The linear mixed model (LMM) is a popular statistical for the analysis of longitudinal data. However, robust estimation and inferential conclusions LMM in presence outliers (i.e., observations with very low probability occurrence under Normality) not part mainstream data analysis. In this work, we compared coverage rates confidence intervals (CIs) ba...

The genotype main effects and genotype-by-environment interaction effects (GGE) model and the additive main effects and multiplicative interaction (AMMI) model are two common models for analysis of genotype-by-environment data. These models are frequently used by agronomists, plant breeders, geneticists and statisticians for analysis of multi-environment trials. In such trials, a set of genotyp...

This paper introduces the confidence interval estimate for measuring the bullwhip effect, which has been observed across most industries. Calculating a confidence interval usually needs the assumption about the underlying distribution. Bootstrapping is a non-parametric, but computer intensive, estimation method. In this paper, a simulation study on the behavior of the 95% bootstrap confidence i...

We propose a test for shape constraints which can be expressed by transformations of the coordinates of multivariate regression functions. The method is motivated by the constraint of symmetry with respect to some unknown hyperplane but can easily be generalized to other shape constraints of this type or other semi-parametric settings. In a first step, the unknown parameters are estimated and i...

Diffusion MRI is a noninvasive imaging modality that allows for the estimation and visualization of white matter connectivity patterns in the human brain. However, due to the low signal-to-noise ratio (SNR) nature of diffusion data, deriving useful statistics from the data is adversely affected by different sources of measurement noise. This is aggravated by the fact that the sampling distribut...

The block bootstrap is the best known bootstrap method for time-series data when the analyst does not have a parametric model that reduces the data generation process to simple random sampling. However, the errors made by the block bootstrap converge to zero only slightly faster than those made by first-order asymptotic approximations. This paper describes a bootstrap procedure for data that ar...

This paper is about constructing confidence bands around an ROC curve such that (1 − δ)% of the ROC curves traced by data sets of size r will fall completely within the bands. We introduce to the machine learning community three methods from the medical field that are applicable to generate such bands. We then evaluate these methods on the simple case of “binormal” distributions— the scores for...

The parametric bootstrap can be used for the efficient computation of Bayes posterior distributions. Importance sampling formulas take on an easy form relating to the deviance in exponential families, and are particularly simple starting from Jeffreys invariant prior. Because of the i.i.d. nature of bootstrap sampling, familiar formulas describe the computational accuracy of the Bayes estimates...

This paper introduces speci"cation tests of parametric mean-regression models. The null hypothesis of interest is that the parametric regression function is correctly speci"ed. The proposed tests are generalizations of the Kolmogorov}Smirnov and Cramer}von Mises tests to the regression framework. They are consistent against all alternatives to the null hypothesis, powerful against 1/Jn local al...

We present a general sampling procedure to quantify model mimicry, defined as the ability of a model to account for data generated by a competing model. This sampling procedure, called the parametric bootstrap cross-fitting method (PBCM; cf. Williams (J. R. Statist. Soc. B 32 (1970) 350; Biometrics 26 (1970) 23)), generates distributions of differences in goodness-of-fit expected under each of ...