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Articles 1 - 8 of 8
Full-Text Articles in Physical Sciences and Mathematics
Non-Parametric Tests For Testing Of Scale Parameters, Manish Goyal, Narinder Kumar
Non-Parametric Tests For Testing Of Scale Parameters, Manish Goyal, Narinder Kumar
Journal of Modern Applied Statistical Methods
One of the fundamental problems in testing of equality of populations is of testing the equality of scale parameters. The subsequent usages for scale are dispersion, spread and variability. In this paper, we proposed non-parametric tests based on U-Statistics for the testing of equality of scale parameters. The null distribution of proposed tests is developed and its Pitman efficiency is worked out to compare proposed tests with respect to some existing tests. Simulation study is carried out to compute the asymptotic power of proposed tests. An illustrative example is also provided.
Estimating Model Complexity Of Feed-Forward Neural Networks, Douglas Landsittel
Estimating Model Complexity Of Feed-Forward Neural Networks, Douglas Landsittel
Journal of Modern Applied Statistical Methods
In a previous simulation study, the complexity of neural networks for limited cases of binary and normally-distributed variables based the null distribution of the likelihood ratio statistic and the corresponding chi-square distribution was characterized. This study expands on those results and presents a more general formulation for calculating degrees of freedom.
Test Statistics Null Distributions In Multiple Testing: Simulation Studies And Applications To Genomics, Katherine S. Pollard, Merrill D. Birkner, Mark J. Van Der Laan, Sandrine Dudoit
Test Statistics Null Distributions In Multiple Testing: Simulation Studies And Applications To Genomics, Katherine S. Pollard, Merrill D. Birkner, Mark J. Van Der Laan, Sandrine Dudoit
U.C. Berkeley Division of Biostatistics Working Paper Series
Multiple hypothesis testing problems arise frequently in biomedical and genomic research, for instance, when identifying differentially expressed or co-expressed genes in microarray experiments. We have developed generally applicable resampling-based single-step and stepwise multiple testing procedures (MTP) for control of a broad class of Type I error rates, defined as tail probabilities and expected values for arbitrary functions of the numbers of false positives and rejected hypotheses (Dudoit and van der Laan, 2005; Dudoit et al., 2004a,b; Pollard and van der Laan, 2004; van der Laan et al., 2005, 2004a,b). As argued in the early article of Pollard and van der …
Resampling Based Multiple Testing Procedure Controlling Tail Probability Of The Proportion Of False Positives, Mark J. Van Der Laan, Merrill D. Birkner, Alan E. Hubbard
Resampling Based Multiple Testing Procedure Controlling Tail Probability Of The Proportion Of False Positives, Mark J. Van Der Laan, Merrill D. Birkner, Alan E. Hubbard
U.C. Berkeley Division of Biostatistics Working Paper Series
Simultaneously testing a collection of null hypotheses about a data generating distribution based on a sample of independent and identically distributed observations is a fundamental and important statistical problem involving many applications. In this article we propose a new resampling based multiple testing procedure asymptotically controlling the probability that the proportion of false positives among the set of rejections exceeds q at level alpha, where q and alpha are user supplied numbers. The procedure involves 1) specifying a conditional distribution for a guessed set of true null hypotheses, given the data, which asymptotically is degenerate at the true set of …
Multiple Testing Procedures And Applications To Genomics, Merrill D. Birkner, Katherine S. Pollard, Mark J. Van Der Laan, Sandrine Dudoit
Multiple Testing Procedures And Applications To Genomics, Merrill D. Birkner, Katherine S. Pollard, Mark J. Van Der Laan, Sandrine Dudoit
U.C. Berkeley Division of Biostatistics Working Paper Series
This chapter proposes widely applicable resampling-based single-step and stepwise multiple testing procedures (MTP) for controlling a broad class of Type I error rates, in testing problems involving general data generating distributions (with arbitrary dependence structures among variables), null hypotheses, and test statistics (Dudoit and van der Laan, 2005; Dudoit et al., 2004a,b; van der Laan et al., 2004a,b; Pollard and van der Laan, 2004; Pollard et al., 2005). Procedures are provided to control Type I error rates defined as tail probabilities for arbitrary functions of the numbers of Type I errors, V_n, and rejected hypotheses, R_n. These error rates include: …
Multiple Testing Procedures For Controlling Tail Probability Error Rates, Sandrine Dudoit, Mark J. Van Der Laan, Merrill D. Birkner
Multiple Testing Procedures For Controlling Tail Probability Error Rates, Sandrine Dudoit, Mark J. Van Der Laan, Merrill D. Birkner
U.C. Berkeley Division of Biostatistics Working Paper Series
The present article discusses and compares multiple testing procedures (MTP) for controlling Type I error rates defined as tail probabilities for the number (gFWER) and proportion (TPPFP) of false positives among the rejected hypotheses. Specifically, we consider the gFWER- and TPPFP-controlling MTPs proposed recently by Lehmann & Romano (2004) and in a series of four articles by Dudoit et al. (2004), van der Laan et al. (2004b,a), and Pollard & van der Laan (2004). The former Lehmann & Romano (2004) procedures are marginal, in the sense that they are based solely on the marginal distributions of the test statistics, i.e., …
Multiple Testing Procedures: R Multtest Package And Applications To Genomics, Katherine S. Pollard, Sandrine Dudoit, Mark J. Van Der Laan
Multiple Testing Procedures: R Multtest Package And Applications To Genomics, Katherine S. Pollard, Sandrine Dudoit, Mark J. Van Der Laan
U.C. Berkeley Division of Biostatistics Working Paper Series
The Bioconductor R package multtest implements widely applicable resampling-based single-step and stepwise multiple testing procedures (MTP) for controlling a broad class of Type I error rates, in testing problems involving general data generating distributions (with arbitrary dependence structures among variables), null hypotheses, and test statistics. The current version of multtest provides MTPs for tests concerning means, differences in means, and regression parameters in linear and Cox proportional hazards models. Procedures are provided to control Type I error rates defined as tail probabilities for arbitrary functions of the numbers of false positives and rejected hypotheses. These error rates include tail probabilities …
Multiple Testing. Part I. Single-Step Procedures For Control Of General Type I Error Rates, Sandrine Dudoit, Mark J. Van Der Laan, Katherine S. Pollard
Multiple Testing. Part I. Single-Step Procedures For Control Of General Type I Error Rates, Sandrine Dudoit, Mark J. Van Der Laan, Katherine S. Pollard
U.C. Berkeley Division of Biostatistics Working Paper Series
The present article proposes general single-step multiple testing procedures for controlling Type I error rates defined as arbitrary parameters of the distribution of the number of Type I errors, such as the generalized family-wise error rate. A key feature of our approach is the test statistics null distribution (rather than data generating null distribution) used to derive cut-offs (i.e., rejection regions) for these test statistics and the resulting adjusted p-values. For general null hypotheses, corresponding to submodels for the data generating distribution, we identify an asymptotic domination condition for a null distribution under which single-step common-quantile and common-cut-off procedures asymptotically …