Goodness-of-fit Testing of Error Distribution in Nonparametric ARCH(1) Models and Linear Measurement Error Models - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Goodness-of-fit Testing of Error Distribution in Nonparametric ARCH(1) Models and Linear Measurement Error Models write by Xiaoqing Zhu. This book was released on 2015. Goodness-of-fit Testing of Error Distribution in Nonparametric ARCH(1) Models and Linear Measurement Error Models available in PDF, EPUB and Kindle.
Nonparametric Goodness-of-Fit Testing Under Gaussian Models
Nonparametric Goodness-of-Fit Testing Under Gaussian Models - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Nonparametric Goodness-of-Fit Testing Under Gaussian Models write by Yu. I. Ingster. This book was released on 2003. Nonparametric Goodness-of-Fit Testing Under Gaussian Models available in PDF, EPUB and Kindle. This book presents the modern theory of nonparametric goodness-of-fit testing. It fills the gap in modern nonparametric statistical theory by discussing hypothesis testing and addresses mathematical statisticians who are interesting in the theory of non-parametric statistical inference. It will be of interest to specialists who are dealing with applied non-parametric statistical problems relevant in signal detection and transmission and in technical and medical diagnostics among others.
Measurement Error in Nonlinear Models
Measurement Error in Nonlinear Models - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Measurement Error in Nonlinear Models write by Raymond J. Carroll. This book was released on 1995-07-06. Measurement Error in Nonlinear Models available in PDF, EPUB and Kindle. This monograph provides an up-to-date discussion of analysis strategies for regression problems in which predictor variables are measured with errors. The analysis of nonlinear regression models includes generalized linear models, transform-both-sides models and quasilikelihood and variance function problems. The text concentrates on the general ideas and strategies of estimation and inference rather than being concerned with a specific problem. Measurement error occurs in many fields, such as biometry, epidemiology and economics. In particular, the book contains a large number of epidemiological examples. An outline of strategies for handling progressively more difficult problems is also provided.
Measurement Error in Nonlinear Models
Measurement Error in Nonlinear Models - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Measurement Error in Nonlinear Models write by Raymond J. Carroll. This book was released on 2006-06-21. Measurement Error in Nonlinear Models available in PDF, EPUB and Kindle. It's been over a decade since the first edition of Measurement Error in Nonlinear Models splashed onto the scene, and research in the field has certainly not cooled in the interim. In fact, quite the opposite has occurred. As a result, Measurement Error in Nonlinear Models: A Modern Perspective, Second Edition has been revamped and ex
Goodness-of-fit Tests in Measurement Error Models with Replications
Goodness-of-fit Tests in Measurement Error Models with Replications - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Goodness-of-fit Tests in Measurement Error Models with Replications write by Weijia Jia. This book was released on 2018. Goodness-of-fit Tests in Measurement Error Models with Replications available in PDF, EPUB and Kindle. In this dissertation, goodness-of-fit tests are proposed for checking the adequacy of parametric distributional forms of the regression error density functions and the error-prone predictor density function in measurement error models, when replications of the surrogates of the latent variables are available. In the first project, we propose goodness-of-fit tests on the density function of the regression error in the errors-in-variables model. Instead of assuming that the distribution of the measurement error is known as is done in most relevant literature, we assume that replications of the surrogates of the latent variables are available. The test statistic is based upon a weighted integrated squared distance between a nonparametric estimate and a semi-parametric estimate of the density functions of certain residuals. Under the null hypothesis, the test statistic is shown to be asymptotically normal. Consistency and local power results of the proposed test under fixed alternatives and local alternatives are also established. Finite sample performance of the proposed test is evaluated via simulation studies. A real data example is also included to demonstrate the application of the proposed test. In the second project, we propose a class of goodness-of-fit tests for checking the parametric distributional forms of the error-prone random variables in the classic additive measurement error models. We also assume that replications of the surrogates of the error-prone variables are available. The test statistic is based upon a weighted integrated squared distance between a non-parametric estimator and a semi-parametric estimator of the density functions of the averaged surrogate data. Under the null hypothesis, the minimum distance estimator of the distribution parameters and the test statistics are shown to be asymptotically normal. Consistency and local power of the proposed tests under fixed alternatives and local alternatives are also established. Finite sample performance of the proposed tests is evaluated via simulation studies.
