Results 231 to 240 of about 430,365 (280)
Some of the next articles are maybe not open access.
Biometrics, 1970
SUMMARY Goodman's work on quasi-independence and missing values for contingency tables is unified so as to cover in general incompleteness, as here defined. Cyclic computational procedures are provided for maximum likelihood fitting of iincomplete data.
openaire +1 more source
SUMMARY Goodman's work on quasi-independence and missing values for contingency tables is unified so as to cover in general incompleteness, as here defined. Cyclic computational procedures are provided for maximum likelihood fitting of iincomplete data.
openaire +1 more source
Visualizing Contingency Tables
2007Categorical data analysis is typically based on two- or higher dimensional contingency tables, cross-tabulating the co-occurrences of levels of nominal and/or ordinal data. In order to explain these, statisticians typically look for (conditional) independence structures using common methods such as independence tests and log-linear models.
Meyer, David +2 more
openaire +2 more sources
Full Contingency Tables, Logits, and Split Contingency Tables
Biometrics, 1969Three methods of fitting log-linear models to multivariate contingency-table data with one dichotomous variable are discussed. Logit analysis is commonly used when a full contingency table of s dimensions is regarded as a table of rates of dimension s -1. The split-table method treats the same data as two separate tables each of dimension s -1. We show
openaire +1 more source
An efficient charting scheme for multivariate categorical process with a sparse contingency table
Journal of Quality Technology, 2021Dongdong Xiang
exaly
Multi-dimensional Contingency Tables
1990In chapter 5, log-linear models for three-dimensional tables were treated in great details. Hence we shall not for higher order tables go into details with the parameterizations of the models or with the exact expressions for test quantities and their distributions.
openaire +1 more source
1990
Consider a three-way contingency table {Xijk, i=1,...I, j=1,...,J, k=1,...,K}. As model for such data, it may be assumed that the x’s are observed values of random variables Xijk, i=1,...,I, j=1,...,J, k=1,...,K with a multinomial distribution $${X_{111}},...,{X_{IJK}} \sim M(n,{p_{111}},...,{p_{IJK}}).$$ (5.1)
openaire +1 more source
Consider a three-way contingency table {Xijk, i=1,...I, j=1,...,J, k=1,...,K}. As model for such data, it may be assumed that the x’s are observed values of random variables Xijk, i=1,...,I, j=1,...,J, k=1,...,K with a multinomial distribution $${X_{111}},...,{X_{IJK}} \sim M(n,{p_{111}},...,{p_{IJK}}).$$ (5.1)
openaire +1 more source