Web-based Supplementary Materials for. of the Null p-values
|
|
- Kory Chambers
- 5 years ago
- Views:
Transcription
1 Web-based Supplementary Materials for ocedures Controlling the -FDR Using Bivariate Distributions of the Null p-values Sanat K Sarar and Wenge Guo Temple University and National Institute of Environmental Health Sciences
2 Web Appendix A: oofs oof of Lemma 2 Let ˆP () ˆP (n0 ) be the ordered values of ˆP,, ˆP n0 Note that ˆP (j) P (n n0 +j) for all j n 0 Therefore, the original stepwise procedure rejects less number of null hypotheses, and hence has less number of falsely rejected null hypotheses, than the corresponding stepwise procedure where the p-values corresponding to the false null hypotheses are all very close to zero Thus, the lemma follows oof of Theorem 3 V -FDR E I (V ) E R r α i r n 0 i r r i r ˆPi α r, V, R r ( ) I ˆPi α r, V, R r P V, R r ˆPi α r (A) Now, for any r > and i n 0, V, R r ˆPi α r V, R r ˆPi α r V, R r ˆPi α r V, R r + ˆPi α r V, R r + ˆPi α r The inequality follows from the assumption (), since V, R r is a decreasing set for a 2
3 stepwise procedure Thus, we get -FDR α α α i α i α i V, R ˆPi α + i r+ i V, R r ˆPi α r V, R ˆPi α + V, R + ˆPi α V ˆP i α i V, ˆP i α (A2) Applying Lemma 2 to (A2), we get -FDR i P ˆRn0, ˆP i α ˆR n 0 Let denote the number of rejections in the corresponding stepwise procedure based on the ordered values ˆP () ˆP (n 0 ) of ˆP,, ˆP n0 \ ˆPi and the n 0 critical values α n n0 +2 α n Then, for any r n 0, we notice that, when our procedure is a stepup procedure ˆRn0 r, ˆP i α ˆP(r) α n n0 +r, ˆP (r+) > α n n0 +r+,, ˆP (n0 ) > α n, ˆPi α ˆP (r ) α n n 0 +r, ˆP (r) > α n n0 +r+,, ˆP (n 0 ) > α n, ˆPi α ˆR n 0 r, ˆP i α ; (A3) 3
4 whereas, for a stepdown procedure, we have ˆRn0 r, ˆP i α ˆP() α n n0 +,, ˆP (r) α n n0 +r, ˆP (r+) > α n n0 +r+, ˆPi α ˆP () α n n0 +2,, ˆP (r ) α n n 0 +r, ˆP (r) > α n n0 +r+, ˆPi α ˆR n 0 r, ˆP i α (A4) Thus, for a stepwise (stepup or stepdown) procedure, we get -FDR n 0 i r n 0 n 0 i j( i) r ˆR n 0 r, ˆP i α r ˆR n 0 r, ˆP i α, ˆP j α n n0 +r (A5) As explained in (A3) and (A4), ˆR n 0 r, ˆP i α, ˆP j α n n0 +r ˆR( i, j) n 0 2 r 2, ˆP i α, ˆP j α n n0 +r, for a stepup procedure, and ˆR n 0 r, ˆP i α, ˆP j α n n0 +r ˆR( i, j) n 0 2 r 2, ˆP i α, ˆP j α n n0 +r, ˆR ( i, j) for a stepdown procedure, where n 0 2 is the number of rejections in the corresponding stepwise procedure involving ˆP,, ˆP n0 \ ˆPi, ˆP j and critical values α n n0 +3 α n Thus, -FDR (n n 0 + )α 2 ( ) i j( i) r ˆR( i, j) n 0 2 r 2, ˆP i α ˆPj α n n0 +r 4
5 The inequality follows from the fact that n n 0 + r r n n 0 +, for all r n 0 Since for r >, ˆR( i, j) n 0 2 r 2, ˆP i α ˆP j α n n0 +r ˆR( i, j) n 0 2 r 2, ˆP i α ˆP j α n n0 +r ˆP i α ˆP j α n n0 +r ˆR( i, j) n 0 2 r 2, ˆP i α ˆP j α n n0 +r, ˆP i α ˆPj α n n0 +r ˆR( i, j) n 0 2 r, ˆR( i, j) n 0 2 r, ( i, j) with the inequality following due to the property () and the fact that ˆR n 0 2 r 2, ˆP i α is decreasing, we have r ˆR( i, j) n 0 2 r 2, ˆP i α ˆPj α n n0 +r ˆR( i, j) n 0 2 2, ˆP i α ˆPj α n n0 + r+ [ ˆR( i, j) n r 2, ˆP i α ˆP j α n n0 +r ˆR( i, j) n 0 2 r, ˆP ] i α ˆPj α n n0 +r ˆR( i, j) n 0 2 2, ˆP i α ˆPj α n n0 + + ˆR( i, j) n 0 2, ˆP i α ˆPj α n n0 + ˆR( i, j) n 0 2 2, ˆP i α ˆPj α n n0 + 5
6 Therefore, -FDR (n n 0 + )α 2 ( ) ˆP j α n n0 + (n n 0 + )α 2 ( ) ( ) i j( i) i j( i) i j( i) ˆR( i, j) n 0 2 2, ˆP i α ˆPi α ˆPj α n n0 + ˆPi α, ˆP j α n n0 + (A6) The theorem then follows by considering the maximum of the right-hand side in (A6) over the set of values of n 0 oof of (8) Since the p-values are independent and α i i β/n, i,, n, we see from the first line in (A5) that -FDR n 0β n r ˆRn0 r n 0β n ˆRn0, the desired inequality oof of Theorem 5 Define p ijr P ˆPi [α j, α j ], V, R r, given a set critical 6
7 values 0 α 0 < α < < α n Then, from (A) we have, -FDR r i n 0 r ˆPi α r, V, R r i j r j i j n 0 i j r p ijr i j j r j j ˆPi [α j, α j ], V α j α j j r r i j p ijr r p ijr Let α i (i )α /, for some fixed 0 < α < Then, -FDR n 0 α + j+ j (A7) The theorem then follows by considering α α/n + n j+ j in (A7) 7
8 Web Figure : rho 0 Average Power !FDR BH!FDR SD!FDR SD The number of false null hypotheses Figure : Power of two -FDR stepdown procedures in the case of independence with parameters n 200, 5, d 2 and α 005 8
PROCEDURES CONTROLLING THE k-fdr USING. BIVARIATE DISTRIBUTIONS OF THE NULL p-values. Sanat K. Sarkar and Wenge Guo
PROCEDURES CONTROLLING THE k-fdr USING BIVARIATE DISTRIBUTIONS OF THE NULL p-values Sanat K. Sarkar and Wenge Guo Temple University and National Institute of Environmental Health Sciences Abstract: Procedures
More informationProcedures controlling generalized false discovery rate
rocedures controlling generalized false discovery rate By SANAT K. SARKAR Department of Statistics, Temple University, hiladelphia, A 922, U.S.A. sanat@temple.edu AND WENGE GUO Department of Environmental
More informationOn adaptive procedures controlling the familywise error rate
, pp. 3 On adaptive procedures controlling the familywise error rate By SANAT K. SARKAR Temple University, Philadelphia, PA 922, USA sanat@temple.edu Summary This paper considers the problem of developing
More informationSTEPDOWN PROCEDURES CONTROLLING A GENERALIZED FALSE DISCOVERY RATE. National Institute of Environmental Health Sciences and Temple University
STEPDOWN PROCEDURES CONTROLLING A GENERALIZED FALSE DISCOVERY RATE Wenge Guo 1 and Sanat K. Sarkar 2 National Institute of Environmental Health Sciences and Temple University Abstract: Often in practice
More informationChapter 1. Stepdown Procedures Controlling A Generalized False Discovery Rate
Chapter Stepdown Procedures Controlling A Generalized False Discovery Rate Wenge Guo and Sanat K. Sarkar Biostatistics Branch, National Institute of Environmental Health Sciences, Research Triangle Park,
More informationOn Methods Controlling the False Discovery Rate 1
Sankhyā : The Indian Journal of Statistics 2008, Volume 70-A, Part 2, pp. 135-168 c 2008, Indian Statistical Institute On Methods Controlling the False Discovery Rate 1 Sanat K. Sarkar Temple University,
More informationTwo-stage stepup procedures controlling FDR
Journal of Statistical Planning and Inference 38 (2008) 072 084 www.elsevier.com/locate/jspi Two-stage stepup procedures controlling FDR Sanat K. Sarar Department of Statistics, Temple University, Philadelphia,
More informationOn Procedures Controlling the FDR for Testing Hierarchically Ordered Hypotheses
On Procedures Controlling the FDR for Testing Hierarchically Ordered Hypotheses Gavin Lynch Catchpoint Systems, Inc., 228 Park Ave S 28080 New York, NY 10003, U.S.A. Wenge Guo Department of Mathematical
More informationSanat Sarkar Department of Statistics, Temple University Philadelphia, PA 19122, U.S.A. September 11, Abstract
Adaptive Controls of FWER and FDR Under Block Dependence arxiv:1611.03155v1 [stat.me] 10 Nov 2016 Wenge Guo Department of Mathematical Sciences New Jersey Institute of Technology Newark, NJ 07102, U.S.A.
More informationON STEPWISE CONTROL OF THE GENERALIZED FAMILYWISE ERROR RATE. By Wenge Guo and M. Bhaskara Rao
ON STEPWISE CONTROL OF THE GENERALIZED FAMILYWISE ERROR RATE By Wenge Guo and M. Bhaskara Rao National Institute of Environmental Health Sciences and University of Cincinnati A classical approach for dealing
More informationResampling-Based Control of the FDR
Resampling-Based Control of the FDR Joseph P. Romano 1 Azeem S. Shaikh 2 and Michael Wolf 3 1 Departments of Economics and Statistics Stanford University 2 Department of Economics University of Chicago
More informationApplying the Benjamini Hochberg procedure to a set of generalized p-values
U.U.D.M. Report 20:22 Applying the Benjamini Hochberg procedure to a set of generalized p-values Fredrik Jonsson Department of Mathematics Uppsala University Applying the Benjamini Hochberg procedure
More informationWeb-based Supplementary Materials for. Controlling False Discoveries in Multidimensional Directional Decisions, with
Web-based Supplementay Mateials fo Contolling False Discoveies in Multidimensional Diectional Decisions, with Applications to Gene Expession Data on Odeed Categoies Wenge Guo Biostatistics Banch, National
More informationFDR-CONTROLLING STEPWISE PROCEDURES AND THEIR FALSE NEGATIVES RATES
FDR-CONTROLLING STEPWISE PROCEDURES AND THEIR FALSE NEGATIVES RATES Sanat K. Sarkar a a Department of Statistics, Temple University, Speakman Hall (006-00), Philadelphia, PA 19122, USA Abstract The concept
More informationarxiv: v1 [math.st] 13 Mar 2008
The Annals of Statistics 2008, Vol. 36, No. 1, 337 363 DOI: 10.1214/009053607000000550 c Institute of Mathematical Statistics, 2008 arxiv:0803.1961v1 [math.st] 13 Mar 2008 GENERALIZING SIMES TEST AND HOCHBERG
More informationFamilywise Error Rate Controlling Procedures for Discrete Data
Familywise Error Rate Controlling Procedures for Discrete Data arxiv:1711.08147v1 [stat.me] 22 Nov 2017 Yalin Zhu Center for Mathematical Sciences, Merck & Co., Inc., West Point, PA, U.S.A. Wenge Guo Department
More informationGENERALIZING SIMES TEST AND HOCHBERG S STEPUP PROCEDURE 1. BY SANAT K. SARKAR Temple University
The Annals of Statistics 2008, Vol. 36, No. 1, 337 363 DOI: 10.1214/009053607000000550 Institute of Mathematical Statistics, 2008 GENERALIZING SIMES TEST AND HOCHBERG S STEPUP PROCEDURE 1 BY SANAT K. SARKAR
More informationComments on: Control of the false discovery rate under dependence using the bootstrap and subsampling
Test (2008) 17: 443 445 DOI 10.1007/s11749-008-0127-5 DISCUSSION Comments on: Control of the false discovery rate under dependence using the bootstrap and subsampling José A. Ferreira Mark A. van de Wiel
More informationA GENERAL DECISION THEORETIC FORMULATION OF PROCEDURES CONTROLLING FDR AND FNR FROM A BAYESIAN PERSPECTIVE
A GENERAL DECISION THEORETIC FORMULATION OF PROCEDURES CONTROLLING FDR AND FNR FROM A BAYESIAN PERSPECTIVE Sanat K. Sarkar 1, Tianhui Zhou and Debashis Ghosh Temple University, Wyeth Pharmaceuticals and
More informationControlling the False Discovery Rate in Two-Stage. Combination Tests for Multiple Endpoints
Controlling the False Discovery Rate in Two-Stage Combination Tests for Multiple ndpoints Sanat K. Sarkar, Jingjing Chen and Wenge Guo May 29, 2011 Sanat K. Sarkar is Professor and Senior Research Fellow,
More informationMultiple Testing. Hoang Tran. Department of Statistics, Florida State University
Multiple Testing Hoang Tran Department of Statistics, Florida State University Large-Scale Testing Examples: Microarray data: testing differences in gene expression between two traits/conditions Microbiome
More informationarxiv: v1 [math.st] 31 Mar 2009
The Annals of Statistics 2009, Vol. 37, No. 2, 619 629 DOI: 10.1214/07-AOS586 c Institute of Mathematical Statistics, 2009 arxiv:0903.5373v1 [math.st] 31 Mar 2009 AN ADAPTIVE STEP-DOWN PROCEDURE WITH PROVEN
More informationCHAPTER 8. Test Procedures is a rule, based on sample data, for deciding whether to reject H 0 and contains:
CHAPTER 8 Test of Hypotheses Based on a Single Sample Hypothesis testing is the method that decide which of two contradictory claims about the parameter is correct. Here the parameters of interest are
More informationSTEPUP PROCEDURES FOR CONTROL OF GENERALIZATIONS OF THE FAMILYWISE ERROR RATE
AOS imspdf v.2006/05/02 Prn:4/08/2006; 11:19 F:aos0169.tex; (Lina) p. 1 The Annals of Statistics 2006, Vol. 0, No. 00, 1 26 DOI: 10.1214/009053606000000461 Institute of Mathematical Statistics, 2006 STEPUP
More informationModified Simes Critical Values Under Positive Dependence
Modified Simes Critical Values Under Positive Dependence Gengqian Cai, Sanat K. Sarkar Clinical Pharmacology Statistics & Programming, BDS, GlaxoSmithKline Statistics Department, Temple University, Philadelphia
More information7.2 One-Sample Correlation ( = a) Introduction. Correlation analysis measures the strength and direction of association between
7.2 One-Sample Correlation ( = a) Introduction Correlation analysis measures the strength and direction of association between variables. In this chapter we will test whether the population correlation
More informationON TWO RESULTS IN MULTIPLE TESTING
ON TWO RESULTS IN MULTIPLE TESTING By Sanat K. Sarkar 1, Pranab K. Sen and Helmut Finner Temple University, University of North Carolina at Chapel Hill and University of Duesseldorf Two known results in
More informationA NEW APPROACH FOR LARGE SCALE MULTIPLE TESTING WITH APPLICATION TO FDR CONTROL FOR GRAPHICALLY STRUCTURED HYPOTHESES
A NEW APPROACH FOR LARGE SCALE MULTIPLE TESTING WITH APPLICATION TO FDR CONTROL FOR GRAPHICALLY STRUCTURED HYPOTHESES By Wenge Guo Gavin Lynch Joseph P. Romano Technical Report No. 2018-06 September 2018
More informationRejoinder on: Control of the false discovery rate under dependence using the bootstrap and subsampling
Test (2008) 17: 461 471 DOI 10.1007/s11749-008-0134-6 DISCUSSION Rejoinder on: Control of the false discovery rate under dependence using the bootstrap and subsampling Joseph P. Romano Azeem M. Shaikh
More informationPartitioning the Parameter Space. Topic 18 Composite Hypotheses
Topic 18 Composite Hypotheses Partitioning the Parameter Space 1 / 10 Outline Partitioning the Parameter Space 2 / 10 Partitioning the Parameter Space Simple hypotheses limit us to a decision between one
More informationCHAPTER 9, 10. Similar to a courtroom trial. In trying a person for a crime, the jury needs to decide between one of two possibilities:
CHAPTER 9, 10 Hypothesis Testing Similar to a courtroom trial. In trying a person for a crime, the jury needs to decide between one of two possibilities: The person is guilty. The person is innocent. To
More information10.4 Hypothesis Testing: Two Independent Samples Proportion
10.4 Hypothesis Testing: Two Independent Samples Proportion Example 3: Smoking cigarettes has been known to cause cancer and other ailments. One politician believes that a higher tax should be imposed
More informationFALSE DISCOVERY AND FALSE NONDISCOVERY RATES IN SINGLE-STEP MULTIPLE TESTING PROCEDURES 1. BY SANAT K. SARKAR Temple University
The Annals of Statistics 2006, Vol. 34, No. 1, 394 415 DOI: 10.1214/009053605000000778 Institute of Mathematical Statistics, 2006 FALSE DISCOVERY AND FALSE NONDISCOVERY RATES IN SINGLE-STEP MULTIPLE TESTING
More informationIMPROVING TWO RESULTS IN MULTIPLE TESTING
IMPROVING TWO RESULTS IN MULTIPLE TESTING By Sanat K. Sarkar 1, Pranab K. Sen and Helmut Finner Temple University, University of North Carolina at Chapel Hill and University of Duesseldorf October 11,
More informationLooking at the Other Side of Bonferroni
Department of Biostatistics University of Washington 24 May 2012 Multiple Testing: Control the Type I Error Rate When analyzing genetic data, one will commonly perform over 1 million (and growing) hypothesis
More informationTopic 15: Simple Hypotheses
Topic 15: November 10, 2009 In the simplest set-up for a statistical hypothesis, we consider two values θ 0, θ 1 in the parameter space. We write the test as H 0 : θ = θ 0 versus H 1 : θ = θ 1. H 0 is
More informationStep-down FDR Procedures for Large Numbers of Hypotheses
Step-down FDR Procedures for Large Numbers of Hypotheses Paul N. Somerville University of Central Florida Abstract. Somerville (2004b) developed FDR step-down procedures which were particularly appropriate
More informationClosure properties of classes of multiple testing procedures
AStA Adv Stat Anal (2018) 102:167 178 https://doi.org/10.1007/s10182-017-0297-0 ORIGINAL PAPER Closure properties of classes of multiple testing procedures Georg Hahn 1 Received: 28 June 2016 / Accepted:
More informationControlling Bayes Directional False Discovery Rate in Random Effects Model 1
Controlling Bayes Directional False Discovery Rate in Random Effects Model 1 Sanat K. Sarkar a, Tianhui Zhou b a Temple University, Philadelphia, PA 19122, USA b Wyeth Pharmaceuticals, Collegeville, PA
More informationSTA 437: Applied Multivariate Statistics
Al Nosedal. University of Toronto. Winter 2015 1 Chapter 5. Tests on One or Two Mean Vectors If you can t explain it simply, you don t understand it well enough Albert Einstein. Definition Chapter 5. Tests
More informationSolution: First note that the power function of the test is given as follows,
Problem 4.5.8: Assume the life of a tire given by X is distributed N(θ, 5000 ) Past experience indicates that θ = 30000. The manufacturere claims the tires made by a new process have mean θ > 30000. Is
More informationare equal to zero, where, q = p 1. For each gene j, the pairwise null and alternative hypotheses are,
Page of 8 Suppleentary Materials: A ultiple testing procedure for ulti-diensional pairwise coparisons with application to gene expression studies Anjana Grandhi, Wenge Guo, Shyaal D. Peddada S Notations
More informationControl of Directional Errors in Fixed Sequence Multiple Testing
Control of Directional Errors in Fixed Sequence Multiple Testing Anjana Grandhi Department of Mathematical Sciences New Jersey Institute of Technology Newark, NJ 07102-1982 Wenge Guo Department of Mathematical
More informationHypothesis testing: theory and methods
Statistical Methods Warsaw School of Economics November 3, 2017 Statistical hypothesis is the name of any conjecture about unknown parameters of a population distribution. The hypothesis should be verifiable
More informationBusiness Statistics: Lecture 8: Introduction to Estimation & Hypothesis Testing
Business Statistics: Lecture 8: Introduction to Estimation & Hypothesis Testing Agenda Introduction to Estimation Point estimation Interval estimation Introduction to Hypothesis Testing Concepts en terminology
More informationStatistica Sinica Preprint No: SS R1
Statistica Sinica Preprint No: SS-2017-0072.R1 Title Control of Directional Errors in Fixed Sequence Multiple Testing Manuscript ID SS-2017-0072.R1 URL http://www.stat.sinica.edu.tw/statistica/ DOI 10.5705/ss.202017.0072
More informationThe Purpose of Hypothesis Testing
Section 8 1A:! An Introduction to Hypothesis Testing The Purpose of Hypothesis Testing See s Candy states that a box of it s candy weighs 16 oz. They do not mean that every single box weights exactly 16
More informationPerformance Evaluation and Comparison
Outline Hong Chang Institute of Computing Technology, Chinese Academy of Sciences Machine Learning Methods (Fall 2012) Outline Outline I 1 Introduction 2 Cross Validation and Resampling 3 Interval Estimation
More informationThe Pennsylvania State University The Graduate School Eberly College of Science GENERALIZED STEPWISE PROCEDURES FOR
The Pennsylvania State University The Graduate School Eberly College of Science GENERALIZED STEPWISE PROCEDURES FOR CONTROLLING THE FALSE DISCOVERY RATE A Dissertation in Statistics by Scott Roths c 2011
More informationSummary of Chapters 7-9
Summary of Chapters 7-9 Chapter 7. Interval Estimation 7.2. Confidence Intervals for Difference of Two Means Let X 1,, X n and Y 1, Y 2,, Y m be two independent random samples of sizes n and m from two
More informationDepartment of Statistics University of Central Florida. Technical Report TR APR2007 Revised 25NOV2007
Department of Statistics University of Central Florida Technical Report TR-2007-01 25APR2007 Revised 25NOV2007 Controlling the Number of False Positives Using the Benjamini- Hochberg FDR Procedure Paul
More informationCIVL /8904 T R A F F I C F L O W T H E O R Y L E C T U R E - 8
CIVL - 7904/8904 T R A F F I C F L O W T H E O R Y L E C T U R E - 8 Chi-square Test How to determine the interval from a continuous distribution I = Range 1 + 3.322(logN) I-> Range of the class interval
More informationReview. December 4 th, Review
December 4 th, 2017 Att. Final exam: Course evaluation Friday, 12/14/2018, 10:30am 12:30pm Gore Hall 115 Overview Week 2 Week 4 Week 7 Week 10 Week 12 Chapter 6: Statistics and Sampling Distributions Chapter
More informationHypothesis for Means and Proportions
November 14, 2012 Hypothesis Tests - Basic Ideas Often we are interested not in estimating an unknown parameter but in testing some claim or hypothesis concerning a population. For example we may wish
More informationExact and Approximate Stepdown Methods For Multiple Hypothesis Testing
Exact and Approximate Stepdown Methods For Multiple Hypothesis Testing Joseph P. Romano Department of Statistics Stanford University Michael Wolf Department of Economics and Business Universitat Pompeu
More informationStatistical Applications in Genetics and Molecular Biology
Statistical Applications in Genetics and Molecular Biology Volume 3, Issue 1 2004 Article 14 Multiple Testing. Part II. Step-Down Procedures for Control of the Family-Wise Error Rate Mark J. van der Laan
More informationSample Size and Power I: Binary Outcomes. James Ware, PhD Harvard School of Public Health Boston, MA
Sample Size and Power I: Binary Outcomes James Ware, PhD Harvard School of Public Health Boston, MA Sample Size and Power Principles: Sample size calculations are an essential part of study design Consider
More informationSpearman Rho Correlation
Spearman Rho Correlation Learning Objectives After studying this Chapter, you should be able to: know when to use Spearman rho, Calculate Spearman rho coefficient, Interpret the correlation coefficient,
More informationhypothesis a claim about the value of some parameter (like p)
Testing hypotheses hypothesis a claim about the value of some parameter (like p) significance test procedure to assess the strength of evidence provided by a sample of data against the claim of a hypothesized
More informationOn Generalized Fixed Sequence Procedures for Controlling the FWER
Research Article Received XXXX (www.interscience.wiley.com) DOI: 10.1002/sim.0000 On Generalized Fixed Sequence Procedures for Controlling the FWER Zhiying Qiu, a Wenge Guo b and Gavin Lynch c Testing
More informationMultiple hypothesis testing using the excess discovery count and alpha-investing rules
Multiple hypothesis testing using the excess discovery count and alpha-investing rules Dean P. Foster and Robert A. Stine Department of Statistics The Wharton School of the University of Pennsylvania Philadelphia,
More informationChapter 7. Hypothesis Testing
Chapter 7. Hypothesis Testing Joonpyo Kim June 24, 2017 Joonpyo Kim Ch7 June 24, 2017 1 / 63 Basic Concepts of Testing Suppose that our interest centers on a random variable X which has density function
More informationPSY 307 Statistics for the Behavioral Sciences. Chapter 20 Tests for Ranked Data, Choosing Statistical Tests
PSY 307 Statistics for the Behavioral Sciences Chapter 20 Tests for Ranked Data, Choosing Statistical Tests What To Do with Non-normal Distributions Tranformations (pg 382): The shape of the distribution
More informationApplied Statistics for the Behavioral Sciences
Applied Statistics for the Behavioral Sciences Chapter 8 One-sample designs Hypothesis testing/effect size Chapter Outline Hypothesis testing null & alternative hypotheses alpha ( ), significance level,
More informationControl of Generalized Error Rates in Multiple Testing
Institute for Empirical Research in Economics University of Zurich Working Paper Series ISSN 1424-0459 Working Paper No. 245 Control of Generalized Error Rates in Multiple Testing Joseph P. Romano and
More informationControl of the False Discovery Rate under Dependence using the Bootstrap and Subsampling
Institute for Empirical Research in Economics University of Zurich Working Paper Series ISSN 1424-0459 Working Paper No. 337 Control of the False Discovery Rate under Dependence using the Bootstrap and
More informationChristopher J. Bennett
P- VALUE ADJUSTMENTS FOR ASYMPTOTIC CONTROL OF THE GENERALIZED FAMILYWISE ERROR RATE by Christopher J. Bennett Working Paper No. 09-W05 April 2009 DEPARTMENT OF ECONOMICS VANDERBILT UNIVERSITY NASHVILLE,
More informationInferences About Two Population Proportions
Inferences About Two Population Proportions MATH 130, Elements of Statistics I J. Robert Buchanan Department of Mathematics Fall 2018 Background Recall: for a single population the sampling proportion
More informationThis paper has been submitted for consideration for publication in Biometrics
BIOMETRICS, 1 10 Supplementary material for Control with Pseudo-Gatekeeping Based on a Possibly Data Driven er of the Hypotheses A. Farcomeni Department of Public Health and Infectious Diseases Sapienza
More informationMTMS Mathematical Statistics
MTMS.01.099 Mathematical Statistics Lecture 12. Hypothesis testing. Power function. Approximation of Normal distribution and application to Binomial distribution Tõnu Kollo Fall 2016 Hypothesis Testing
More informationChapter Eight: Assessment of Relationships 1/42
Chapter Eight: Assessment of Relationships 1/42 8.1 Introduction 2/42 Background This chapter deals, primarily, with two topics. The Pearson product-moment correlation coefficient. The chi-square test
More informationLecture 28. Ingo Ruczinski. December 3, Department of Biostatistics Johns Hopkins Bloomberg School of Public Health Johns Hopkins University
Lecture 28 Department of Biostatistics Johns Hopkins Bloomberg School of Public Health Johns Hopkins University December 3, 2015 1 2 3 4 5 1 Familywise error rates 2 procedure 3 Performance of with multiple
More informationFamily-wise Error Rate Control in QTL Mapping and Gene Ontology Graphs
Family-wise Error Rate Control in QTL Mapping and Gene Ontology Graphs with Remarks on Family Selection Dissertation Defense April 5, 204 Contents Dissertation Defense Introduction 2 FWER Control within
More informationSome General Types of Tests
Some General Types of Tests We may not be able to find a UMP or UMPU test in a given situation. In that case, we may use test of some general class of tests that often have good asymptotic properties.
More informationarxiv:math/ v1 [math.st] 29 Dec 2006 Jianqing Fan Peter Hall Qiwei Yao
TO HOW MANY SIMULTANEOUS HYPOTHESIS TESTS CAN NORMAL, STUDENT S t OR BOOTSTRAP CALIBRATION BE APPLIED? arxiv:math/0701003v1 [math.st] 29 Dec 2006 Jianqing Fan Peter Hall Qiwei Yao ABSTRACT. In the analysis
More informationA BAYESIAN STEPWISE MULTIPLE TESTING PROCEDURE. By Sanat K. Sarkar 1 and Jie Chen. Temple University and Merck Research Laboratories
A BAYESIAN STEPWISE MULTIPLE TESTING PROCEDURE By Sanat K. Sarar 1 and Jie Chen Temple University and Merc Research Laboratories Abstract Bayesian testing of multiple hypotheses often requires consideration
More informationHypothesis Testing. Hypothesis: conjecture, proposition or statement based on published literature, data, or a theory that may or may not be true
Hypothesis esting Hypothesis: conjecture, proposition or statement based on published literature, data, or a theory that may or may not be true Statistical Hypothesis: conjecture about a population parameter
More informationLecture 3. Inference about multivariate normal distribution
Lecture 3. Inference about multivariate normal distribution 3.1 Point and Interval Estimation Let X 1,..., X n be i.i.d. N p (µ, Σ). We are interested in evaluation of the maximum likelihood estimates
More informationSummary: the confidence interval for the mean (σ 2 known) with gaussian assumption
Summary: the confidence interval for the mean (σ known) with gaussian assumption on X Let X be a Gaussian r.v. with mean µ and variance σ. If X 1, X,..., X n is a random sample drawn from X then the confidence
More informationTesting Statistical Hypotheses
E.L. Lehmann Joseph P. Romano Testing Statistical Hypotheses Third Edition 4y Springer Preface vii I Small-Sample Theory 1 1 The General Decision Problem 3 1.1 Statistical Inference and Statistical Decisions
More informationBonferroni - based gatekeeping procedure with retesting option
Bonferroni - based gatekeeping procedure with retesting option Zhiying Qiu Biostatistics and Programming, Sanofi Bridgewater, NJ 08807, U.S.A. Wenge Guo Department of Mathematical Sciences New Jersey Institute
More informationStatistical Inference
Statistical Inference Classical and Bayesian Methods Class 6 AMS-UCSC Thu 26, 2012 Winter 2012. Session 1 (Class 6) AMS-132/206 Thu 26, 2012 1 / 15 Topics Topics We will talk about... 1 Hypothesis testing
More informationAssociation Between Variables Measured at the Interval-Ratio Level: Bivariate Correlation and Regression
Association Between Variables Measured at the Interval-Ratio Level: Bivariate Correlation and Regression Last couple of classes: Measures of Association: Phi, Cramer s V and Lambda (nominal level of measurement)
More informationSoc3811 Second Midterm Exam
Soc38 Second Midterm Exam SEMI-OPE OTE: One sheet of paper, signed & turned in with exam booklet Bring our Own Pencil with Eraser and a Hand Calculator! Standardized Scores & Probability If we know the
More informationi=1 X i/n i=1 (X i X) 2 /(n 1). Find the constant c so that the statistic c(x X n+1 )/S has a t-distribution. If n = 8, determine k such that
Math 47 Homework Assignment 4 Problem 411 Let X 1, X,, X n, X n+1 be a random sample of size n + 1, n > 1, from a distribution that is N(µ, σ ) Let X = n i=1 X i/n and S = n i=1 (X i X) /(n 1) Find the
More informationMultiple Testing. Anjana Grandhi. BARDS, Merck Research Laboratories. Rahway, NJ Wenge Guo. Department of Mathematical Sciences
Control of Directional Errors in Fixed Sequence arxiv:1602.02345v2 [math.st] 18 Mar 2017 Multiple Testing Anjana Grandhi BARDS, Merck Research Laboratories Rahway, NJ 07065 Wenge Guo Department of Mathematical
More informationStat 231 Exam 2 Fall 2013
Stat 231 Exam 2 Fall 2013 I have neither given nor received unauthorized assistance on this exam. Name Signed Date Name Printed 1 1. Some IE 361 students worked with a manufacturer on quantifying the capability
More informationClass 24. Daniel B. Rowe, Ph.D. Department of Mathematics, Statistics, and Computer Science. Marquette University MATH 1700
Class 4 Daniel B. Rowe, Ph.D. Department of Mathematics, Statistics, and Computer Science Copyright 013 by D.B. Rowe 1 Agenda: Recap Chapter 9. and 9.3 Lecture Chapter 10.1-10.3 Review Exam 6 Problem Solving
More informationCorrelation Examining the relationship between interval-ratio variables
Correlation Examining the relationship between interval-ratio variables Young Americans Stress Hours of Exercise Y = 10.0-2.5 X + e Older Americans Stress Hours of Exercise Y = 10.0-2.5 X + e PEARSON CORRELATION
More informationChapte The McGraw-Hill Companies, Inc. All rights reserved.
12er12 Chapte Bivariate i Regression (Part 1) Bivariate Regression Visual Displays Begin the analysis of bivariate data (i.e., two variables) with a scatter plot. A scatter plot - displays each observed
More informationVisual interpretation with normal approximation
Visual interpretation with normal approximation H 0 is true: H 1 is true: p =0.06 25 33 Reject H 0 α =0.05 (Type I error rate) Fail to reject H 0 β =0.6468 (Type II error rate) 30 Accept H 1 Visual interpretation
More informationChapter Six: Two Independent Samples Methods 1/51
Chapter Six: Two Independent Samples Methods 1/51 6.3 Methods Related To Differences Between Proportions 2/51 Test For A Difference Between Proportions:Introduction Suppose a sampling distribution were
More informationTables Table A Table B Table C Table D Table E 675
BMTables.indd Page 675 11/15/11 4:25:16 PM user-s163 Tables Table A Standard Normal Probabilities Table B Random Digits Table C t Distribution Critical Values Table D Chi-square Distribution Critical Values
More informationStatistical Applications in Genetics and Molecular Biology
Statistical Applications in Genetics and Molecular Biology Volume 5, Issue 1 2006 Article 28 A Two-Step Multiple Comparison Procedure for a Large Number of Tests and Multiple Treatments Hongmei Jiang Rebecca
More informationDifference in two or more average scores in different groups
ANOVAs Analysis of Variance (ANOVA) Difference in two or more average scores in different groups Each participant tested once Same outcome tested in each group Simplest is one-way ANOVA (one variable as
More informationBIO5312 Biostatistics Lecture 6: Statistical hypothesis testings
BIO5312 Biostatistics Lecture 6: Statistical hypothesis testings Yujin Chung October 4th, 2016 Fall 2016 Yujin Chung Lec6: Statistical hypothesis testings Fall 2016 1/30 Previous Two types of statistical
More informationLec 1: An Introduction to ANOVA
Ying Li Stockholm University October 31, 2011 Three end-aisle displays Which is the best? Design of the Experiment Identify the stores of the similar size and type. The displays are randomly assigned to
More informationLecture Testing Hypotheses: The Neyman-Pearson Paradigm
Math 408 - Mathematical Statistics Lecture 29-30. Testing Hypotheses: The Neyman-Pearson Paradigm April 12-15, 2013 Konstantin Zuev (USC) Math 408, Lecture 29-30 April 12-15, 2013 1 / 12 Agenda Example:
More informationMultiple testing: Intro & FWER 1
Multiple testing: Intro & FWER 1 Mark van de Wiel mark.vdwiel@vumc.nl Dep of Epidemiology & Biostatistics,VUmc, Amsterdam Dep of Mathematics, VU 1 Some slides courtesy of Jelle Goeman 1 Practical notes
More informationTests about a population mean
October 2 nd, 2017 Overview Week 1 Week 2 Week 4 Week 7 Week 10 Week 12 Chapter 1: Descriptive statistics Chapter 6: Statistics and Sampling Distributions Chapter 7: Point Estimation Chapter 8: Confidence
More information