Cryptic multiple hypotheses testing in linear models: overestimated effect sizes and the winner's curse

[1]  William N. Venables,et al.  Modern Applied Statistics with S , 2010 .

[2]  H. Schielzeth.  Simple means to improve the interpretability of regression coefficients , 2010 .

[3]  L. Garamszegi,et al.  Information-theoretic approaches to statistical analysis in behavioural ecology: an introduction , 2010, Behavioral Ecology and Sociobiology.

[4]  Roger Mundry,et al.  Issues in information theory-based statistical inference—a commentary from a frequentist’s perspective , 2010, Behavioral Ecology and Sociobiology.

[5]  N. Dochtermann,et al.  Developing multiple hypotheses in behavioral ecology , 2010, Behavioral Ecology and Sociobiology.

[6]  Robert P. Freckleton,et al.  Dealing with collinearity in behavioural and ecological data: model averaging and the problems of measurement error , 2010, Behavioral Ecology and Sociobiology.

[7]  David R. Anderson,et al.  Model selection bias and Freedman’s paradox , 2010 .

[8]  R. L. Rodríguez.  Trait duplication by means of sensory bias , 2009 .

[9]  Kimberly A. Pollard,et al.  Changing philosophies and tools for statistical inferences in behavioral ecology , 2009 .

[10]  John P. A. Ioannidis,et al.  Validating, augmenting and refining genome-wide association signals , 2009, Nature Reviews Genetics.

[11]  H. Schielzeth,et al.  Conclusions beyond support: overconfident estimates in mixed models , 2008, Behavioral ecology : official journal of the International Society for Behavioral Ecology.

[12]  Roger Mundry,et al.  Stepwise Model Fitting and Statistical Inference: Turning Noise into Signal Pollution , 2008, The American Naturalist.

[13]  J. Ioannidis.  Why Most Discovered True Associations Are Inflated , 2008, Epidemiology.

[14]  P. Legendre,et al.  Forward selection of explanatory variables. , 2008, Ecology.

[15]  Michael J. Crawley,et al.  The R book , 2022 .

[16]  J. Dale,et al.  Individual recognition: it is good to be different. , 2007, Trends in ecology & evolution.

[17]  Philip A. Stephens,et al.  Inference in ecology and evolution. , 2007, Trends in ecology & evolution.

[18]  J. Pritchard,et al.  Overcoming the winner's curse: estimating penetrance parameters from case-control data. , 2007, American journal of human genetics.

[19]  Clint D. Kelly,et al.  Replicating Empirical Research In Behavioral Ecology: How And Why It Should Be Done But Rarely Ever Is , 2006, The Quarterly Review of Biology.

[20]  Robert P Freckleton,et al.  Why do we still use stepwise modelling in ecology and behaviour? , 2006, The Journal of animal ecology.

[21]  J. Ioannidis.  Why Most Published Research Findings Are False , 2005, PLoS medicine.

[22]  Koen J. F. Verhoeven,et al.  Implementing false discovery rate control: increasing your power , 2005 .

[23]  Shinichi Nakagawa.  A farewell to Bonferroni: the problems of low statistical power and publication bias , 2004, Behavioral Ecology.

[24]  John D. Storey,et al.  Statistical significance for genomewide studies , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[25]  David R. Anderson,et al.  Model selection and multimodel inference : a practical information-theoretic approach , 2003 .

[26]  Walter Krämer,et al.  Review of Modern applied statistics with S, 4th ed. by W.N. Venables and B.D. Ripley. Springer-Verlag 2002 , 2003 .

[27]  A. Møller,et al.  A survey of the statistical power of research in behavioral ecology and animal behavior , 2003 .

[28]  G. Quinn,et al.  Experimental Design and Data Analysis for Biologists , 2002 .

[29]  M. Jennions,et al.  Relationships fade with time: a meta-analysis of temporal trends in publication in ecology and evolution , 2002, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[30]  J Blangero,et al.  Large upward bias in estimation of locus-specific effects from genomewide scans. , 2001, American journal of human genetics.

[31]  David R. Anderson,et al.  Null Hypothesis Testing: Problems, Prevalence, and an Alternative , 2000 .

[32]  Andy P. Field,et al.  Discovering Statistics Using SPSS , 2000 .

[33]  T. Perneger.  What's wrong with Bonferroni adjustments , 1998, BMJ.

[34]  C. Chatfield.  Model uncertainty, data mining and statistical inference , 1995 .

[35]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[36]  Larry E. Toothaker,et al.  Multiple Regression: Testing and Interpreting Interactions , 1991 .

[37]  S. P. Wright,et al.  Adjusted P-values for simultaneous inference , 1992 .

[38]  Ping Zhang,et al.  Inference after variable selection in linear regression models , 1992 .

[39]  H. Keselman,et al.  Backward, forward and stepwise automated subset selection algorithms: Frequency of obtaining authentic and noise variables , 1992 .

[40]  W. Rice.  ANALYZING TABLES OF STATISTICAL TESTS , 1989, Evolution; international journal of organic evolution.

[41]  Y. Hochberg.  A sharper Bonferroni procedure for multiple tests of significance , 1988 .

[42]  Alan J. Miller.  Sélection of subsets of regression variables , 1984 .

[43]  S. Holm.  A Simple Sequentially Rejective Multiple Test Procedure , 1979 .