Introduction to Robust Estimation and Hypothesis Testing (4th Edition)


Download Introduction to Robust Estimation and Hypothesis Testing (4th Edition) written by Rand R. Wilcox in PDF format. This book is under the category Computers and bearing the isbn/isbn13 number 012804733X; 012804781X/9780128047330/ 9780128047811. You may reffer the table below for additional details of the book.


Introduction to Robust Estimating and Hypothesis Testing; 4th Edition; (PDF) is a ‘how-to’ on the usage of sturdy strategies utilizing accessible software program. Modern sturdy strategies supply improved strategies for coping with outliers; skewed distribution curvature; and heteroscedasticity that may give substantial good points in energy as well as to a deeper; extra correct; and extra nuanced understanding of information. Since the earlier edition; there have been a number of advances and enhancements. They include new strategies for evaluating teams and measuring impact measurement together with new strategies for evaluating quantiles. Many new regression strategies have been included that comprise each parametric and nonparametric strategies. The strategies associated to ANCOVA have been expanded significantly. New views related to discrete distributions with a relatively small pattern area are described in addition to new outcomes related to the shift operate. The sensible significance of those strategies is proven utilizing knowledge from actual-world research. The R bundle written for this ebook now incorporates greater than 1200 capabilities.

New to this 4e edition:

  • 35% revised content material
  • Features latest rank-primarily based strategies
  • Includes newest enhancements in ANOVA
  • Describes and illustrated simple to use software program
  • Includes many new and improved R capabilities
  • New strategies that take care of an in depth vary of conditions
  • Extensive revisions to include the newest developments in sturdy regression

NOTE: The product solely consists of the ebook Introduction to Robust Estimating and Hypothesis Testing; 4th Editon;  in PDF. No access codes are included.


Additional information


Rand R. Wilcox


Academic Press; 4th edition










012804733X; 012804781X


9780128047330/ 9780128047811

Table of contents

Table of contents :
1 Introduction
1.1 Problems with Assuming Normality
1.2 Transformations
1.3 The Influence Curve
1.4 The Central Limit Theorem
1.5 Is the ANOVA F Robust?
1.6 Regression
1.7 More Remarks
1.8 R Software
1.9 Some Data Management Issues
1.9.1 Eliminating Missing Values
1.10 Data Sets
2 A Foundation for Robust Methods
2.1 Basic Tools for Judging Robustness
2.1.1 Qualitative Robustness
2.1.2 Infinitesimal Robustness
2.1.3 Quantitative Robustness
2.2 Some Measures of Location and Their Influence Function
2.2.1 Quantiles
2.2.2 The Winsorized Mean
2.2.3 The Trimmed Mean
2.2.4 M-Measures of Location
2.2.5 R-Measures of Location
2.3 Measures of Scale
2.4 Scale Equivariant M-Measures of Location
2.5 Winsorized Expected Values
3 Estimating Measures of Locationand Scale
3.1 A Bootstrap Estimate of a Standard Error
3.1.1 R Function bootse
3.2 Density Estimators
3.2.1 Silverman’s Rule of Thumb
3.2.2 Rosenblatt’s Shifted Histogram
3.2.3 The Expected Frequency Curve
3.2.4 An Adaptive Kernel Estimator
3.2.5 R Functions skerd, kerSORT, kerden, kdplot, rdplot, akerd and splot
3.3 The Sample Trimmed Mean
3.3.1 R Functions mean, tmean and lloc
3.3.2 Estimating the Standard Error of the Trimmed Mean
3.3.3 Estimating the Standard Error of the Sample Winsorized Mean
3.3.4 R Functions winmean, winvar, trimse and winse
3.3.5 Estimating the Standard Error of the Sample Median
3.3.6 R Function msmedse
3.4 The Finite Sample Breakdown Point
3.5 Estimating Quantiles
3.5.1 Estimating the Standard Error of the Sample Quantile
3.5.2 R Function qse
3.5.3 The Maritz-Jarrett Estimate of the Standard Error of xq
3.5.4 R Function mjse
3.5.5 The Harrell-Davis Estimator
3.5.6 R Functions qest and hd
3.5.7 A Bootstrap Estimate of the Standard Error of thetaq
3.5.8 R Function hdseb
3.6 An M-Estimator of Location
3.6.1 R Function mad
3.6.2 Computing an M-Estimator of Location
3.6.3 R Functions mest
3.6.4 Estimating the Standard Error of the M-Estimator
3.6.5 R Function mestse
3.6.6 A Bootstrap Estimate of the Standard Error of µm
3.6.7 R Function mestseb
3.7 One-Step M-Estimator
3.7.1 R Function onestep
3.8 W-Estimators
3.8.1 Tau Measure of Location
3.8.2 R Function tauloc
3.8.3 Zuo’s Weighted Estimator
3.9 The Hodges-Lehmann Estimator
3.10 Skipped Estimators
3.10.1 R Functions mom and bmean
3.11 Some Comparisons of the Location Estimators
3.12 More Measures of Scale
3.12.1 The Biweight Midvariance
3.12.2 R Function bivar
3.12.3 The Percentage Bend Midvariance and Tau Measure of Variation
3.12.4 R Functions pbvar, tauvar
3.12.5 The Interquartile Range
3.12.6 R Functions idealf and idrange
3.13 Some Outlier Detection Methods
3.13.1 Rules Based on Means and Variances
3.13.2 A Method Based on the Interquartile Range
3.13.3 Carling’s Modification
3.13.4 A MAD-Median Rule
3.13.5 R Functions outbox, out and boxplot
3.13.6 R Functions adjboxout and adjbox
3.14 Exercises
4 Confidence Intervals in theOne-Sample Case
4.1 Problems when Working with Means
4.2 The g-and-h Distribution
Multivariate g-and-h Distributions
4.2.1 R Functions ghdist, rmul, rngh and ghtrim
4.3 Inferences About the Trimmed and Winsorized Means
4.3.1 R Functions trimci, winci and D.akp.effect
4.4 Basic Bootstrap Methods
4.4.1 The Percentile Bootstrap Method
4.4.2 R Functions onesampb and hdpb
4.4.3 Bootstrap-t Method
4.4.4 Bootstrap Methods when Using a Trimmed Mean
4.4.5 Singh’s Modification
4.4.6 R Functions trimpb and trimcibt
4.5 Inferences About M-Estimators
4.5.1 R Functions mestci and momci
4.6 Confidence Intervals for Quantiles
4.6.1 Beware of Tied Values when Making Inferences About Quantiles
4.6.2 A Modification of the Distribution-Free Method for the Median
4.6.3 R Functions qmjci, hdci, sint, sintv2, qci, qcipb and qint
4.7 Empirical Likelihood
4.7.1 Bartlett Corrected Empirical Likelihood
4.8 Concluding Remarks
4.9 Exercises
5 Comparing Two Groups
5.1 The Shift Function
5.1.1 The Kolmogorov-Smirnov Test
5.1.2 R Functions ks, kssig, kswsig, and kstiesig
5.1.3 The B and W Band for the Shift Function
5.1.4 R Functions sband and wband
5.1.5 Confidence Band for Specified Quantiles
Method Q1
Method Q2
5.1.6 R Functions shifthd, qcomhd, qcomhdMC and q2gci
5.1.7 R Functions g2plot and g5plot
5.2 Student’s t Test
5.3 Comparing Medians and Other Trimmed Means
Yuen’s Method
Comparing Medians
5.3.1 R Functions yuen and msmed
5.3.2 A Bootstrap-t Method for Comparing Trimmed Means
5.3.3 R Functions yuenbt and yhbt
5.3.4 Measuring Effect Size
A Standardized Difference
Explanatory Power
A Classification Perspective
A Probabilistic Measure of Effect Size
5.3.5 R Functions akp.effect, yuenv2,, med.effect and qhat
5.4 Inferences Based on a Percentile Bootstrap Method
5.4.1 Comparing M-Estimators
5.4.2 Comparing Trimmed Means and Medians
5.4.3 R Functions trimpb2, pb2gen, m2ci, medpb2 and M2gbt
5.5 Comparing Measures of Scale
5.5.1 Comparing Variances
5.5.2 R Function comvar2
5.5.3 Comparing Biweight Midvariances
5.5.4 R Function b2ci
5.6 Permutation Tests
5.6.1 R Function permg
5.7 Rank-Based Methods and a Probabilistic Measure of Effect Size
5.7.1 The Cliff and Brunner-Munzel Methods
Cliff’s Method
Brunner-Munzel Method
5.7.2 R Functions cid, cidv2, bmp, wmwloc, wmwpb and loc2plot
5.8 Comparing Two Independent Binomial and Multinomial Distributions
5.8.1 Storer-Kim Method
5.8.2 Beal’s Method
5.8.3 KMS Method
5.8.4 R Functions twobinom, twobici, bi2KMS, bi2KMSv2 and bi2CR
5.8.5 Comparing Discrete (Multinomial) Distributions
5.8.6 R Functions binband, splotg2, cumrelf
5.9 Comparing Dependent Groups
5.9.1 A Shift Function for Dependent Groups
5.9.2 R Function lband
5.9.3 Comparing Specified Quantiles
Method D1
Method D2
Method D3
5.9.4 R Functions shiftdhd, Dqcomhd, qdec2ci, Dqdif and difQpci
5.9.5 Comparing Trimmed Means
5.9.6 R Functions yuend, yuendv2 and D.akp.effect
5.9.7 A Bootstrap-t Method for Marginal Trimmed Means
5.9.8 R Function ydbt
5.9.9 Inferences About the Distribution of Difference Scores
5.9.10 R Functions loc2dif and l2drmci
5.9.11 Percentile Bootstrap: Comparing Medians, M-Estimators and Other Measures of Location and Scale
5.9.12 R Function bootdpci
5.9.13 Handling Missing Values
Method M1
Method M2
Method M3
Comments on Choosing a Method
5.9.14 R Functions rm2miss and rmmismcp
5.9.15 Comparing Variances
5.9.16 R Function comdvar
5.9.17 The Sign Test and Inferences About the Binomial Distribution
5.9.18 R Functions binomci, acbinomci and binomLCO
5.10 Exercises
6 Some Multivariate Methods
6.1 Generalized Variance
6.2 Depth
6.2.1 Mahalanobis Depth
6.2.2 Halfspace Depth
6.2.3 Computing Halfspace Depth
6.2.4 R Functions depth2, depth, fdepth, fdepthv2, unidepth
6.2.5 Projection Depth
6.2.6 R Functions pdis, pdisMC, and pdepth
6.2.7 Other Measures of Depth
6.2.8 R Functions zdist, zoudepth and prodepth
6.3 Some Affine Equivariant Estimators
6.3.1 Minimum Volume Ellipsoid Estimator
6.3.2 The Minimum Covariance Determinant Estimator
6.3.3 S-Estimators and Constrained M-Estimators
6.3.4 R Function tbs
6.3.5 Donoho-Gasko Generalization of a Trimmed Mean
6.3.6 R Functions dmean and dcov
6.3.7 The Stahel-Donoho W-Estimator
6.3.8 R Function sdwe
6.3.9 Median Ball Algorithm
6.3.10 R Function rmba
6.3.11 OGK Estimator
6.3.12 R Function ogk
6.3.13 An M-Estimator
6.3.14 R Functions MARest and dmedian
6.4 Multivariate Outlier Detection Methods
6.4.1 A Relplot
6.4.2 R Function relplot
6.4.3 The MVE Method
6.4.4 The MCD Method
6.4.5 R Functions covmve and covmcd
6.4.6 R Function out
6.4.7 The MGV Method
6.4.8 R Function outmgv
6.4.9 A Projection Method
6.4.10 R Functions outpro and out3d
6.4.11 Outlier Identification in High Dimensions
6.4.12 R Functions outproad and outmgvad
6.4.13 Methods Designed for Functional Data
6.4.14 R Functions FBplot, Flplot, medcurve, func.out, spag.plot, funloc and funlocpb
6.4.15 Comments on Choosing a Method
6.5 A Skipped Estimator of Location and Scatter
6.5.1 R Functions smean, wmcd, wmve, mgvmean, L1medcen, spat, mgvcov, skip, skipcov
6.6 Robust Generalized Variance
6.6.1 R Function gvarg
6.7 Multivariate Location: Inference in the One-Sample Case
6.7.1 Inferences Based on the OP Measure of Location
6.7.2 Extension of Hotelling’s T2 to Trimmed Means
6.7.3 R Functions smeancrv2 and
6.7.4 Inferences Based on the MGV Estimator
6.7.5 R Function smgvcr
6.8 Comparing OP Measures of Location
6.8.1 R Functions smean2, matsplit and mat2grp
Data Management
6.8.2 Comparing Robust Generalized Variances
6.8.3 R Function gvar2g
6.9 Multivariate Density Estimators
6.10 A Two-Sample, Projection-Type Extension of the Wilcoxon-Mann-Whitney Test
6.10.1 R Functions mulwmw and mulwmwv2
6.11 A Relative Depth Analog of the Wilcoxon-Mann-Whitney Test
6.11.1 R Function mwmw
6.12 Comparisons Based on Depth
6.12.1 R Functions lsqs3 and depthg2
6.13 Comparing Dependent Groups Based on All Pairwise Differences
6.13.1 R Function dfried
6.14 Robust Principal Components Analysis
6.14.1 R Functions prcomp and regpca
6.14.2 Maronna’s Method
6.14.3 The SPCA Method
6.14.4 Method HRVB
6.14.5 Method OP
6.14.6 Method PPCA
6.14.7 R Functions outpca, robpca, robpcaS, SPCA, Ppca, Ppca.summary
6.14.8 Comments on Choosing the Number of Components
6.15 Cluster Analysis
6.15.1 R Functions Kmeans, kmeans.grp, TKmeans, TKmeans.grp
6.16 Multivariate Discriminate Analysis
6.16.1 R Function KNNdist
6.17 Exercises
7 One-Way and Higher Designs for Independent Groups
7.1 Trimmed Means and a One-Way Design
7.1.1 A Welch-Type Procedure and a Robust Measure of Effect Size
A Robust, Heteroscedastic Measure of Effect Size
7.1.2 R Functions t1way, t1wayv2, esmcp, fac2list, t1wayF
Data Management
7.1.3 A Generalization of Box’s Method
7.1.4 R Function box1way
7.1.5 Comparing Medians and Other Quantiles
7.1.6 R Functions med1way and Qanova
7.1.7 A Bootstrap-t Method
7.1.8 R Functions t1waybt and btrim
7.2 Two-Way Designs and Trimmed Means
7.2.1 R Function t2way
7.2.2 Comparing Medians
7.2.3 R Functions med2way and Q2anova
7.3 Three-Way Designs and Trimmed Means Including Medians
7.3.1 R Functions t3way, fac2list and Q3anova
7.4 Multiple Comparisons Based on Medians and Other Trimmed Means
7.4.1 Basic Methods Based on Trimmed Means
A Step-Down Multiple Comparison Procedure
7.4.2 R Functions lincon, conCON and stepmcp
7.4.3 Multiple Comparisons for Two-Way and Three-Way Designs
7.4.4 R Functions bbmcp, mcp2med, bbbmcp, mcp3med, con2way and con3way
7.4.5 A Bootstrap-t Procedure
7.4.6 R Functions linconb, bbtrim and bbbtrim
7.4.7 Controlling the Familywise Error Rate: Improvements on the Bonferroni Method
Rom’s Method
Hochberg’s Method
Hommel’s Method
Benjamini-Hochberg Method
The k-FWER Procedures
7.4.8 R Functions p.adjust and mcpKadjp
7.4.9 Percentile Bootstrap Methods for Comparing Medians, Other Trimmed Means and Quantiles
7.4.10 R Functions linconpb, bbmcppb, bbbmcppb, medpb, Qmcp, med2mcp, med3mcp and q2by2
7.4.11 Judging Sample Sizes
7.4.12 R Function hochberg
7.4.13 Explanatory Measure of Effect Size
7.4.14 R Functions ESmainMCP and esImcp
7.4.15 Comparing Curves (Functional Data)
7.4.16 R Functions funyuenpb and Flplot2g
7.5 A Random Effects Model for Trimmed Means
7.5.1 A Winsorized Intraclass Correlation
7.5.2 R Function rananova
7.6 Global Tests Based on M-Measures of Location
Method SHB
Method LSB
7.6.1 R Functions b1way and pbadepth
7.6.2 M-Estimators and Multiple Comparisons
Variation of a Bootstrap-t Method
A Percentile Bootstrap Method: Method SR
7.6.3 R Functions linconm and pbmcp
7.6.4 M-Estimators and the Random Effects Model
7.6.5 Other Methods for One-Way Designs
7.7 M-Measures of Location and a Two-Way Design
7.7.1 R Functions pbad2way and mcp2a
7.8 Ranked-Based Methods for a One-Way Design
7.8.1 The Rust-Fligner Method
7.8.2 R Function rfanova
7.8.3 A Heteroscedastic Rank-Based Method That Allows Tied Values
7.8.4 R Function bdm
7.8.5 Inferences About a Probabilistic Measure of Effect Size
Method CHMCP
Method DBH
7.8.6 R Functions cidmulv2, wmwaov and cidM
7.9 A Rank-Based Method for a Two-Way Design
7.9.1 R Function bdm2way
7.9.2 The Patel-Hoel Approach to Interactions
7.9.3 R Function rimul
7.10 MANOVA Based on Trimmed Means
7.10.1 R Functions MULtr.anova, MULAOVp, bw2list and YYmanova
7.10.2 Linear Contrasts
7.10.3 R Functions linconMpb, linconSpb, YYmcp, fac2Mlist and fac2BBMlist
Data Management
7.11 Nested Designs
7.11.1 R Functions anova.nestA, mcp.nestA and anova.nestAP
7.12 Exercises
8 Comparing Multiple Dependent Groups
8.1 Comparing Trimmed Means
8.1.1 Omnibus Test Based on the Trimmed Means of the Marginal Distributions
8.1.2 R Function rmanova
8.1.3 Pairwise Comparisons and Linear Contrasts Based on Trimmed Means
8.1.4 Linear Contrasts Based on the Marginal Random Variables
8.1.5 R Functions rmmcp, rmmismcp and trimcimul
8.1.6 Judging the Sample Size
8.1.7 R Functions and
8.2 Bootstrap Methods Based on Marginal Distributions
8.2.1 Comparing Trimmed Means
8.2.2 R Function rmanovab
8.2.3 Multiple Comparisons Based on Trimmed Means
8.2.4 R Functions pairdepb and bptd
8.2.5 Percentile Bootstrap Methods
Method RMPB3
Method RMPB4
Missing Values
8.2.6 R Functions bd1way, ddep and ddepGMC_C
8.2.7 Multiple Comparisons Using M-Estimators or Skipped Estimators
8.2.8 R Functions lindm and mcpOV
8.3 Bootstrap Methods Based on Difference Scores
8.3.1 R Function rmdzero
8.3.2 Multiple Comparisons
8.3.3 R Functions rmmcppb, wmcppb, dmedpb, lindepbt and qdmcpdif
8.4 Comments on Which Method to Use
8.5 Some Rank-Based Methods
Method AP
Method BPRM
Decision Rule
8.5.1 R Functions apanova and bprm
8.6 Between-by-Within and Within-by-Within Designs
8.6.1 Analyzing a Between-by-Within Design Based on Trimmed Means
8.6.2 R Functions bwtrim and tsplit
8.6.3 Data Management: R Function bw2list
8.6.4 Bootstrap-t Method for a Between-by-Within Design
8.6.5 R Functions bwtrimbt and tsplitbt
8.6.6 Percentile Bootstrap Methods for a Between-by-Within Design
8.6.7 R Functions sppba, sppbb and sppbi
8.6.8 Multiple Comparisons
Method BWMCP
Method BWAMCP: Comparing Levels of Factor A for Each Level of Factor B
Method BWBMCP: Dealing with Factor B
Method BWIMCP: Interactions
8.6.9 R Functions bwmcp, bwamcp, bwbmcp, bwimcp, bwimcpES, spmcpa, spmcpb and spmcpi
8.6.10 Within-by-Within Designs
8.6.11 R Functions wwtrim, wwtrimbt, wwmcp, wwmcppb and wwmcpbt
8.6.12 A Rank-Based Approach
8.6.13 R Function bwrank
8.6.14 Rank-Based Multiple Comparisons
8.6.15 R Function bwrmcp
8.6.16 Multiple Comparisons when Using a Patel-Hoel Approach to Interactions
8.6.17 R Function sisplit
8.7 Some Rank-Based Multivariate Methods
8.7.1 The Munzel-Brunner Method
8.7.2 R Function mulrank
8.7.3 The Choi-Marden Multivariate Rank Test
8.7.4 R Function cmanova
8.8 Three-Way Designs
8.8.1 Global Tests Based on Trimmed Means
8.8.2 R Functions bbwtrim, bwwtrim, wwwtrim, bbwtrimbt, bwwtrimbt and wwwtrimbt
8.8.3 Data Management: R Functions bw2list and bbw2list
8.8.4 Multiple Comparisons
8.8.5 R Function wwwmcp
8.8.6 R Functions bbwmcp, bwwmcp, bbwmcppb, bwwmcppb and wwwmcppb
Bootstrap-t Methods
Percentile Bootstrap Methods
8.9 Exercises
9 Correlation and Tests of Independence
9.1 Problems with Pearson’s Correlation
9.1.1 Features of Data That Affect r and T
9.1.2 Heteroscedasticity and the Classic Test that rho=0
9.2 Two Types of Robust Correlations
9.3 Some Type M Measures of Correlation
9.3.1 The Percentage Bend Correlation
9.3.2 A Test of Independence Based on rhopb
9.3.3 R Function pbcor
9.3.4 A Test of Zero Correlation Among p Random Variables
9.3.5 R Function pball
9.3.6 The Winsorized Correlation
9.3.7 R Functions wincor and winall
9.3.8 The Biweight Midcovariance and Correlation
9.3.9 R Functions bicov and bicovm
9.3.10 Kendall’s tau
9.3.11 Spearman’s rho
9.3.12 R Functions tau, spear, cor and taureg
9.3.13 Heteroscedastic Tests of Zero Correlation
9.3.14 R Functions corb, pcorb and pcorhc4
9.4 Some Type O Correlations
9.4.1 MVE and MCD Correlations
9.4.2 Skipped Measures of Correlation
9.4.3 The OP Correlation
9.4.4 Inferences Based on Multiple Skipped Correlations
9.4.5 R Functions scor, mscor and scorci
9.5 A Test of Independence Sensitive to Curvature
Method INDT
9.5.1 R Functions indt, indtall and medind
9.6 Comparing Correlations: Independent Case
9.6.1 Comparing Pearson Correlations
9.6.2 Comparing Robust Correlations
9.6.3 R Functions twopcor, twohc4cor and twocor
9.7 Exercises
10 Robust Regression
10.1 Problems with Ordinary Least Squares
10.1.1 Computing Confidence Intervals Under Heteroscedasticity
Method HC4WB-D
Method HC4WB-C
10.1.2 An Omnibus Test
10.1.3 R Functions lsfitci, olshc4, hc4test and hc4wtest
10.1.4 Comments on Comparing Means via Dummy Coding
10.1.5 Salvaging the Homoscedasticity Assumption
10.2 Theil-Sen Estimator
10.2.1 R Functions tsreg, tshdreg, correg, regplot and regp2plot
10.3 Least Median of Squares
10.3.1 R Function lmsreg
10.4 Least Trimmed Squares Estimator
10.4.1 R Functions ltsreg and ltsgreg
10.5 Least Trimmed Absolute Value Estimator
10.5.1 R Function ltareg
10.6 M-Estimators
10.7 The Hat Matrix
10.8 Generalized M-Estimators
10.8.1 R Function bmreg
10.9 The Coakley-Hettmansperger and Yohai Estimators
10.9.1 MM-Estimator
10.9.2 R Functions chreg and MMreg
10.10 Skipped Estimators
10.10.1 R Functions mgvreg and opreg
10.11 Deepest Regression Line
10.11.1 R Functions rdepth and mdepreg
10.12 A Criticism of Methods with a High Breakdown Point
10.13 Some Additional Estimators
10.13.1 S-Estimators and tau-Estimators
10.13.2 R Functions snmreg and stsreg
10.13.3 E-Type Skipped Estimators
10.13.4 R Functions mbmreg, tstsreg, tssnmreg and gyreg
10.13.5 Methods Based on Robust Covariances
10.13.6 R Functions bireg, winreg and COVreg
10.13.7 L-Estimators
10.13.8 L1 and Quantile Regression
10.13.9 R Functions qreg, rqfit, qplotreg
10.13.10 Methods Based on Estimates of the Optimal Weights
10.13.11 Projection Estimators
10.13.12 Methods Based on Ranks
10.13.13 R Functions Rfit and Rfit.est
10.13.14 Empirical Likelihood Type and Distance-Constrained Maximum Likelihood Estimators
10.14 Comments About Various Estimators
10.14.1 Contamination Bias
10.15 Outlier Detection Based on a Robust Fit
10.15.1 Detecting Regression Outliers
10.15.2 R Functions reglev and rmblo
10.16 Logistic Regression and the General Linear Model
10.16.1 R Functions glm, logreg, wlogreg, logreg.plot
10.16.2 The General Linear Model
10.16.3 R Function glmrob
10.17 Multivariate Regression
10.17.1 The RADA Estimator
10.17.2 The Least Distance Estimator
10.17.3 R Functions MULMreg, mlrreg and Mreglde
10.17.4 Multivariate Least Trimmed Squares Estimator
10.17.5 R Function MULtsreg
10.17.6 Other Robust Estimators
10.18 Exercises
11 More Regression Methods
11.1 Inferences About Robust Regression Parameters
11.1.1 Omnibus Tests for Regression Parameters
11.1.2 R Function regtest
11.1.3 Inferences About Individual Parameters
11.1.4 R Functions regci, regciMC and wlogregci
11.1.5 Methods Based on the Quantile Regression Estimator
11.1.6 R Functions rqtest, qregci and qrchk
11.1.7 Inferences Based on the OP Estimator
11.1.8 R Functions opregpb and opregpbMC
11.1.9 Hypothesis Testing when Using a Multivariate Regression Estimator RADA
11.1.10 R Function mlrGtest
11.1.11 Robust ANOVA via Dummy Coding
11.1.12 Confidence Bands for the Typical Value of y Given x
11.1.13 R Functions regYhat, regYci, and regYband
11.1.14 R Function regse
11.2 Comparing the Regression Parameters of J >=2 Groups
11.2.1 Methods for Comparing Independent Groups
Methods Based on the Least Squares Regression Estimator
Multiple Comparisons
Methods Based on Robust Estimators
11.2.2 R Functions reg2ci, reg1way, reg1wayISO, ancGpar, ols1way, ols1wayISO, olsJmcp, olsJ2, reg1mcp and olsWmcp
11.2.3 Methods for Comparing Two Dependent Groups
Methods Based on a Robust Estimator
Methods Based on the Least Squares Estimator
11.2.4 R Functions DregG, difreg, DregGOLS
11.3 Detecting Heteroscedasticity
11.3.1 A Quantile Regression Approach
11.3.2 Koenker-Bassett Method
11.3.3 R Functions qhomt and khomreg
11.4 Curvature and Half-Slope Ratios
11.4.1 R Function hratio
11.5 Curvature and Nonparametric Regression
11.5.1 Smoothers
11.5.2 Kernel Estimators and Cleveland’s LOWESS
Kernel Smoothing
Cleveland’s LOWESS
11.5.3 R Functions lplot, lplot.pred and kerreg
11.5.4 The Running-Interval Smoother
11.5.5 R Functions rplot and runYhat
11.5.6 Smoothers for Estimating Quantiles
11.5.7 R Function qhdsm
11.5.8 Special Methods for Binary Outcomes
11.5.9 R Functions logSM, logSMpred, bkreg and rplot.bin
11.5.10 Smoothing with More than One Predictor
11.5.11 R Functions rplot, runYhat, rplotsm and runpd
11.5.12 LOESS
11.5.13 Other Approaches
11.5.14 R Functions adrun, adrunl, gamplot, gamplotINT
11.6 Checking the Specification of a Regression Model
11.6.1 Testing the Hypothesis of a Linear Association
11.6.2 R Function lintest
11.6.3 Testing the Hypothesis of a Generalized Additive Model
11.6.4 R Function adtest
11.6.5 Inferences About the Components of a Generalized Additive Model
11.6.6 R Function adcom
11.6.7 Detecting Heteroscedasticity Based on Residuals
11.6.8 R Function rhom
11.7 Regression Interactions and Moderator Analysis
11.7.1 R Functions kercon, riplot, runsm2g, ols.plot.inter, olshc4.inter, reg.plot.inter and regci.inter
11.7.2 Mediation Analysis
11.7.3 R Functions ZYmediate, regmed2 and regmediate
11.8 Comparing Parametric, Additive and Nonparametric Fits
11.8.1 R Functions adpchk and pmodchk
11.9 Measuring the Strength of an Association Given a Fit to the Data
11.9.1 R Functions RobRsq, qcorp1 and qcor
11.9.2 Comparing Two Independent Groups via the LOWESS Version of Explanatory Power
11.9.3 R Functions smcorcom and smstrcom
11.10 Comparing Predictors
11.10.1 Comparing Correlations
11.10.2 R Functions TWOpov, TWOpNOV, corCOMmcp, twoDcorR, and twoDNOV
11.10.3 Methods Based on Prediction Error
The 0.632 Estimator
The Leave-One-Out Cross-Validation Method
11.10.4 R Functions regpre and regpreCV
11.10.5 R Function larsR
11.10.6 Inferences About Which Predictors Are Best
Method IBS
Method BTS
Method SM
11.10.7 R Functions regIVcom, ts2str and sm2strv7
11.11 Marginal Longitudinal Data Analysis: Comments on Comparing Groups
11.11.1 R Functions long2g, longreg, longreg.plot and xyplot
11.12 Exercises
12.1 Methods Based on Specific Design Points and a Linear Model
12.1.1 Method S1
12.1.2 Method S2
12.1.3 Dealing with Two Covariates
12.1.4 R Functions ancJN, ancJNmp, ancJNmpcp, anclin, reg2plot and reg2g.p2plot
12.2 Methods when There Is Curvature and a Single Covariate
12.2.1 Method Y
12.2.2 Method BB: Bootstrap Bagging
12.2.3 Method UB
12.2.4 Method TAP
12.2.5 Method G
12.2.6 R Functions ancova, ancovaWMW, ancpb, rplot2g, runmean2g, lplot2g, ancdifplot, ancboot, ancbbpb, qhdsm2g, ancovaUB, ancovaUB.pv, ancdet, ancmg1 and ancGLOB
12.3 Dealing with Two Covariates when There Is Curvature
12.3.1 Method MC1
12.3.2 Method MC2
12.3.3 Method MC3
12.3.4 R Functions ancovamp, ancovampG, ancmppb, ancmg, ancov2COV, ancdes and ancdet2C
12.4 Some Global Tests
12.4.1 Method TG
12.4.2 R Functions ancsm and Qancsm
12.5 Methods for Dependent Groups
12.5.1 Methods Based on a Linear Model
12.5.2 R Functions Dancts and Dancols
12.5.3 Dealing with Curvature: Methods DY, DUB and DTAP
12.5.4 R Functions Dancova, Dancovapb, DancovaUB and Dancdet
12.6 Exercises

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