Efficient statistical inference procedures for partially nonlinear models and their applications.
Motivated by an analysis of a real data set in ecology, we consider a class of partially nonlinear models where both a nonparametric component and a parametric component are present. We develop two new estimation procedures to estimate the parameters in the parametric component. Consistency and asymptotic normality of the resulting estimators are established. We further propose an estimation procedure and a generalized F-test procedure for the nonparametric component in the partially nonlinear models. Asymptotic properties of the newly proposed estimation procedure and the test statistic are derived. Finite sample performance of the proposed inference procedures are assessed by Monte Carlo simulation studies. An application in ecology is used to illustrate the proposed methods.
Department of Statistics and The Methodology Center, The Pennsylvania State University, University Park, Pennsylvania 16802, U.S.A. email:rli@stat.psu.edu.
Runze
R
Li
Department of Biostatistics, Bioinformatiocs, and Biomathematics, Georgetown University, Washington DC20057, U.S.A. email:ln54@georgetown.edu.
Lei
L
Nie
D001699:Biometry / Q000379:methods*; D003627:Data Interpretation, Statistical; D004463:Ecology / Q000706:statistics & numerical data; D017753:Ecosystem; D020342:Finite Element Analysis; D016018:Least-Squares Analysis; D016014:Linear Models; D015233:Models, Statistical; D009010:Monte Carlo Method; D017711:Nonlinear Dynamics; D010788:Photosynthesis; D018709:Statistics, Nonparametric