DeepAI AI Chat
Log In Sign Up

Multiplication-Combination Tests for Incomplete Paired Data

by   Lubna Amro, et al.
The University of Texas at Dallas
Universität Ulm

We consider statistical procedures for hypothesis testing of real valued functionals of matched pairs with missing values. In order to improve the accuracy of existing methods, we propose a novel multiplication combination procedure. Dividing the observed data into dependent (completely observed) pairs and independent (incompletely observed) components, it is based on combining separate results of adequate tests for the two sub datasets. Our methods can be applied for parametric as well as semi- and nonparametric models and make efficient use of all available data. In particular, the approaches are flexible and can be used to test different hypotheses in various models of interest. This is exemplified by a detailed study of mean- as well as rank-based apporaches. Extensive simulations show that the proposed procedures are more accurate than existing competitors. A real data set illustrates the application of the methods.


page 1

page 2

page 3

page 4


New Upper Bounds in the Hypothesis Testing Problem with Information Constraints

We consider a hypothesis testing problem where a part of data cannot be ...

Asymptotic based bootstrap approach for matched pairs with missingness in a single-arm

The issue of missing values is an arising difficulty when dealing with p...

Independent additive weighted bias distributions and associated goodness-of-fit tests

We use a Stein identity to define a new class of parametric distribution...

A cautionary tale on using imputation methods for inference in matched pairs design

Imputation procedures in biomedical fields have turned into statistical ...

Frequency-Domain Stochastic Modeling of Stationary Bivariate or Complex-Valued Signals

There are three equivalent ways of representing two jointly observed rea...

Kernel Mean Embedding Based Hypothesis Tests for Comparing Spatial Point Patterns

This paper introduces an approach for detecting differences in the first...