We compare parametric and nonparametric estimation methods in the context of PBPK modeling using simulation studies. We implement a Monte Carlo Markov Chain simulation technique in the parametric method, and a functional analytical approach to estimate the probability distribution function directly in the nonparametric method. The simulation results suggest an advantage for the parametric metho...

the frequency response analysis (fra) test has been recognized as one of the sensitive tools available for detecting electrical and mechanical faults inside power transformers. however, there is still no universally systematic interpretation technique for these tests. many research efforts have employed different statistical criteria in order to aid the interpretative capability of the fra, but...

Additive models with backfitting algorithms are popular multivariate nonparametric fitting techniques. However, the inferences of the models have not been very well developed, due partially to the complexity of the backfitting estimators. There are few tools available to answer some important and frequently asked questions, such as whether a specific additive component is significant or admits ...

In practice, parametric likelihood-ratio techniques are powerful statistical tools. In this article, we propose and examine novel and simple distribution-free test statistics that efficiently approximate parametric likelihood ratios to analyze and compare distributions of K groups of observations. Using the density-based empirical likelihood methodology, we develop a Stata package that applies ...

Behavioural studies are commonly plagued with data that violate the assumptions of parametric statistics. Consequently, classic nonparametric methods (e.g. rank tests) and novel distribution-free methods (e.g. randomization tests) have been used to a great extent by behaviourists. However, the robustness of such methods in terms of statistical power and type I error have seldom been evaluated. ...

In this paper, a new adaptive control law combining nonparametric and parametric estimators is proposed to control stochastic d-dimensional discrete-time nonlinear models of the form Xn+1 = f(Xn) + Un + εn+1. The unknown function f is assumed to be parametric outside a given domain of Rd and fully nonparametric inside. The nonparametric part of f is estimated using a kernel-based method and the...

We study the Cox models with semiparametric relative risk, which can be partially linear with one nonparametric component, or multiple additive or nonadditive nonparametric components. A penalized partial likelihood procedure is proposed to simultaneously estimate the parameters and select variables for both the parametric and the nonparametric parts. Two penalties are applied sequentially. The...

In this paper, we propose a new diagnostic test for residual cross–section independence in a nonparametric panel data model. The proposed nonparametric cross–section dependence (CD) test is a nonparametric counterpart of an existing parametric CD test proposed in Pesaren (2004) for the parametric case. We establish an asymptotic distribution of the proposed test statistic under the null hypothe...

This paper addresses much needed asymptotic and nonparametric bootstrap methodology for two-sample tests for means on Riemannian manifolds with a simply transitive group of isometries. In particular, we develop a two-sample procedure for testing the equality of the generalized Frobenius means of two independent populations on the space of symmetric positive matrices. The new method naturally le...