NONLINEAR REGRESSION MODELING VIA REGULARIZED GAUSSIAN BASIS FUNCTIONS
スポンサーリンク
概要
- 論文の詳細を見る
Nonlinear regression modeling based on basis expansions has been widely used to explore data with complex structure. There are various types of basis functions to capture complex nonlinear phenomena. In this paper we introduce nonlinear regression models with Gaussian basis functions, for which new Gaussian bases are constructed, taking advantages of $ B $-spline bases. In order to choose adjusted parameters, we derive model selection and evaluation criteria from information-theoretic and Bayesian viewpoints. Monte Carlo simulations and real data analysis show that our proposed modeling strategy performs well in various situations.
論文 | ランダム
- 磯前順一著『土偶と仮面』
- ケインズ--人と業績 (特集 よみがえるケインズ)
- 「北欧神話・宇宙論の基礎構造--『巫女の予言』の秘文を解く」尾崎和彦
- 「イギリスの経済的衰退」観をめぐって--覇権国,国民経済,経済主体の混成物
- 神話から民話へ--「小さなグウィヨン物語」と「タリエシン物語」における印欧語族神話要素