A Guide to Robust Statistical Methods in Neuroscience
暂无分享,去创建一个
[1] Rand R. Wilcox,et al. Comparing Two Independent Groups Via a Quantile Generalization of the Wilcoxon-Mann-Whitney Test , 2012 .
[2] Gian Domenico Iannetti,et al. Whole-Body Mapping of Spatial Acuity for Pain and Touch , 2014, Annals of neurology.
[3] N. Cliff. Ordinal methods for behavioral data analysis , 1996 .
[4] Jie Mi,et al. Robust Nonparametric Statistical Methods , 1999, Technometrics.
[5] W. Cleveland. Robust Locally Weighted Regression and Smoothing Scatterplots , 1979 .
[6] V. Yohai,et al. Robust Statistics: Theory and Methods , 2006 .
[7] Rand R. Wilcox,et al. Comparing Pearson Correlations: Dealing with Heteroscedasticity and Nonnormality , 2009, Commun. Stat. Simul. Comput..
[8] J. L. Rasmussen,et al. Data transformation, Type I error rate and power , 1989 .
[9] Kjell A. Doksum,et al. Statistical Tests Based on Transformed Data , 1983 .
[10] Rand Wilcox,et al. An inferential method for determining which of two independent variables is most important when there is curvature , 2018, Journal of Modern Applied Statistical Methods.
[11] Morten W Fagerland,et al. The Wilcoxon–Mann–Whitney test under scrutiny , 2009, Statistics in medicine.
[12] Stephane Heritier,et al. Robust Methods in Biostatistics , 2009 .
[13] Howard Wainer,et al. Robust Regression & Outlier Detection , 1988 .
[14] Peter J. Rousseeuw,et al. Robust Regression and Outlier Detection , 2005, Wiley Series in Probability and Statistics.
[15] B. L. Welch. THE SIGNIFICANCE OF THE DIFFERENCE BETWEEN TWO MEANS WHEN THE POPULATION VARIANCES ARE UNEQUAL , 1938 .
[16] Kjell A. Doksum,et al. Plotting with confidence: Graphical comparisons of two populations , 1976 .
[17] J. Tukey,et al. LESS VULNERABLE CONFIDENCE AND SIGNIFICANCE PROCEDURES FOR LOCATION BASED ON A SINGLE SAMPLE : TRIMMING/WINSORIZATION 1 , 2016 .
[18] Simon J. Sheather,et al. Confidence intervals based on interpolated order statistics , 1986 .
[19] J. Ruscio,et al. A probability-based measure of effect size: robustness to base rates and other factors. , 2008, Psychological methods.
[20] Joseph P. Romano,et al. EXACT AND ASYMPTOTICALLY ROBUST PERMUTATION TESTS , 2013, 1304.5939.
[21] E. Ziegel. Introduction to Robust Estimation and Hypothesis Testing (2nd ed.) , 2005 .
[22] S. Sheather,et al. Robust Estimation & Testing: Staudte/Robust , 1990 .
[23] J. Ruscio,et al. Confidence Intervals for the Probability of Superiority Effect Size Measure and the Area Under a Receiver Operating Characteristic Curve , 2012, Multivariate behavioral research.
[24] Guillaume A. Rousselet,et al. Robust Correlation Analyses: False Positive and Power Validation Using a New Open Source Matlab Toolbox , 2012, Front. Psychology.
[25] G. T. Duncan,et al. A Monte-Carlo study of asymptotically robust tests for correlation coefficients , 1973 .
[26] D. Grayson,et al. Some Myths and Legends in Quantitative Psychology , 2004 .
[27] V. Garovic,et al. Beyond Bar and Line Graphs: Time for a New Data Presentation Paradigm , 2015, PLoS biology.
[28] Eric M. Prager,et al. Reduced GABAergic Inhibition in the Basolateral Amygdala and the Development of Anxiety-Like Behaviors after Mild Traumatic Brain Injury , 2014, PloS one.
[29] J. Tukey. The Philosophy of Multiple Comparisons , 1991 .
[30] K. Yuen,et al. The two-sample trimmed t for unequal population variances , 1974 .
[31] H. J. Whitford,et al. How to Use the Two Sample t‐Test , 1986 .
[32] R. Newcombe,et al. Confidence intervals for an effect size measure based on the Mann–Whitney statistic. Part 1: general issues and tail‐area‐based methods , 2006, Statistics in medicine.
[33] Julia Kastner,et al. Introduction to Robust Estimation and Hypothesis Testing , 2005 .
[34] Catherine Lebel,et al. Reading skill and structural brain development , 2014, Neuroreport.
[35] Rand R. Wilcox,et al. Generalized Linear Model Analyses for Treatment Group Equality when Data are Non-Normal , 2016 .
[36] Curtis L Baker,et al. Categorically distinct types of receptive fields in early visual cortex. , 2016, Journal of neurophysiology.
[37] Frank E. Harrell,et al. A new distribution-free quantile estimator , 1982 .
[38] J. Brian Gray,et al. Introduction to Linear Regression Analysis , 2002, Technometrics.
[39] Joseph P. Romano. On the behaviour of randomization tests without the group invariance assumption , 1990 .
[40] Jimmy A. Doi,et al. A Coverage Probability Approach to Finding an Optimal Binomial Confidence Procedure , 2014 .
[41] Guillaume A. Rousselet,et al. Improving standards in brain-behavior correlation analyses , 2012, Front. Hum. Neurosci..
[42] Barbara J. Wendling,et al. Woodcock-Johnson III Tests of Achievement. , 2009 .
[43] Thomas E. Nichols,et al. luster-based computational methods for mass univariate analyses f event-related brain potentials / fields : A simulation study , 2022 .
[44] Morton B. Brown,et al. The Small Sample Behavior of Some Statistics Which Test the Equality of Several Means , 1974 .
[45] Rand R. Wilcox,et al. Understanding and Applying Basic Statistical Methods Using R , 2016 .
[46] A. Hald. A history of mathematical statistics from 1750 to 1930 , 1998 .
[47] Subir Ghosh,et al. Nonparametric Analysis of Longitudinal Data in Factorial Experiments , 2003, Technometrics.
[48] Y. Hochberg. A sharper Bonferroni procedure for multiple tests of significance , 1988 .
[49] Clemens S. Bernhardson,et al. 375: Type I Error Rates When Multiple Comparison Procedures Follow a Significant F Test of ANOVA , 1975 .
[50] Rand R. Wilcox,et al. Comparing Measures of Location: Some Small-Sample Results When Distributions Differ in Skewness and Kurtosis Under Heterogeneity of Variances , 2013, Commun. Stat. Simul. Comput..
[51] S. Sheather,et al. Robust Estimation and Testing , 1990 .
[52] G. Hommel. A stagewise rejective multiple test procedure based on a modified Bonferroni test , 1988 .
[53] Rand R. Wilcox,et al. Robust regression: an inferential method for determining which independent variables are most important , 2018, Introduction to Robust Estimation and Hypothesis Testing.
[54] Cyril R Pernet,et al. Beyond differences in means: robust graphical methods to compare two groups in neuroscience , 2017, bioRxiv.
[55] H. Keselman,et al. Backward, forward and stepwise automated subset selection algorithms: Frequency of obtaining authentic and noise variables , 1992 .
[56] E. Wagenmakers,et al. Erroneous analyses of interactions in neuroscience: a problem of significance , 2011, Nature Neuroscience.
[57] Robert J. Boik,et al. The Fisher-Pitman permutation test: A non-robust alternative to the normal theory F test when variances are heterogeneous , 1987 .
[58] Stephen M. Smith,et al. Permutation inference for the general linear model , 2014, NeuroImage.
[59] Guillaume A. Rousselet,et al. A few simple steps to improve the description of group results in neuroscience , 2016, The European journal of neuroscience.
[60] A. Agresti,et al. Approximate is Better than “Exact” for Interval Estimation of Binomial Proportions , 1998 .
[61] Han L. J. van der Maas,et al. Science Perspectives on Psychological an Agenda for Purely Confirmatory Research on Behalf Of: Association for Psychological Science , 2022 .
[62] Stacey A. Hancock. Modern Statistics for the Social and Behavioral Sciences: A Practical Introduction , 2012 .