BOOTSTRAP-BASED INFERENCE FOR GROUPED DATA

Network Inference from Grouped Data

In medical research, economics, and the social sciences data frequently appear as subsets of a set of objects. Over the past century a number of descriptive statistics have been developed to construct network structure from such data. However, these measures lack a generating mechanism that links the inferred network structure to the observed groups. To address this issue, we propose a modelbas...

Bootstrap Statistical Inference for the Variance Based on Fuzzy Data

Abstract: The bootstrap is a simple and straightforward method for calculating approximated biases, standard deviations, confidence intervals, testing statistical hypotheses, and so forth, in almost any nonparametric estimation problem. In this paper we describe a bootstrap method for variance that is designed directly for hypothesis testing in case of fuzzy data based on Yao-Wu signed distance.

Large Sample and Bootstrap Intervals for the Gamma Scale Parameter Based on Grouped Data

Inference for grouped data with a truncated skew-Laplace distribution

The skew-Laplace distribution has been used for modelling particle size with point observations. In reality, the observations are truncated and grouped (rounded). This must be formally taken into account for accurate modelling, and it is shown how this leads to convenient closed-form expressions for the likelihood in this model. In a Bayesian framework, “noninformative” benchmark priors, which ...

Bootstrap Tilting Inference and Large Data Sets

and hypothesis tests using fast bootstrap methods, and ways to make bootstrapping feasible for large data sets. Classical inference (intervals and tests) methods are known to be inaccurate when the underlying assumptions are violated, the usual case in practice. For example, skewness causes the usual t-test to be in error. The new methods would be an order of magnitude (power of p (n), where n ...