A Simulation-Based Comparison Between Parametric and Nonparametric Estimation Methods in PBPK Models PDF Download
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Author: Publisher: ISBN: Category : Languages : en Pages : 31
Book Description
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 method when the underlying model can capture the true population distribution. On the other hand, our calculations demonstrate some advantages for a nonparametric approach since it is a more cautious (and hence safer) way to assess the distribution when one does not have sufficient knowledge to assume a population distribution form or parametrization. The parametric approach has obvious advantages when one has significant a priori information on the distributions sought, although when used in the nonparametric method, prior information can also significantly facilitate estimation.
Author: Publisher: ISBN: Category : Languages : en Pages : 31
Book Description
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 method when the underlying model can capture the true population distribution. On the other hand, our calculations demonstrate some advantages for a nonparametric approach since it is a more cautious (and hence safer) way to assess the distribution when one does not have sufficient knowledge to assume a population distribution form or parametrization. The parametric approach has obvious advantages when one has significant a priori information on the distributions sought, although when used in the nonparametric method, prior information can also significantly facilitate estimation.
Author: James R. Thompson Publisher: SIAM ISBN: 9781611971712 Category : Mathematics Languages : en Pages : 320
Book Description
Topics emphasized include nonparametric density estimation as an exploratory device plus the deeper models to which the exploratory analysis points, multi-dimensional data analysis, and analysis of remote sensing data, cancer progression, chaos theory, epidemiological modeling, and parallel based algorithms. New methods discussed are quick nonparametric density estimation based techniques for resampling and simulation based estimation techniques not requiring closed form solutions.
Author: Sam Efromovich Publisher: Springer Science & Business Media ISBN: 0387987401 Category : Mathematics Languages : en Pages : 423
Book Description
This book gives a systematic, comprehensive, and unified account of modern nonparametric statistics of density estimation, nonparametric regression, filtering signals, and time series analysis. The companion software package, available over the Internet, brings all of the discussed topics into the realm of interactive research. Virtually every claim and development mentioned in the book is illustrated with graphs which are available for the reader to reproduce and modify, making the material fully transparent and allowing for complete interactivity.
Author: Harvey Thomas Banks Publisher: ISBN: Category : Inverse problems (Differential equations) Languages : en Pages : 42
Book Description
We report progress on the development of methods in a number of specific areas of application including static, non-cooperative games related to counter- and counter-counter-electromagnetic interrogation of targets, modeling of complex viscoelastic polymeric materials, stochastic and deterministic models for complex networks and development of inverse problem methodologies (generalized sensitivity functions; asymptotic standard errors) for estimation of infinite dimensional functional parameters including probability measures and temporal/spatial dependent functions in complex nonlinear dynamical systems. These efforts are part of our fundamental research in a modeling, estimation and control methodology (theoretical, statistical and computational) for systems in the presence of major model and observation uncertainties.
Author: Alexandre B. Tsybakov Publisher: Springer Science & Business Media ISBN: 0387790527 Category : Mathematics Languages : en Pages : 222
Book Description
Developed from lecture notes and ready to be used for a course on the graduate level, this concise text aims to introduce the fundamental concepts of nonparametric estimation theory while maintaining the exposition suitable for a first approach in the field.
Author: Stefan Sperlich Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
This article gives the asymptotic properties for nonparametric kernel based density and regression estimators when one of the variables, respectively regressors, had to be pre-estimated. Those variables are known as constructed variables or generatedregressors, and their impact on the -nal estimator is well studied in the fully para-metric context. The problem of making inference based on predicted rather than on observed values is quite frequent in econometrics and applied economics. The results are derived in such a way that the pre-estimation steps could be performed by any con-sistent nonparametric or parametric method. The case of parametric estimation with nonparametric predictors is discussed, as well. In most cases it is obvious and mathematically straightforward how to extend the results to semiparametric models or to other nonparametric smoothing methods. We also study the performance of nonparametric estimators with constructed variables by simulations and compare the numerical to our theoretical results.
Author: Zhibiao Zhao Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
We study non-parametric regression function estimation for models with strong dependence. Compared with short-range dependent models, long-range dependent models often result in slower convergence rates. We propose a simple differencing-sequence based non-parametric estimator that achieves the same convergence rate as if the data were independent. Simulation studies show that the proposed method has good finite sample performance.
Author: Jiti Gao Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
In this paper, we consider both estimation and testing problems in a nonlinear time series model with nonstationarity. A nonparametric estimation method is proposed to estimate a sequence of nonparametric “distance functions”. We also propose a test statistic to test whether the regression function is of a known parametric nonlinear form. The power function of the proposed nonparametric test is studied and an asymptotic distribution of the test statistic is shown to depend on the asymptotic behavior of the “distance function” under a sequence of general semiparametric local alternatives. The asymptotic theory developed in this paper differs from existing work on nonparametric estimation and specification testing in the stationary time series case. In order to implement the proposed test in practice, a computer-intensive bootstrap simulation procedure is proposed and asymptotic approximations for both the size and power functions are established. Furthermore, the bandwidth involved in the test statistic is selected by maximizing the power function while the size function is controlled by a significance level. Meanwhile, both simulated and real data examples are provided to illustrate the proposed approach.
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Author: James R. Thompson
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Author: Sam Efromovich
Author: Harvey Thomas Banks
Author: Alexandre B. Tsybakov
Author: Stefan Sperlich
Author: Zhibiao Zhao
Author: Jiti Gao
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