The possible discrepancy between a hypothesized model and the observed data is measured by so called Goodness of Fit Statistics. In order to decide whether the observed discrepancy is substantial, the distributions of these statistics under the hypothesised model are needed to perform a statistical test. Because of the difficulty to compute the exact distributions, just when the sample size is small, better approximations than provided by common asymptotic theory have to be found. In the case of a loglinear Poisson model we will do that by different bootstrap methods
Abstract _ Random coefficient regressions have been applied in a wide range of fields, from biology ...
An axiomatic approach is used to develop a one-parameter family of measures of divergence between di...
<p>Differences in log-likelihood per base between the fitted model and the empirical distribution, a...
The possible discrepancy between a hypothesized model and the observed data is measured by so called...
Bivariate count data arise in several different disciplines and the bivariate Poisson distribution i...
We are studying a novel class of goodness-of-fit tests for parametric count time series regression m...
This paper utilizes the bootstrap to construct tests using the measures for goodness-of-fit for nonn...
Bootstrap tests are tests for which the significance level is calculated using some variant of the b...
We introduce a new concept of nonparametric test for statistically deciding if a model fits a sample...
A common question in the analysis of binary data is how to deal with overdispersion. One widely advo...
grantor: University of TorontoThe statistical analysis of dichotomous outcome variables is...
Goodness-of-fit is a very important concept in data analysis, as most statistical models make some u...
Random coefficient regressions have been applied in a wide range of fields, from biology to economic...
This manuscript overviews exact testing of goodness of fit for log-linear models using the R package...
In this paper, we propose a procedure for reducing the uncertainty about mortality projections, on t...
Abstract _ Random coefficient regressions have been applied in a wide range of fields, from biology ...
An axiomatic approach is used to develop a one-parameter family of measures of divergence between di...
<p>Differences in log-likelihood per base between the fitted model and the empirical distribution, a...
The possible discrepancy between a hypothesized model and the observed data is measured by so called...
Bivariate count data arise in several different disciplines and the bivariate Poisson distribution i...
We are studying a novel class of goodness-of-fit tests for parametric count time series regression m...
This paper utilizes the bootstrap to construct tests using the measures for goodness-of-fit for nonn...
Bootstrap tests are tests for which the significance level is calculated using some variant of the b...
We introduce a new concept of nonparametric test for statistically deciding if a model fits a sample...
A common question in the analysis of binary data is how to deal with overdispersion. One widely advo...
grantor: University of TorontoThe statistical analysis of dichotomous outcome variables is...
Goodness-of-fit is a very important concept in data analysis, as most statistical models make some u...
Random coefficient regressions have been applied in a wide range of fields, from biology to economic...
This manuscript overviews exact testing of goodness of fit for log-linear models using the R package...
In this paper, we propose a procedure for reducing the uncertainty about mortality projections, on t...
Abstract _ Random coefficient regressions have been applied in a wide range of fields, from biology ...
An axiomatic approach is used to develop a one-parameter family of measures of divergence between di...
<p>Differences in log-likelihood per base between the fitted model and the empirical distribution, a...