Abstract
In this study, we use the log-linear link function and propose a generalized fused Lasso (GFL) Poisson regression model in which the nonlinear trend is discretely represented by categorical covariates in the additive model. We use the coordinate descent algorithm for the estimation and show that the optimal solution in a coordinate axis can be found explicitly. To demonstrate the proposed approach, we analyze Japanese crime data. Simulation results showed a fitness ratio for true fusion to be more than 90% in total, demonstrating the reliability of the estimates.
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