Empirical Bayes analysis of log-linear models for a generalized finite stationary Markov chain

Metrika ◽  
2004 ◽  
Vol 59 (2) ◽  
pp. 173-191 ◽  
Author(s):  
Mohammad R. Meshkani ◽  
Farzad Eskandari
Keyword(s):  
Markov Chain ◽  
Empirical Bayes ◽  
Linear Models ◽  
Stationary Markov Chain ◽  
Bayes Analysis ◽  
Log Linear

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

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

Asymptotic behaviour of sample weighted circuits representing recurrent Markov chains

1990 ◽  
Vol 27 (03) ◽  
pp. 545-556 ◽  
Author(s):  
S. Kalpazidou
Keyword(s):  
Markov Chain ◽  
Markov Chains ◽  
Asymptotic Behaviour ◽  
Probabilistic Interpretation ◽  
Stationary Markov Chain

The asymptotic behaviour of the sequence (𝒞 n (ω), wc,n (ω)/n), is studied where 𝒞 n (ω) is the class of all cycles c occurring along the trajectory ωof a recurrent strictly stationary Markov chain (ξ n ) until time n and wc,n (ω) is the number of occurrences of the cycle c until time n. The previous sequence of sample weighted classes converges almost surely to a class of directed weighted cycles (𝒞∞, ω c ) which represents uniquely the chain (ξ n ) as a circuit chain, and ω c is given a probabilistic interpretation.

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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