scholarly journals Assessing Bayesian Semi‐Parametric Log‐Linear Models: An Application to Disclosure Risk Estimation

2021 ◽  
Author(s):  
Cinzia Carota ◽  
Maurizio Filippone ◽  
Silvia Polettini
Keyword(s):  
Linear Models ◽  
Risk Estimation ◽  
Disclosure Risk ◽  
Log Linear

scholarly journals Bayesian nonparametric disclosure risk estimation via mixed effects log-linear models

2015 ◽  
Vol 9 (1) ◽  
pp. 525-546 ◽  
Author(s):  
Cinzia Carota ◽  
Maurizio Filippone ◽  
Roberto Leombruni ◽  
Silvia Polettini
Keyword(s):  
Linear Models ◽  
Risk Estimation ◽  
Mixed Effects ◽  
Bayesian Nonparametric ◽  
Disclosure Risk ◽  
Log Linear

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).

Polymicrobial Sepsis: An Analysis of 184 Cases Using Log Linear Models

1980 ◽  
Vol 280 (2) ◽  
pp. 73-80 ◽  
Author(s):  
Philip A. Mackowiak ◽  
Richard H. Browne ◽  
Paul M. Southern ◽  
James W. Smith
Keyword(s):  
Linear Models ◽  
Polymicrobial Sepsis ◽  
Log Linear
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