INTERACTION DETECTION FOR GENERALIZED LINEAR MODELS
First Claim
1. A method, comprising:
- calculating, using a computer, basic statistics for a pair of categorical predictor variables and a target variable from a dataset during a single pass over the dataset; and
determining, using the computer, whether there is a significant interaction effect for the pair of categorical predictor variables on the target variable by;
calculating, using the computer, a log-likelihood value for a full generalized linear model without estimating model parameters;
calculating, using the computer, the model parameters for a reduced generalized linear model with a recursive marginal mean accumulation technique using the basic statistics;
calculating, using the computer, a log-likelihood value for the reduced generalized linear model;
calculating, using the computer, a likelihood ratio test statistic using the log-likelihood value for the full generalized linear model and the log-likelihood value for the reduced generalized linear model;
calculating, using the computer, a p-value of the likelihood ratio test statistic; and
comparing, using the computer, the p-value to a significance level.
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Abstract
Provided are techniques for interaction detection for generalized linear models. Basic statistics are calculated for a pair of categorical predictor variables and a target variable from a dataset during a single pass over the dataset. It is determined whether there is a significant interaction effect for the pair of categorical predictor variables on the target variable by: calculating a log-likelihood value for a full generalized linear model without estimating model parameters; calculating the model parameters for a reduced generalized linear model with a recursive marginal mean accumulation technique using the basic statistics; calculating a log-likelihood value for the reduced generalized linear model; calculating a likelihood ratio test statistic using the log-likelihood value for the full generalized linear model and the log-likelihood value for the reduced generalized linear model; calculating a p-value of the likelihood ratio test statistic; and comparing the p-value to a significance level.
26 Citations
8 Claims
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1. A method, comprising:
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calculating, using a computer, basic statistics for a pair of categorical predictor variables and a target variable from a dataset during a single pass over the dataset; and determining, using the computer, whether there is a significant interaction effect for the pair of categorical predictor variables on the target variable by; calculating, using the computer, a log-likelihood value for a full generalized linear model without estimating model parameters; calculating, using the computer, the model parameters for a reduced generalized linear model with a recursive marginal mean accumulation technique using the basic statistics; calculating, using the computer, a log-likelihood value for the reduced generalized linear model; calculating, using the computer, a likelihood ratio test statistic using the log-likelihood value for the full generalized linear model and the log-likelihood value for the reduced generalized linear model; calculating, using the computer, a p-value of the likelihood ratio test statistic; and comparing, using the computer, the p-value to a significance level. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8-21. -21. (canceled)
Specification