A note on improved likelihood ratio statistics for generalized log linear models

Biometrika ◽  
1985 ◽  
Vol 72 (2) ◽  
pp. 473-475 ◽  
Author(s):  
B. T. PORTEOUS
Keyword(s):  
Likelihood Ratio ◽  
Linear Models ◽  
Log Linear ◽  
Likelihood Ratio Statistics

Asymptotic Log-Linear Analysis: Some Cautions concerning Sparse Frequency Tables

2004 ◽  
Vol 94 (1) ◽  
pp. 19-32 ◽  
Author(s):  
Paul W. Mielke ◽  
Kenneth J. Berry ◽  
Janis E. Johnston
Keyword(s):  
Likelihood Ratio ◽  
Linear Models ◽  
Linear Analysis ◽  
Exact Probability ◽  
Permutation Methods ◽  
Chi Squared ◽  
Asymptotic Probability ◽  
Log Linear ◽  
Likelihood Ratio Statistics ◽  
Hypergeometric Distributions

Traditional asymptotic probability values resulting from log-linear analyses of sparse frequency tables are often much too large. Asymptotic probability values for chi-squared and likelihood-ratio statistics are compared to nonasymptotic and exact probability values for selected log-linear models. The asymptotic probability values are all too often substantially larger than the exact probability values for the analysis of sparse frequency tables. An exact nondirectional permutation method is presented to analyze combined independent multinomial distributions. Exact nondirectional permutation methods to analyze hypergeometric distributions associated with r-way frequency tables are confined to r = 2.

When and why are log-linear models self-normalizing?

2015 ◽  
Author(s):  
Jacob Andreas ◽  
Dan Klein
Keyword(s):  
Linear Models ◽  
Log Linear

Log-Linear Models with Dependent Spatial Data

1983 ◽  
Vol 15 (6) ◽  
pp. 801-813 ◽  
Author(s):  
B Fingleton
Keyword(s):  
Spatial Data ◽  
Statistical Models ◽  
Linear Models ◽  
Square Lattice ◽  
Categorical Variables ◽  
Systematic Sampling ◽  
Pairwise Correlations ◽  
Independent Observations ◽  
Intersection Points ◽  
Log Linear

Log-linear models are an appropriate means of determining the magnitude and direction of interactions between categorical variables that in common with other statistical models assume independent observations. Spatial data are often dependent rather than independent and thus the analysis of spatial data by log-linear models may erroneously detect interactions between variables that are spurious and are the consequence of pairwise correlations between observations. A procedure is described in this paper to accommodate these effects that requires only very minimal assumptions about the nature of the autocorrelation process given systematic sampling at intersection points on a square lattice.

Educational Homogamy Among Married and Unmarried Couples in Europe

2008 ◽  
Vol 30 (1) ◽  
pp. 28-52 ◽  
Author(s):  
Dana Hamplova
Keyword(s):  
Linear Models ◽  
Married Couples ◽  
European Social Survey ◽  
Social Survey ◽  
Educational Homogamy ◽  
Educational Barriers ◽  
Educational Groups ◽  
Institutional Contexts ◽  
Using Data ◽  
Log Linear

In this article, educational homogamy among married and cohabiting couples in selected European countries is examined. Using data from two waves (2002 and 2004) of the European Social Survey, this article compares three cultural and institutional contexts that differ in terms of institutionalization of cohabitation. Evidence from log-linear models yields two main conclusions. First, as cohabitation becomes more common in society, marriage and cohabitation become more similar with respect to partner selection. Second, where married and unmarried unions differ in terms of educational homogamy, married couples have higher odds of overcoming educational barriers (i.e., intermarrying with other educational groups).

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