New Minimum Chi-Square Methods in Empirical Finance

A VARIATION ON THE MINIMUM CHI - SQUARE TEST by Paul

On Empirical Likelihood and Non-parametric Pearson's Chi-square Statistic

Owen (1988) showed that the non-parametric empirical likelihood ratio under a linear constrain has an asymptotic chi-square distribution, same as in the paramet-ric case. This fact can be used to form (non-parametric, asymptotic) conndence intervals. We suggest here to use the non-parametric version of the Pearson's chi-square statistic instead of the likelihood ratio to form conndence interval...

Standardizing the Empirical Distribution Function Yields the Chi-Square Statistic

Standardizing the empirical distribution function yields a statistic with norm square that matches the chi-square test statistic. To show this one may use the covariance matrix of the empirical distribution which, at any finite set of points, is shown to have an inverse which is tridiagonal. Moreover, a representation of the inverse is given which is a product of bidiagonal matrices correspondi...

Chi-square lower bounds

The information inequality has been shown to be an effective tool for providing lower bounds for the minimax risk. Bounds based on the chisquare distance can sometimes offer a considerable improvement especially when applied iteratively. This paper compares these two methods in four examples including the bounded normal mean problem as well as obervations from a Poisson distribution.

The Chi Square Test

The Chi square test is a statistical test which measures the association between two categorical variables. A working knowledge of tests of this nature are important for the chiropractor and osteopath in order to be able to critically appraise the literature.