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Comparative investigation of three Bayesian p values
Bayesian model checking Posterior predictive p value Sampled posterior p value
2016/1/26
Please check your proof carefully and mark all corrections at the appropriate place in the proof (e.g., by using on-screen annotation in the PDF file) or compile them in a separate list. Note: if you ...
Backgroud: Epistatic Miniarray Profiles (EMAP) enables the research of genetic interaction as an importan-t method to construct large-scale genetic interaction network. However, high proportion of mis...
Mann-Whitney Test with Adjustments to Pre-treatment Variables for Missing Values and Observational Study
Dimension reduction Kernel smoothing Mann-Whitney statistic Missing out- comes Observational studies Selection bias
2016/1/25
The conventional Wilcoxon/Mann-Whitney test can be invalid for comparing treatment effects in the presence of missing values or in observational studies. This is because the missingness of the outcome...
Comparative investigation of three Bayesian p values
Bayesian model checking Posterior predictive p value Sampled posterior p value
2016/1/20
Bayesian p values are a popular and important class of approaches for Bayesian model checking. They are used to quantify the degree of surprise from the observed data given the specified data model an...
Backgroud: Epistatic Miniarray Profiles (EMAP) enables the research of genetic interaction as an importan-t method to construct large-scale genetic interaction network. However, high proportion of mis...
Mann-Whitney Test with Adjustments to Pre-treatment Variables for Missing Values and Observational Study
Dimension reduction Kernel smoothing Mann-Whitney statistic
2016/1/20
The conventional Wilcoxon/Mann-Whitney test can be invalid for comparing treatment effects in the presence of missing values or in observational studies. This is because the missingness of the outcome...
Nonparametric Regression with Discrete Covariate and Missing Values
Nonparametric Regression Discrete kernel smoothing Imputation Missing Values Variance Reduction
2016/1/19
We consider nonparametric regression with a mixture of continuous and discrete ex-planatory variables where realizations of the response variable may be missing. An impu-tation based nonparametric reg...
The Optimal Hard Threshold for Singular Values is 4/sqrt(3)
Optimal Hard Threshold Singular Values 4/sqrt(3)
2013/6/14
We consider recovery of low-rank matrices from noisy data by hard thresholding of singular values, where singular values below a prescribed threshold \lambda are set to 0. We study the asymptotic MSE ...
Statistical modelling of summary values leads to accurate Approximate Bayesian Computations
Statistical modelling summary values leads accurate Approximate Bayesian Computations
2013/6/14
Approximate Bayesian Computations (ABC) are considered to be noisy. We show that ABC can be set up to estimate the mode of the true posterior density exactly, or alternatively provide unbiased estimat...
Testing in the Presence of Nuisance Parameters: Some Comments on Tests Post-Model-Selection and Random Critical Values
Nuisance Parameters Post-Model-Selection Random Critical Values
2012/11/22
We point out that the ideas underlying some test procedures recently proposed for testing post-model-selection (and for some other test problems) in the econometrics literature have been around for qu...
Convergence and asymptotic normality of variational Bayesian approximations for exponential family models with missing values
Convergence asymptotic normality variational Bayesian approximations exponential family models missing values
2012/9/19
We study the properties of variational Bayes approximations for exponential family mod-els with missing values. It is shown that the iterative algorithm for obtaining the varia-tional Bayesian estimat...
A note on extreme values and kernel estimators of sample boundaries
support estimation asymptotic normality kernel estimator ex-treme values.
2012/9/18
In a previous paper [3], we studied a kernel estimate of the upper edge of a two-dimensional bounded set, based upon the extreme values of a Poisson point process. The initial paper [1] on the subject...
Simple estimators of false discovery rates given as few as one or two p-values without strong parametric assumptions
Bayesian false discovery rate confidence distribution empirical Bayes
2011/7/6
Multiple comparison procedures that control a family-wise error rate or false discovery rate provide an achieved error rate as the adjusted p-value for each hypothesis tested.
Simultaneous critical values for $t$-tests in very high dimensions
empirical processes FDR high dimension microarrays multiple hypothesis testing one-sample t-statistics self-normalized moderate deviation two-sample t-statistics
2011/3/21
This article considers the problem of multiple hypothesis testing using $t$-tests. The observed data are assumed to be independently generated conditional on an underlying and unknown two-state hidden...
Invariant $P$-values for model checking
P-values invariance under transformations discrepancy mea-sures for model checking
2010/3/9
P-values have been the focus of considerable criticism based on
various considerations. Still, the P-value represents one of the most
commonly used statistical tools. When assessing the suitability ...