title: Variable selection for binary spatial regression: Penalized quasi-likelihood approach.
abstract: We consider the problem of selecting covariates in a spatial regression model when the response is binary. Penalized likelihood-based approach is proved to be effective for both variable selection and estimation simultaneously. In the context of a spatially dependent binary variable, an uniquely interpretable likelihood is not available, rather a quasi-likelihood might be more suitable. We develop a penalized quasi-likelihood with spatial dependence for simultaneous variable selection and parameter estimation along with an efficient computational algorithm. The theoretical properties including asymptotic normality and consistency are studied under increasing domain asymptotics framework. An extensive simulation study is conducted to validate the methodology. Real data examples are provided for illustration and applicability. Although theoretical justification has not been made, we also investigate empirical performance of the proposed penalized quasi-likelihood approach for spatial count data to explore suitability of this method to a general exponential family of distributions.
authors.affiliation: American Express, New York, U.S.A.
authors.forename: Wenning
authors.initials: W
authors.lastname: Feng
authors.affiliation: Michigan State University, East Lansing, Michigan 48824, U.S.A.
authors.forename: Abdhi
authors.initials: A
authors.lastname: Sarkar
authors.affiliation: Seoul National University, Seoul, Korea.
authors.forename: Chae Young
authors.initials: CY
authors.lastname: Lim
authors.affiliation: Michigan State University, East Lansing, Michigan 48824, U.S.A.
authors.forename: Tapabrata
authors.initials: T
authors.lastname: Maiti
mesh_terms: D000465:Algorithms; D001699:Biometry / Q000379:methods; D003198:Computer Simulation; D005390:Fires / Q000706:statistics & numerical data; D016013:Likelihood Functions; D008824:Michigan; D015233:Models, Statistical; D000068916:Spatial Regression
keywords: Binary response; Increasing domain asymptotics; LASSO; MM algorithm; Penalized quasi-likelihood; SCAD; Spatial regression; Variable selection