Yet specifying an overfitte regression with IFE instea may seem innocuous to the applie researcher, as the coefficient estimates on the treatment effect uner both fixe effect specifications are ientical. Moreover, stanar software packages provie easy to use options for iniviual fixe effects, making an overfitte specification seem attractive. However, while the estimate treatment effect uner IFE an FE is the same, its estimate stanar error is not. Overfitting through IFE leas to spurious precision of the estimate treatment effect coefficient. he resulting bias is relate to the reuction in the resiual sum of squares inuce by employing IFE instea of FE. Uner ieal conitions where all Financial support from Deutsche Forschungsgemeinschaft uner SFB649 Economic Risk at Humbolt University Berlin is gratefully acknowlege.
2 other regressors are uncorrelate to the treatment an the fixe effects, this relation is strictly proportional. he rest of this note is structure as followe. he next section provies the setup. Section (3) presents the result. Section (4) conclues. 2 A Minimal an an Overfitte Setup Consier a ata panel with n observation units in the cross section an time perios. In this panel, enote by Y n the epenent variable. Z is a matrix of characteristics of interest, as well as any time fixe effects, while X inclues the regression constant an/or a suitably chosen matrix of either iniviual or group fixe effects. A policy treatment is applie to some observation units y i uring treatment perio τ {s,..., s+τ}. reatment uring perio t τ is inicate by a (n )-vector of ummy variables t, which are equal to one if unit i is uner treatment at time t, an zero otherwise. tr( ) < n is the number of observation units i in the treatment group. Accoringly, n is the number of observation units in the non-treate group. A stanar linear panel moel of this treatment effect problem is: Y (XD)β + Zγ + v () where v N(0, σ 2 v) an where D (0... s... τ... 0) is a ummy vector capturing the policy treatment in τ perios. Fixe effects estimation of moels like () is a popular (yet problematic) attempt to ensure the exogeneity of D with respect to the isturbance term v. o focus on the essentials, consier an ieal regression in which any characteristics inclue in Z are orthogonal to the fixe effects X an the treatment ummy D. Define the etrene variable y M z Y with M z I Z(Z Z) Z, where the influence on Y of any such characteristics, as well as any time fixe effects inclue in Z has been remove 2. As M z XD XD if Z XD 0, the moel becomes a Least Squares Dummy Variables (LSDV) regression on the fixe effect terms an the treatment ummy only: y (XD)β + u (2) where X is a suitably chosen matrix of fixe effects, D ( τ... 0) is a ummy vector capturing the policy treatment in τ perios, an u N(0, σu). 2 Uner iniviual fixe effects, X consists of stacke (n n) ientity matrices: X I I n n. I n n n n Uner the alternative assumption of a group fixe effect on the treate, matrix X takes the form: 2 ime fixe effects woul be orthogonal to X. heir inclusion in Z makes the FE an DiD estimators in y ientical. 2
3 X G.. n 2 Note that the column imension of X G is 2 as oppose to n in X I. LSDV estimation of (2) uner the two ifferent fixe effect specifications yiels: ˆβ I (X I D] X I D]) X I D] y ˆΩ ˆβ,I ˆσ 2 u,i (XI D] X I D]) (3) ˆβ G (X G D] X G D]) X G D] y ˆΩ ˆβ,G ˆσ 2 u,g (XG D] X G D]) (4) Let b I be the n+th (i.e., last) element of ˆβ I, an b G be the 3r (i.e., last) element of ˆβ G. b I an b G are the coefficients on the treatment ummy uner Iniviual Fixe Effects (I) an the Group Fixe Effect on the reate (G), respectively. Likewise, let ˆσ 2 (b I ) ˆΩ ˆβ,I,(n+,n+) an ˆσ 2 (b G ) ˆΩ ˆβ,G,(3,3) be the estimate variances of these coefficients, with Sn+,n+ I X I D] X I D]) n+,n+ an S3,3 G X G D] X G D]) 3,3 as the pertaining elements of the matrix inverses in (3) an (4), respectively. 3 Spurious Significance uner Overfitting Consier a treatment effect moel as in eq. (2), in which the enogenous variable has been etrene from any time effects, an in which any further characteristics are orthogonal to the fixe effects an treatment ummy, an have been eliminate as well. Estimation uner the alternatives of Iniviual Fixe Effects (IFE) an Fixe Effects on the reate (FE) as in (3) yiels ientical coefficient estimates on the treatment effects. However, the estimate variances on these coefficients in (4) iffer, owing to the presence of unnecessary ummy variables in the IFE specification that artificially increases the fit of the regression. his is expresse in the following Proposition. In a treatment effect moel as in eq. (2), the estimate variance of the treatment effect coefficient is ownwar biase uner IFE relative to FE. he bias is equal to the ratio of the estimate resiual variances uner IFE an FE: ˆσ 2 (b I ) ˆσ2 u,i. ˆσ 2 (b G ) ˆσ u,g 2 Proof. It suffices to show that Sn+,n+ I S3,3, G i.e. the last elements on the main iagonal of the inverte prouct sum matrices in eqs. (3) an (4) are ientical. By elementary operations, X I X I I n n. Hence, uner Iniviual Fixe Effects: ( ) X I D] X I I D] n n τ τ τ where, as efine further above, tr( ) tr( ). Inverting this partitione matrix, we fin for the (n+,n+)-element of the inverse: (X I D] X I D]) n+,n+ (τ τ τ) 3 τ( τ) (5)
4 Uner Fixe Effects on the reate, the prouct sum matrix becomes: ( ) n X G X G Hence, n τ X G D] X G D] τ τ τ τ Inverting this partitione matrix, we fin for the element (3,3) of the inverse: Using this becomes: (X G D] X G D]) 3,3 τ τ( (X G X G ) (X G D] X G D]) 3,3 τ τ τ τ 2 (n ) )(X G X G ) τ ( ) n ( ) (n ) τ( ) τ n ( )] (6) ( )] ( )] (n ) τ( ) 0 τ (n ) ] (7) is equal to (5), which completes the proof. τ( τ) (7) In applie work, the possible correlation of aitional characteristics Z with XD means the above relation no longer obtains exactly. Unless, however, this correlation amounts to near-collinearity, its effect is small relative to the overfitting effect escribe in the proposition. 4 Conclusion Applications of fixe effect an ifference in ifferences estimators sometimes employ iniviual, observation-unit specific fixe effects when group-specific fixe effects woul suffice for ientification. his note has examine the properties of ifference in ifferences estimators of treatment effects uner two ifferent fixe effects specifications. It shows that overfitting uner iniviual, observation-unit specific fixe effects generates lower stanar errors on the treatment effect coefficient than estimation uner a minimal specification with group specific effects. Depening on the correlation with other regressors, this bias grows at or near the relative ecrease 4
5 of the resiual sum of squares as the number of overfitte fixe effects increases. In large samples, which are frequent in evaluation stuies, this overfitting bias may lea to substantial unerestimation of the stanar errors on treatment effect coefficients, an hence to substantial false positives. References Angrist, J., an S. Pischke (2009): Mostly Harmless Econometrics. Princeton University Press. Ashenfelter, O. (978): Estimating the Effect of raining Programs on Earnings, Review of Economics an Statistics, 60, Ashenfelter, O., an D. Car (985): Using the Longituinal Structure of Earnings to Estimate the Effect of raining Programs, Review of Economics an Statistics, 64, Bertran, M., E. Duflo, an S. Mullainathan (2004): How Much Shoul We rust the Differences-in-Differences Estimator?, Quarterly Journal of Economics, 9, Heckman, J., R. Lalone, an J. Smith (999): he Economics an Econometrics of Active Labor Market Programs, in Hanbook of Labor Economics, e. by O. Ashenfelter, an D. Car, pp Elsevier. 5
Test of Hypotheses in a Time Trend Panel Data Model with Serially Correlated Error Component Disturbances
HE UNIVERSIY OF EXAS A SAN ANONIO, COLLEGE OF BUSINESS Working Paper SERIES Date September 25, 205 WP # 000ECO-66-205 est of Hypotheses in a ime ren Panel Data Moel with Serially Correlate Error Component
More informationUNIFYING PCA AND MULTISCALE APPROACHES TO FAULT DETECTION AND ISOLATION
UNIFYING AND MULISCALE APPROACHES O FAUL DEECION AND ISOLAION Seongkyu Yoon an John F. MacGregor Dept. Chemical Engineering, McMaster University, Hamilton Ontario Canaa L8S 4L7 yoons@mcmaster.ca macgreg@mcmaster.ca
More informationLATTICE-BASED D-OPTIMUM DESIGN FOR FOURIER REGRESSION
The Annals of Statistics 1997, Vol. 25, No. 6, 2313 2327 LATTICE-BASED D-OPTIMUM DESIGN FOR FOURIER REGRESSION By Eva Riccomagno, 1 Rainer Schwabe 2 an Henry P. Wynn 1 University of Warwick, Technische
More information19 Eigenvalues, Eigenvectors, Ordinary Differential Equations, and Control
19 Eigenvalues, Eigenvectors, Orinary Differential Equations, an Control This section introuces eigenvalues an eigenvectors of a matrix, an iscusses the role of the eigenvalues in etermining the behavior
More informationOnline Appendix for Trade Policy under Monopolistic Competition with Firm Selection
Online Appenix for Trae Policy uner Monopolistic Competition with Firm Selection Kyle Bagwell Stanfor University an NBER Seung Hoon Lee Georgia Institute of Technology September 6, 2018 In this Online
More informationOn the Use of Linear Fixed Effects Regression Models for Causal Inference
On the Use of Linear Fixed Effects Regression Models for ausal Inference Kosuke Imai Department of Politics Princeton University Joint work with In Song Kim Atlantic ausal Inference onference Johns Hopkins
More informationWeb-Based Technical Appendix: Multi-Product Firms and Trade Liberalization
Web-Base Technical Appeni: Multi-Prouct Firms an Trae Liberalization Anrew B. Bernar Tuck School of Business at Dartmouth & NBER Stephen J. Reing LSE, Yale School of Management & CEPR Peter K. Schott Yale
More informationLecture Introduction. 2 Examples of Measure Concentration. 3 The Johnson-Lindenstrauss Lemma. CS-621 Theory Gems November 28, 2012
CS-6 Theory Gems November 8, 0 Lecture Lecturer: Alesaner Mąry Scribes: Alhussein Fawzi, Dorina Thanou Introuction Toay, we will briefly iscuss an important technique in probability theory measure concentration
More informationu!i = a T u = 0. Then S satisfies
Deterministic Conitions for Subspace Ientifiability from Incomplete Sampling Daniel L Pimentel-Alarcón, Nigel Boston, Robert D Nowak University of Wisconsin-Maison Abstract Consier an r-imensional subspace
More informationA Modification of the Jarque-Bera Test. for Normality
Int. J. Contemp. Math. Sciences, Vol. 8, 01, no. 17, 84-85 HIKARI Lt, www.m-hikari.com http://x.oi.org/10.1988/ijcms.01.9106 A Moification of the Jarque-Bera Test for Normality Moawa El-Fallah Ab El-Salam
More informationSystems & Control Letters
Systems & ontrol Letters ( ) ontents lists available at ScienceDirect Systems & ontrol Letters journal homepage: www.elsevier.com/locate/sysconle A converse to the eterministic separation principle Jochen
More information'HVLJQ &RQVLGHUDWLRQ LQ 0DWHULDO 6HOHFWLRQ 'HVLJQ 6HQVLWLYLW\,1752'8&7,21
Large amping in a structural material may be either esirable or unesirable, epening on the engineering application at han. For example, amping is a esirable property to the esigner concerne with limiting
More informationA simple underidentification test for linear IV models, with an application to dynamic panel data models
A simple unerientification test for linear IV moels, with an application to ynamic panel ata moels Frank Winmeijer Department of Economics University of Bristol, UK an Cemmap, Lonon, UK October 2016 Preliminary
More informationSurvey-weighted Unit-Level Small Area Estimation
Survey-weighte Unit-Level Small Area Estimation Jan Pablo Burgar an Patricia Dörr Abstract For evience-base regional policy making, geographically ifferentiate estimates of socio-economic inicators are
More informationWeb Appendix to Firm Heterogeneity and Aggregate Welfare (Not for Publication)
Web ppeni to Firm Heterogeneity an ggregate Welfare Not for Publication Marc J. Melitz Harvar University, NBER, an CEPR Stephen J. Reing Princeton University, NBER, an CEPR March 6, 203 Introuction his
More informationImproving Estimation Accuracy in Nonrandomized Response Questioning Methods by Multiple Answers
International Journal of Statistics an Probability; Vol 6, No 5; September 207 ISSN 927-7032 E-ISSN 927-7040 Publishe by Canaian Center of Science an Eucation Improving Estimation Accuracy in Nonranomize
More informationLeast-Squares Regression on Sparse Spaces
Least-Squares Regression on Sparse Spaces Yuri Grinberg, Mahi Milani Far, Joelle Pineau School of Computer Science McGill University Montreal, Canaa {ygrinb,mmilan1,jpineau}@cs.mcgill.ca 1 Introuction
More informationResearch Article When Inflation Causes No Increase in Claim Amounts
Probability an Statistics Volume 2009, Article ID 943926, 10 pages oi:10.1155/2009/943926 Research Article When Inflation Causes No Increase in Claim Amounts Vytaras Brazauskas, 1 Bruce L. Jones, 2 an
More informationIntroduction to the Vlasov-Poisson system
Introuction to the Vlasov-Poisson system Simone Calogero 1 The Vlasov equation Consier a particle with mass m > 0. Let x(t) R 3 enote the position of the particle at time t R an v(t) = ẋ(t) = x(t)/t its
More informationAdvanced Econometrics
Based on the textbook by Verbeek: A Guide to Modern Econometrics Robert M. Kunst robert.kunst@univie.ac.at University of Vienna and Institute for Advanced Studies Vienna May 16, 2013 Outline Univariate
More informationModeling time-varying storage components in PSpice
Moeling time-varying storage components in PSpice Dalibor Biolek, Zenek Kolka, Viera Biolkova Dept. of EE, FMT, University of Defence Brno, Czech Republic Dept. of Microelectronics/Raioelectronics, FEEC,
More informationQuality competition versus price competition goods: An empirical classification
HEID Working Paper No7/2008 Quality competition versus price competition goos: An empirical classification Richar E. Balwin an Taashi Ito Grauate Institute of International an Development Stuies Abstract
More informationDamage identification based on incomplete modal data and constrained nonlinear multivariable function
Journal of Physics: Conference Series PAPER OPEN ACCESS Damage ientification base on incomplete moal ata an constraine nonlinear multivariable function To cite this article: S S Kourehli 215 J. Phys.:
More informationProblem Sheet 2: Eigenvalues and eigenvectors and their use in solving linear ODEs
Problem Sheet 2: Eigenvalues an eigenvectors an their use in solving linear ODEs If you fin any typos/errors in this problem sheet please email jk28@icacuk The material in this problem sheet is not examinable
More informationCONSERVATION PROPERTIES OF SMOOTHED PARTICLE HYDRODYNAMICS APPLIED TO THE SHALLOW WATER EQUATIONS
BIT 0006-3835/00/4004-0001 $15.00 200?, Vol.??, No.??, pp.?????? c Swets & Zeitlinger CONSERVATION PROPERTIES OF SMOOTHE PARTICLE HYROYNAMICS APPLIE TO THE SHALLOW WATER EQUATIONS JASON FRANK 1 an SEBASTIAN
More informationUnexplained Gaps and Oaxaca-Blinder Decompositions
DISCUSSION PAPER SERIES IZA DP No. 4159 Uneplaine Gaps an Oaaca-Bliner Decompositions To E. Eler John H. Goeeris Steven J. Haier April 009 Forschungsinstitut zur Zukunft er Arbeit Institute for the Stuy
More informationPlacement and tuning of resonance dampers on footbridges
Downloae from orbit.tu.k on: Jan 17, 19 Placement an tuning of resonance ampers on footbriges Krenk, Steen; Brønen, Aners; Kristensen, Aners Publishe in: Footbrige 5 Publication ate: 5 Document Version
More informationCapacity Analysis of MIMO Systems with Unknown Channel State Information
Capacity Analysis of MIMO Systems with Unknown Channel State Information Jun Zheng an Bhaskar D. Rao Dept. of Electrical an Computer Engineering University of California at San Diego e-mail: juzheng@ucs.eu,
More informationGaussian processes with monotonicity information
Gaussian processes with monotonicity information Anonymous Author Anonymous Author Unknown Institution Unknown Institution Abstract A metho for using monotonicity information in multivariate Gaussian process
More informationTime-of-Arrival Estimation in Non-Line-Of-Sight Environments
2 Conference on Information Sciences an Systems, The Johns Hopkins University, March 2, 2 Time-of-Arrival Estimation in Non-Line-Of-Sight Environments Sinan Gezici, Hisashi Kobayashi an H. Vincent Poor
More informationLogarithmic spurious regressions
Logarithmic spurious regressions Robert M. e Jong Michigan State University February 5, 22 Abstract Spurious regressions, i.e. regressions in which an integrate process is regresse on another integrate
More informationThis module is part of the. Memobust Handbook. on Methodology of Modern Business Statistics
This moule is part of the Memobust Hanbook on Methoology of Moern Business Statistics 26 March 2014 Metho: Balance Sampling for Multi-Way Stratification Contents General section... 3 1. Summary... 3 2.
More informationComputing Exact Confidence Coefficients of Simultaneous Confidence Intervals for Multinomial Proportions and their Functions
Working Paper 2013:5 Department of Statistics Computing Exact Confience Coefficients of Simultaneous Confience Intervals for Multinomial Proportions an their Functions Shaobo Jin Working Paper 2013:5
More informationTHE VAN KAMPEN EXPANSION FOR LINKED DUFFING LINEAR OSCILLATORS EXCITED BY COLORED NOISE
Journal of Soun an Vibration (1996) 191(3), 397 414 THE VAN KAMPEN EXPANSION FOR LINKED DUFFING LINEAR OSCILLATORS EXCITED BY COLORED NOISE E. M. WEINSTEIN Galaxy Scientific Corporation, 2500 English Creek
More informationAgmon Kolmogorov Inequalities on l 2 (Z d )
Journal of Mathematics Research; Vol. 6, No. ; 04 ISSN 96-9795 E-ISSN 96-9809 Publishe by Canaian Center of Science an Eucation Agmon Kolmogorov Inequalities on l (Z ) Arman Sahovic Mathematics Department,
More informationSurvey Sampling. 1 Design-based Inference. Kosuke Imai Department of Politics, Princeton University. February 19, 2013
Survey Sampling Kosuke Imai Department of Politics, Princeton University February 19, 2013 Survey sampling is one of the most commonly use ata collection methos for social scientists. We begin by escribing
More informationLectures - Week 10 Introduction to Ordinary Differential Equations (ODES) First Order Linear ODEs
Lectures - Week 10 Introuction to Orinary Differential Equations (ODES) First Orer Linear ODEs When stuying ODEs we are consiering functions of one inepenent variable, e.g., f(x), where x is the inepenent
More informationensembles When working with density operators, we can use this connection to define a generalized Bloch vector: v x Tr x, v y Tr y
Ph195a lecture notes, 1/3/01 Density operators for spin- 1 ensembles So far in our iscussion of spin- 1 systems, we have restricte our attention to the case of pure states an Hamiltonian evolution. Toay
More informationCostly Divorce and Marriage Rates
MPRA Munich Personal RePEc Archive Costly Divorce an Marriage Rates Anna Yurko NRU - Higher School of Economics 1. February 2012 Online at https://mpra.ub.uni-muenchen.e/37810/ MPRA Paper No. 37810, poste
More informationDamage detection of shear building structure based on FRF response variation
, pp.18-5 http://x.oi.org/10.1457/astl.013.3.05 Damage etection of shear builing structure base on FRF response variation Hee-Chang Eun 1,*, Su-Yong Park 1, Rae-Jung im 1 1 Dept. of Architectural Engineering,
More informationBivariate distributions characterized by one family of conditionals and conditional percentile or mode functions
Journal of Multivariate Analysis 99 2008) 1383 1392 www.elsevier.com/locate/jmva Bivariate istributions characterize by one family of conitionals an conitional ercentile or moe functions Barry C. Arnol
More informationA simple model for the small-strain behaviour of soils
A simple moel for the small-strain behaviour of soils José Jorge Naer Department of Structural an Geotechnical ngineering, Polytechnic School, University of São Paulo 05508-900, São Paulo, Brazil, e-mail:
More informationSYSTEMS OF DIFFERENTIAL EQUATIONS, EULER S FORMULA. where L is some constant, usually called the Lipschitz constant. An example is
SYSTEMS OF DIFFERENTIAL EQUATIONS, EULER S FORMULA. Uniqueness for solutions of ifferential equations. We consier the system of ifferential equations given by x = v( x), () t with a given initial conition
More information3.2 Shot peening - modeling 3 PROCEEDINGS
3.2 Shot peening - moeling 3 PROCEEDINGS Computer assiste coverage simulation François-Xavier Abaie a, b a FROHN, Germany, fx.abaie@frohn.com. b PEENING ACCESSORIES, Switzerlan, info@peening.ch Keywors:
More informationEntanglement is not very useful for estimating multiple phases
PHYSICAL REVIEW A 70, 032310 (2004) Entanglement is not very useful for estimating multiple phases Manuel A. Ballester* Department of Mathematics, University of Utrecht, Box 80010, 3508 TA Utrecht, The
More informationarxiv: v4 [math.pr] 27 Jul 2016
The Asymptotic Distribution of the Determinant of a Ranom Correlation Matrix arxiv:309768v4 mathpr] 7 Jul 06 AM Hanea a, & GF Nane b a Centre of xcellence for Biosecurity Risk Analysis, University of Melbourne,
More informationunder the null hypothesis, the sign test (with continuity correction) rejects H 0 when α n + n 2 2.
Assignment 13 Exercise 8.4 For the hypotheses consiere in Examples 8.12 an 8.13, the sign test is base on the statistic N + = #{i : Z i > 0}. Since 2 n(n + /n 1) N(0, 1) 2 uner the null hypothesis, the
More informationChapter 2 Lagrangian Modeling
Chapter 2 Lagrangian Moeling The basic laws of physics are use to moel every system whether it is electrical, mechanical, hyraulic, or any other energy omain. In mechanics, Newton s laws of motion provie
More informationAddendum to A Simple Differential Equation System for the Description of Competition among Religions
International Mathematical Forum, Vol. 7, 2012, no. 54, 2681-2686 Aenum to A Simple Differential Equation System for the Description of Competition among Religions Thomas Wieer Germany thomas.wieer@t-online.e
More informationMulti-View Clustering via Canonical Correlation Analysis
Technical Report TTI-TR-2008-5 Multi-View Clustering via Canonical Correlation Analysis Kamalika Chauhuri UC San Diego Sham M. Kakae Toyota Technological Institute at Chicago ABSTRACT Clustering ata in
More informationThe influence of the equivalent hydraulic diameter on the pressure drop prediction of annular test section
IOP Conference Series: Materials Science an Engineering PAPER OPEN ACCESS The influence of the equivalent hyraulic iameter on the pressure rop preiction of annular test section To cite this article: A
More informationOPTIMAL CONTROL OF A PRODUCTION SYSTEM WITH INVENTORY-LEVEL-DEPENDENT DEMAND
Applie Mathematics E-Notes, 5(005), 36-43 c ISSN 1607-510 Available free at mirror sites of http://www.math.nthu.eu.tw/ amen/ OPTIMAL CONTROL OF A PRODUCTION SYSTEM WITH INVENTORY-LEVEL-DEPENDENT DEMAND
More informationMATH , 06 Differential Equations Section 03: MWF 1:00pm-1:50pm McLaury 306 Section 06: MWF 3:00pm-3:50pm EEP 208
MATH 321-03, 06 Differential Equations Section 03: MWF 1:00pm-1:50pm McLaury 306 Section 06: MWF 3:00pm-3:50pm EEP 208 Instructor: Brent Deschamp Email: brent.eschamp@ssmt.eu Office: McLaury 316B Phone:
More informationLinear Regression with Limited Observation
Ela Hazan Tomer Koren Technion Israel Institute of Technology, Technion City 32000, Haifa, Israel ehazan@ie.technion.ac.il tomerk@cs.technion.ac.il Abstract We consier the most common variants of linear
More informationConcentration of Measure Inequalities for Compressive Toeplitz Matrices with Applications to Detection and System Identification
Concentration of Measure Inequalities for Compressive Toeplitz Matrices with Applications to Detection an System Ientification Borhan M Sananaji, Tyrone L Vincent, an Michael B Wakin Abstract In this paper,
More informationDuctility and Failure Modes of Single Reinforced Concrete Columns. Hiromichi Yoshikawa 1 and Toshiaki Miyagi 2
Ductility an Failure Moes of Single Reinforce Concrete Columns Hiromichi Yoshikawa 1 an Toshiaki Miyagi 2 Key Wors: seismic capacity esign, reinforce concrete column, failure moes, eformational uctility,
More informationA COMPARISON OF SMALL AREA AND CALIBRATION ESTIMATORS VIA SIMULATION
SAISICS IN RANSIION new series an SURVEY MEHODOLOGY 133 SAISICS IN RANSIION new series an SURVEY MEHODOLOGY Joint Issue: Small Area Estimation 014 Vol. 17, No. 1, pp. 133 154 A COMPARISON OF SMALL AREA
More informationThe Role of Models in Model-Assisted and Model- Dependent Estimation for Domains and Small Areas
The Role of Moels in Moel-Assiste an Moel- Depenent Estimation for Domains an Small Areas Risto Lehtonen University of Helsini Mio Myrsylä University of Pennsylvania Carl-Eri Särnal University of Montreal
More informationANALYSIS OF A GENERAL FAMILY OF REGULARIZED NAVIER-STOKES AND MHD MODELS
ANALYSIS OF A GENERAL FAMILY OF REGULARIZED NAVIER-STOKES AND MHD MODELS MICHAEL HOLST, EVELYN LUNASIN, AND GANTUMUR TSOGTGEREL ABSTRACT. We consier a general family of regularize Navier-Stokes an Magnetohyroynamics
More informationNew Statistical Test for Quality Control in High Dimension Data Set
International Journal of Applie Engineering Research ISSN 973-456 Volume, Number 6 (7) pp. 64-649 New Statistical Test for Quality Control in High Dimension Data Set Shamshuritawati Sharif, Suzilah Ismail
More informationMulti-View Clustering via Canonical Correlation Analysis
Keywors: multi-view learning, clustering, canonical correlation analysis Abstract Clustering ata in high-imensions is believe to be a har problem in general. A number of efficient clustering algorithms
More information3 The variational formulation of elliptic PDEs
Chapter 3 The variational formulation of elliptic PDEs We now begin the theoretical stuy of elliptic partial ifferential equations an bounary value problems. We will focus on one approach, which is calle
More informationIPA Derivatives for Make-to-Stock Production-Inventory Systems With Backorders Under the (R,r) Policy
IPA Derivatives for Make-to-Stock Prouction-Inventory Systems With Backorers Uner the (Rr) Policy Yihong Fan a Benamin Melame b Yao Zhao c Yorai Wari Abstract This paper aresses Infinitesimal Perturbation
More informationOptimization of Geometries by Energy Minimization
Optimization of Geometries by Energy Minimization by Tracy P. Hamilton Department of Chemistry University of Alabama at Birmingham Birmingham, AL 3594-140 hamilton@uab.eu Copyright Tracy P. Hamilton, 1997.
More informationSparse Reconstruction of Systems of Ordinary Differential Equations
Sparse Reconstruction of Systems of Orinary Differential Equations Manuel Mai a, Mark D. Shattuck b,c, Corey S. O Hern c,a,,e, a Department of Physics, Yale University, New Haven, Connecticut 06520, USA
More informationNecessary and Sufficient Conditions for Sketched Subspace Clustering
Necessary an Sufficient Conitions for Sketche Subspace Clustering Daniel Pimentel-Alarcón, Laura Balzano 2, Robert Nowak University of Wisconsin-Maison, 2 University of Michigan-Ann Arbor Abstract This
More informationInfluence of weight initialization on multilayer perceptron performance
Influence of weight initialization on multilayer perceptron performance M. Karouia (1,2) T. Denœux (1) R. Lengellé (1) (1) Université e Compiègne U.R.A. CNRS 817 Heuiasyc BP 649 - F-66 Compiègne ceex -
More informationMODELLING DEPENDENCE IN INSURANCE CLAIMS PROCESSES WITH LÉVY COPULAS ABSTRACT KEYWORDS
MODELLING DEPENDENCE IN INSURANCE CLAIMS PROCESSES WITH LÉVY COPULAS BY BENJAMIN AVANZI, LUKE C. CASSAR AND BERNARD WONG ABSTRACT In this paper we investigate the potential of Lévy copulas as a tool for
More informationLecture 6: Generalized multivariate analysis of variance
Lecture 6: Generalize multivariate analysis of variance Measuring association of the entire microbiome with other variables Distance matrices capture some aspects of the ata (e.g. microbiome composition,
More informationA Hybrid Approach for Modeling High Dimensional Medical Data
A Hybri Approach for Moeling High Dimensional Meical Data Alok Sharma 1, Gofrey C. Onwubolu 1 1 University of the South Pacific, Fii sharma_al@usp.ac.f, onwubolu_g@usp.ac.f Abstract. his work presents
More informationTHE EFFICIENCIES OF THE SPATIAL MEDIAN AND SPATIAL SIGN COVARIANCE MATRIX FOR ELLIPTICALLY SYMMETRIC DISTRIBUTIONS
THE EFFICIENCIES OF THE SPATIAL MEDIAN AND SPATIAL SIGN COVARIANCE MATRIX FOR ELLIPTICALLY SYMMETRIC DISTRIBUTIONS BY ANDREW F. MAGYAR A issertation submitte to the Grauate School New Brunswick Rutgers,
More informationResilient Modulus Prediction Model for Fine-Grained Soils in Ohio: Preliminary Study
Resilient Moulus Preiction Moel for Fine-Graine Soils in Ohio: Preliminary Stuy by Teruhisa Masaa: Associate Professor, Civil Engineering Department Ohio University, Athens, OH 4570 Tel: (740) 59-474 Fax:
More informationCONTROL CHARTS FOR VARIABLES
UNIT CONTOL CHATS FO VAIABLES Structure.1 Introuction Objectives. Control Chart Technique.3 Control Charts for Variables.4 Control Chart for Mean(-Chart).5 ange Chart (-Chart).6 Stanar Deviation Chart
More informationInter-domain Gaussian Processes for Sparse Inference using Inducing Features
Inter-omain Gaussian Processes for Sparse Inference using Inucing Features Miguel Lázaro-Greilla an Aníbal R. Figueiras-Vial Dep. Signal Processing & Communications Universia Carlos III e Mari, SPAIN {miguel,arfv}@tsc.uc3m.es
More informationLevel Construction of Decision Trees in a Partition-based Framework for Classification
Level Construction of Decision Trees in a Partition-base Framework for Classification Y.Y. Yao, Y. Zhao an J.T. Yao Department of Computer Science, University of Regina Regina, Saskatchewan, Canaa S4S
More information(2012) , ISBN
Cregan, V. an O'Brien, Stephen B.G. an McKee, Sean (2012) Asymptotics of a small liqui rop on a cone an plate rheometer. In: Progress in Inustrial Mathematics at ECMI 2010. Mathematics in Inustry: Progress
More informationEstimating International Migration on the Base of Small Area Techniques
MPRA Munich Personal RePEc Archive Estimating International Migration on the Base of Small Area echniques Vergil Voineagu an Nicoleta Caragea an Silvia Pisica 2013 Online at http://mpra.ub.uni-muenchen.e/48775/
More informationA. Exclusive KL View of the MLE
A. Exclusive KL View of the MLE Lets assume a change-of-variable moel p Z z on the ranom variable Z R m, such as the one use in Dinh et al. 2017: z 0 p 0 z 0 an z = ψz 0, where ψ is an invertible function
More informationFLUCTUATIONS IN THE NUMBER OF POINTS ON SMOOTH PLANE CURVES OVER FINITE FIELDS. 1. Introduction
FLUCTUATIONS IN THE NUMBER OF POINTS ON SMOOTH PLANE CURVES OVER FINITE FIELDS ALINA BUCUR, CHANTAL DAVID, BROOKE FEIGON, MATILDE LALÍN 1 Introuction In this note, we stuy the fluctuations in the number
More informationTutorial on Maximum Likelyhood Estimation: Parametric Density Estimation
Tutorial on Maximum Likelyhoo Estimation: Parametric Density Estimation Suhir B Kylasa 03/13/2014 1 Motivation Suppose one wishes to etermine just how biase an unfair coin is. Call the probability of tossing
More informationInfluence of Radiation on Product Yields in a Film Boiling Reactor
R&D NOTES Influence of Raiation on Prouct Yiels in a Film Boiling Reactor C. Thomas Aveisian, Wing Tsang, Terence Daviovits, an Jonah R. Allaben Sibley School of Mechanical an Aerospace Engineering, Cornell
More informationHeteroscedasticityinstochastic frontier models: A Monte Carlo Analysis
Heterosceasticityinstochastic frontier moels: A Monte Carlo Analysis by C. Guermat University of Exeter, Exeter EX4 4RJ, UK an K. Hari City University, Northampton Square, Lonon EC1V 0HB, UK First version:
More informationClosed and Open Loop Optimal Control of Buffer and Energy of a Wireless Device
Close an Open Loop Optimal Control of Buffer an Energy of a Wireless Device V. S. Borkar School of Technology an Computer Science TIFR, umbai, Inia. borkar@tifr.res.in A. A. Kherani B. J. Prabhu INRIA
More informationReal-time arrival prediction models for light rail train systems EDOUARD NAYE
DEGREE PROJECT IN TRANSPORT AND LOCATION ANALYSIS STOCKHOLM, SWEDEN 14 Real-time arrival preiction moels for light rail train systems EDOUARD NAYE KTH ROYAL INSTITUTE OF TECHNOLOGY SCHOOL OF ARCHITECTURE
More informationCHAPTER 1 : DIFFERENTIABLE MANIFOLDS. 1.1 The definition of a differentiable manifold
CHAPTER 1 : DIFFERENTIABLE MANIFOLDS 1.1 The efinition of a ifferentiable manifol Let M be a topological space. This means that we have a family Ω of open sets efine on M. These satisfy (1), M Ω (2) the
More informationRobustness and Perturbations of Minimal Bases
Robustness an Perturbations of Minimal Bases Paul Van Dooren an Froilán M Dopico December 9, 2016 Abstract Polynomial minimal bases of rational vector subspaces are a classical concept that plays an important
More informationStable and compact finite difference schemes
Center for Turbulence Research Annual Research Briefs 2006 2 Stable an compact finite ifference schemes By K. Mattsson, M. Svär AND M. Shoeybi. Motivation an objectives Compact secon erivatives have long
More informationModelling and simulation of dependence structures in nonlife insurance with Bernstein copulas
Moelling an simulation of epenence structures in nonlife insurance with Bernstein copulas Prof. Dr. Dietmar Pfeifer Dept. of Mathematics, University of Olenburg an AON Benfiel, Hamburg Dr. Doreen Straßburger
More informationEcon 582 Fixed Effects Estimation of Panel Data
Econ 582 Fixed Effects Estimation of Panel Data Eric Zivot May 28, 2012 Panel Data Framework = x 0 β + = 1 (individuals); =1 (time periods) y 1 = X β ( ) ( 1) + ε Main question: Is x uncorrelated with?
More informationNonparametric Additive Models
Nonparametric Aitive Moels Joel L. Horowitz The Institute for Fiscal Stuies Department of Economics, UCL cemmap working paper CWP20/2 Nonparametric Aitive Moels Joel L. Horowitz. INTRODUCTION Much applie
More informationExperimental Robustness Study of a Second-Order Sliding Mode Controller
Experimental Robustness Stuy of a Secon-Orer Sliing Moe Controller Anré Blom, Bram e Jager Einhoven University of Technology Department of Mechanical Engineering P.O. Box 513, 5600 MB Einhoven, The Netherlans
More informationShort Intro to Coordinate Transformation
Short Intro to Coorinate Transformation 1 A Vector A vector can basically be seen as an arrow in space pointing in a specific irection with a specific length. The following problem arises: How o we represent
More informationSituation awareness of power system based on static voltage security region
The 6th International Conference on Renewable Power Generation (RPG) 19 20 October 2017 Situation awareness of power system base on static voltage security region Fei Xiao, Zi-Qing Jiang, Qian Ai, Ran
More informationThe Use of the Durbin-Watson d Statistic in Rietveld Analysis
356 J. Appl. Cryst. (1987). 20, 356-361 The Use of the Durbin-Watson Statistic in Rietvel Analysis BY R. J. HILL Division of Mineral Chemistry, CSIRO, PO Box 124, Port Melbourne, Victoria 3207, Australia
More informationConnections Between Duality in Control Theory and
Connections Between Duality in Control heory an Convex Optimization V. Balakrishnan 1 an L. Vanenberghe 2 Abstract Several important problems in control theory can be reformulate as convex optimization
More informationEconometrics of Panel Data
Econometrics of Panel Data Jakub Mućk Meeting # 1 Jakub Mućk Econometrics of Panel Data Meeting # 1 1 / 31 Outline 1 Course outline 2 Panel data Advantages of Panel Data Limitations of Panel Data 3 Pooled
More informationarxiv: v4 [stat.ml] 21 Dec 2016
Online Active Linear Regression via Thresholing Carlos Riquelme Stanfor University riel@stanfor.eu Ramesh Johari Stanfor University rjohari@stanfor.eu Baosen Zhang University of Washington zhangbao@uw.eu
More informationDeveloping a Method for Increasing Accuracy and Precision in Measurement System Analysis: A Fuzzy Approach
Journal of Inustrial Engineering 6(00 5-3 Developing a Metho for Increasing Accuracy an Precision in Measurement System Analysis: A Fuzzy Approach Abolfazl Kazemi a, Hassan Haleh a, Vahi Hajipour b, Seye
More informationNearly finite Chacon Transformation
Nearly finite hacon Transformation Élise Janvresse, Emmanuel Roy, Thierry De La Rue To cite this version: Élise Janvresse, Emmanuel Roy, Thierry De La Rue Nearly finite hacon Transformation 2018
More informationinflow outflow Part I. Regular tasks for MAE598/494 Task 1
MAE 494/598, Fall 2016 Project #1 (Regular tasks = 20 points) Har copy of report is ue at the start of class on the ue ate. The rules on collaboration will be release separately. Please always follow the
More information