We address the problem of maximally selected chi-square statistics in the case of a binary Y variable and a nominal X variable with several categories. The distribution of the maximally selected chi-square statistic has already been derived when the best cutpoint is chosen from a continuous or an ordinal X, but not when the best split is chosen from a nominal X. In this paper, we derive the exact distribution of the maximally selected chi-square statistic in this case using a combinatorial approach. Applications of the derived distribution to variable selection and hypothesis testing are discussed based on simulations. As an illustration, our method is applied to a pregnancy and birth data set
Zero-inflated distributions are common in statistical problems where there is interest in testing ho...
Pearson's chi-square test is widely employed in social and health sciences to analyse categorical da...
Pearson's chi-square test is widely employed in social and health sciences to analyse categorical da...
We address the problem of maximally selected chi-square statistics in the case of a binary Y variabl...
The association between a binary variable Y and a variable X with an at least ordinal measurement sc...
Binary outcomes that depend on an ordinal predictor in a non-monotonic way are common in medical dat...
The Gini gain is one of the most common variable selection criteria in machine learning. We derive t...
Chi-square test and the logic of hypothesis testing were developed by Karl Pearson. In this article ...
The Chi-square statistic is a non-parametric (distribution free) tool designed to analyze group diff...
The maximally selected statistic approach in building tree models is shown to be a cause of variable...
The maximally selected statistic approach in building tree models is shown to be a cause of variable...
This paper proposes a method of partitioning the total chi-square statistic obtained for matched dic...
It is well known that the Pearson statistic χ2 can perform poorly in studying the association be...
Zero-inflated distributions are common in statistical problems where there is interest in testing ho...
Pearson's chi-square test is widely employed in social and health sciences to analyse categorical da...
Pearson's chi-square test is widely employed in social and health sciences to analyse categorical da...
We address the problem of maximally selected chi-square statistics in the case of a binary Y variabl...
The association between a binary variable Y and a variable X with an at least ordinal measurement sc...
Binary outcomes that depend on an ordinal predictor in a non-monotonic way are common in medical dat...
The Gini gain is one of the most common variable selection criteria in machine learning. We derive t...
Chi-square test and the logic of hypothesis testing were developed by Karl Pearson. In this article ...
The Chi-square statistic is a non-parametric (distribution free) tool designed to analyze group diff...
The maximally selected statistic approach in building tree models is shown to be a cause of variable...
The maximally selected statistic approach in building tree models is shown to be a cause of variable...
This paper proposes a method of partitioning the total chi-square statistic obtained for matched dic...
It is well known that the Pearson statistic χ2 can perform poorly in studying the association be...
Zero-inflated distributions are common in statistical problems where there is interest in testing ho...
Pearson's chi-square test is widely employed in social and health sciences to analyse categorical da...
Pearson's chi-square test is widely employed in social and health sciences to analyse categorical da...