Log-Linear Modeling Algorithm

A Log-Linear Modeling Algorithm is a correlational modeling algorithm that can be implemented by a Log-Linear Analysis System (that solves a log-linear analysis task).

• AKA: Loglinear Analysis.
• Context:
  • ...
• Counter-Example(s):
  • a Linear Analysis Algorithm.
• See: Pearson's Chi-Square Test, Statistical Hypothesis Testing, Log-Linear Model.

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References

2015

• (Wikipedia, 2015) ⇒ http://en.wikipedia.org/wiki/log-linear_analysis Retrieved:2015-5-1.
  • Log-linear analysis is a technique used in statistics to examine the relationship between more than two categorical variables. The technique is used for both hypothesis testing and model building. In both these uses, models are tested to find the most parsimonious (i.e., least complex) model that best accounts for the variance in the observed frequencies. (A Pearson's chi-square test could be used instead of log-linear analysis, but that technique only allows for two of the variables to be compared at a time.)

2010

• (Agarwal et al., 2010) ⇒ Deepak Agarwal, Rahul Agrawal, Rajiv Khanna, and Nagaraj Kota. (2010). "Estimating Rates of Rare Events with Multiple Hierarchies through Scalable Log-linear Models." In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2010). doi:10.1145/1835804.1835834
  • QUOTE: We consider the problem of estimating rates of rare events for high dimensional, multivariate categorical data where several dimensions are hierarchical. ... We propose LMMH, a novel log-linear modeling method that scales to massive data applications with billions of training records and several million potential predictors in a map-reduce framework.

1994

• (Bakeman & Robinson, 1994) => Roger Bakeman, and Byron F. Robinson. (1994). {Understanding Log-Linear Analysis with ILOG: An interactive approach." Psychology Press.

1981

• (Laird, 1981) & rArr; Nan Laird, and Donald Olivier. (1981). "Covariance Analysis of Censored Survival Data Using Log-linear Analysis Techniques." In: Journal of the American Statistical Association, 76(374).

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