Inferences with generalized partially linear single-index models for longitudinal data
Inferences with generalized partially linear single-index models for longitudinal data is a scholarly work, published in 2019 in ''Journal of Statistical Planning and Inference''. The main subjects of the publication include dependent and independent variables, Single-index model, non-parametric statistics, Monte Carlo method, Estimating equations, categorical variable, discrete choice, semiparametric model, generalized linear model, statistics, missing data, spatial econometrics, manicule, linear model, estimator, applied mathematics, mathematics, and generalized linear mixed model. The authors propose a method to efficiently estimate both the parameters and the nonparametric single-index function in generalized partially linear single-index models when subjects are observed or measured over time.