Showing 1 - 10 of 247
A multiple-sample semiparametric density ratio model, which is equivalent to a generalized logistic regression model … nonparametric.The combined data from all the samples are used in the semiparametric large sample problem of estimating each … class of semiparametric models to time series. The method combines information from several time series and estimates their …
Persistent link: https://www.econbiz.de/10009450972
There are two topics in this dissertation. The first topic is 'Smoothing Parameter Selection in Nonparametric Generalized Linear Models via Sixth-order Laplace Approximation' and the second topic is 'Smoothing Spline-based Score Tests for Proportional Hazards Models'.We present a new approach...
Persistent link: https://www.econbiz.de/10009431282
In many scientific studies, the response variable bears a generalized nonlinear regression relationship with a certain covariate of interest, which may, however, be confounded by other covariates with unknown functional form. We propose a new class of models, the partly parametric generalized...
Persistent link: https://www.econbiz.de/10009466085
semiparametric statistical methods. This article describes the new Stata command haplologit, which implements efficient profile …-likelihood semiparametric methods for fitting gene-environment models in the very important special cases of a rare disease, a single candidate …
Persistent link: https://www.econbiz.de/10005583263
In this paper, we consider three major types of nonparametric regression tests that are based on kernel and local polynomial smoothing techniques. Their asymptotic power comparisons are established systematically under the fixed and contiguous alternatives, and are also illustrated through...
Persistent link: https://www.econbiz.de/10010306282
In this work, the authors propose a novel method called online variable kernel estimation of the probability density function (pdf). This new online estimator combines the characteristics and properties of two estimators namely nearest neighbors estimator and the Parzen-Rosenblatt estimator....
Persistent link: https://www.econbiz.de/10012046926
Purpose: The purpose of this paper is to propose a mathematical model for the prediction of railway freight volume, and therefore provide railway freight resource allocation with an accurate direction. With an accurate railway freight volume prediction, railway freight enterprises can integrate...
Persistent link: https://www.econbiz.de/10011939126
Summary The HP filter is the most popular filter for extracting the unobserved trend and cycle components from a time series. Many researchers consider the smoothing parameter λ = 1600 as something like a universal constant. It is well known that the HP filter is an optimal filter under some...
Persistent link: https://www.econbiz.de/10014609545
Nonparametric density estimation is of great importance when econometricians want to model the probabilistic or stochastic structure of a data set. This comprehensive review summarizes the most important theoretical aspects of kernel density estimation and provides an extensive description of...
Persistent link: https://www.econbiz.de/10012610946
The HP filter is the most popular filter for extracting the trend and cycle components from an observed time series. Many researchers consider the smoothing parameter lambda = 1600 as something like an universal constant. It is well known that the HP filter is an optimal filter under some...
Persistent link: https://www.econbiz.de/10010281937