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Asymptotic Theory For Ordinary Least Squares Estimators In Regression Models With Forecast Feedback
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Book Synopsis Asymptotic Theory for Ordinary Least Squares Estimators in Regression Models with Forecast Feedback by : Michael Mohr
Download or read book Asymptotic Theory for Ordinary Least Squares Estimators in Regression Models with Forecast Feedback written by Michael Mohr and published by . This book was released on 1990* with total page 107 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Asymptotic properties of least squares estimators in regression models with forecast feedback by : Michael Mohr
Download or read book Asymptotic properties of least squares estimators in regression models with forecast feedback written by Michael Mohr and published by . This book was released on 1989 with total page 25 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Stochastic Approximation and Optimization of Random Systems by : L. Ljung
Download or read book Stochastic Approximation and Optimization of Random Systems written by L. Ljung and published by Birkhäuser. This book was released on 2012-12-06 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt: The DMV seminar "Stochastische Approximation und Optimierung zufalliger Systeme" was held at Blaubeuren, 28. 5. -4. 6. 1989. The goal was to give an approach to theory and application of stochas tic approximation in view of optimization problems, especially in engineering systems. These notes are based on the seminar lectures. They consist of three parts: I. Foundations of stochastic approximation (H. Walk); n. Applicational aspects of stochastic approximation (G. PHug); In. Applications to adaptation :ugorithms (L. Ljung). The prerequisites for reading this book are basic knowledge in probability, mathematical statistics, optimization. We would like to thank Prof. M. Barner and Prof. G. Fischer for the or ganization of the seminar. We also thank the participants for their cooperation and our assistants and secretaries for typing the manuscript. November 1991 L. Ljung, G. PHug, H. Walk Table of contents I Foundations of stochastic approximation (H. Walk) §1 Almost sure convergence of stochastic approximation procedures 2 §2 Recursive methods for linear problems 17 §3 Stochastic optimization under stochastic constraints 22 §4 A learning model; recursive density estimation 27 §5 Invariance principles in stochastic approximation 30 §6 On the theory of large deviations 43 References for Part I 45 11 Applicational aspects of stochastic approximation (G. PHug) §7 Markovian stochastic optimization and stochastic approximation procedures 53 §8 Asymptotic distributions 71 §9 Stopping times 79 §1O Applications of stochastic approximation methods 80 References for Part II 90 III Applications to adaptation algorithms (L.
Book Synopsis An Asymptotic Theory for Weighted Least Squares with Weights Estimated by Replication by : Raymond J. Carroll
Download or read book An Asymptotic Theory for Weighted Least Squares with Weights Estimated by Replication written by Raymond J. Carroll and published by . This book was released on 1988 with total page 19 pages. Available in PDF, EPUB and Kindle. Book excerpt: This document considers a heteroscedastic linear regression model with replication. To estimate the variances, one can use the sample variances or the sample average squared errors from a regression fit. The authors study the large sample properties of these weighted least squares estimates with estimated weights when the number of replicates is small. The estimates are generally inconsistent for asymmetrically distributed data. If sample variances are used based on m replicates, the weighted least squares estimates are inconsistent for m=2 replicates even when the data are normally distributed. With between 3 and 5 replicates, the rates of convergence are slower than the usual square root of N. With m> or = 6 replicates, the effect of estimating the weights is to increase variances by (m-5)/(m-3), relative to weighted least squares estimates with known weights. (KR).
Book Synopsis Properties of Ordinary Least Squares Estimators in Regression Models with Non-spherical Disturbances by : Denzil G. Fiebig
Download or read book Properties of Ordinary Least Squares Estimators in Regression Models with Non-spherical Disturbances written by Denzil G. Fiebig and published by . This book was released on 1989 with total page 44 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Asymptotic Normality of Least Squares Estimators in Autoregressive Linear Regression Models by : B. B. van der Genugten
Download or read book Asymptotic Normality of Least Squares Estimators in Autoregressive Linear Regression Models written by B. B. van der Genugten and published by . This book was released on 1985 with total page 27 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Asymptotic Theory of Nonlinear Regression by : A. V. Ivanov
Download or read book Asymptotic Theory of Nonlinear Regression written by A. V. Ivanov and published by Springer. This book was released on 1997 with total page 344 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents up-to-date mathematical results in asymptotic theory on nonlinear regression on the basis of various asymptotic expansions of least squares, its characteristics, and its distribution functions of functionals of Least Squares Estimator. It is divided into four chapters. In Chapter 1 assertions on the probability of large deviation of normal Least Squares Estimator of regression function parameters are made. Chapter 2 indicates conditions for Least Moduli Estimator asymptotic normality. An asymptotic expansion of Least Squares Estimator as well as its distribution function are obtained and two initial terms of these asymptotic expansions are calculated. Separately, the Berry-Esseen inequality for Least Squares Estimator distribution is deduced. In the third chapter asymptotic expansions related to functionals of Least Squares Estimator are dealt with. Lastly, Chapter 4 offers a comparison of the powers of statistical tests based on Least Squares Estimators. The Appendix gives an overview of subsidiary facts and a list of principal notations. Additional background information, grouped per chapter, is presented in the Commentary section. The volume concludes with an extensive Bibliography. Audience: This book will be of interest to mathematicians and statisticians whose work involves stochastic analysis, probability theory, mathematics of engineering, mathematical modelling, systems theory or cybernetics.
Book Synopsis Asymptotic Theory for Weighted Least Squares Estimators in Aalen's Additive Risk Model by : Ian W. McKeague
Download or read book Asymptotic Theory for Weighted Least Squares Estimators in Aalen's Additive Risk Model written by Ian W. McKeague and published by . This book was released on 1987 with total page 16 pages. Available in PDF, EPUB and Kindle. Book excerpt: Let h(t/Z sub i) be the conditional hazard function for the survival time of an individual sub i given the p-dimensional covariate process Z sub i(t). This document inference for Aalen's additive risk model h(t/Z sub i)=Z sub i(t) alpha (t), where alpha is a p-vector of unknown hazard functions. The theory of counting processes is used to obtain weak convergence results for weighted least squares estimators of the hazard functions and the cumulative hazard functions based on continuous data. Results for weighted least squares estimators based on grouped data are also described. Keywords: Regression models, Biostatistics.
Book Synopsis Asymptotic Theory of the Least Squares Estimators of Sinusoidal Signal by :
Download or read book Asymptotic Theory of the Least Squares Estimators of Sinusoidal Signal written by and published by . This book was released on 1997 with total page 16 pages. Available in PDF, EPUB and Kindle. Book excerpt: The consistency and the asymptotic normality of the least squares estimators are derived of the sinusoidal model under the assumption of stationary random error. It is observed that the model does not satisfy the standard sufficient conditions of Jennrich (1969) Wu (1981) or Kundu (1991). Recently the consistency and the asymptotic normality are derived for the sinusoidal signal under the assumption of normal error (Kundu; 1993) and under the assumptions of independent and identically distributed random variables in Kundu and Mitra (1996). This paper will generalize them. Hannan (1971) also considered the similar kind of model and establish the result after making the Fourier transform of the data for one parameter model. We establish the result without making the Fourier transform of the data. We give an explicit expression of the asymptotic distribution of the multiparameter case, which is not available in the literature. Our approach is different from Hannan's approach. We do some simulations study to see the small sample properties of the two types of estimators.
Book Synopsis Asymptotic Distribution of the Bias Corrected Least Squares Estimators in Measurement Error Linear Regression Models Under Long Memory by : Hira Koul
Download or read book Asymptotic Distribution of the Bias Corrected Least Squares Estimators in Measurement Error Linear Regression Models Under Long Memory written by Hira Koul and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This article derives the consistency and asymptotic distribution of the bias corrected least squares estimators (LSEs) of the regression parameters in linear regression models when covariates have measurement error (ME) and errors and covariates form mutually independent long memory moving average processes. In the structural ME linear regression model, the nature of the asymptotic distribution of suitably standardized bias corrected LSEs depends on the range of the values of where ,, and are the LM parameters of the covariate, ME and regression error processes respectively. This limiting distribution is Gaussian when and non-Gaussian in the case . In the former case some consistent estimators of the asymptotic variances of these estimators and a log()-consistent estimator of an underlying LM parameter are also provided. They are useful in the construction of the large sample confidence intervals for regression parameters. The article also discusses the asymptotic distribution of these estimators in some functional ME linear regression models, where the unobservable covariate is non-random. In these models, the limiting distribution of the bias corrected LSEs is always a Gaussian distribution determined by the range of the values of )-)
Book Synopsis Asymptotic properties of least-squares estimators in semimartingale regression models by : Norbert Christopeit
Download or read book Asymptotic properties of least-squares estimators in semimartingale regression models written by Norbert Christopeit and published by . This book was released on 1985 with total page 14 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Asymptotic Theory of a Bias-corrected Least Squares Estimator in Truncated Regression by : Stanford University. Department of Statistics
Download or read book Asymptotic Theory of a Bias-corrected Least Squares Estimator in Truncated Regression written by Stanford University. Department of Statistics and published by . This book was released on 1990 with total page 23 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Distribution Theory of the Least Squares Averaging Estimator by : Chu-An Liu
Download or read book Distribution Theory of the Least Squares Averaging Estimator written by Chu-An Liu and published by . This book was released on 2017 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper derives the limiting distributions of least squares averaging estimators for linear regression models in a local asymptotic framework. We show that the averaging estimators with fixed weights are asymptotically normal and then develop a plug-in averaging estimator that minimizes the sample analog of the asymptotic mean squared error. We investigate the focused information criterion (Claeskens and Hjort, 2003), the plug-in averaging estimator, the Mallows model averaging estimator (Hansen, 2007), and the jackknife model averaging estimator (Hansen and Racine, 2012). We find that the asymptotic distributions of averaging estimators with data-dependent weights are nonstandard and cannot be approximated by simulation. To address this issue, we propose a simple procedure to construct valid confidence intervals with improved coverage probability. Monte Carlo simulations show that the plug-in averaging estimator generally has smaller expected squared error than other existing model averaging methods, and the coverage probability of proposed confidence intervals achieves the nominal level. As an empirical illustration, the proposed methodology is applied to cross-country growth regressions.
Book Synopsis Asymptotic Properties of the Ordinary Least Squares Estimator in Simultaneous Equations Models by : Virendra K. Srivastava
Download or read book Asymptotic Properties of the Ordinary Least Squares Estimator in Simultaneous Equations Models written by Virendra K. Srivastava and published by . This book was released on 1989 with total page 18 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis ASYMPTOTIC THEORY OF NONLINEAR LEAST SQUARES ESTIMATION by : Chien-Fu WU
Download or read book ASYMPTOTIC THEORY OF NONLINEAR LEAST SQUARES ESTIMATION written by Chien-Fu WU and published by . This book was released on 1979 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Asymptotic Theory for Iterated One-step Huber-skip Estimators by : Søren Johansen
Download or read book Asymptotic Theory for Iterated One-step Huber-skip Estimators written by Søren Johansen and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Asymptotic Properties of Some Estimators in Moving Average Models by : Stanford University. Department of Statistics
Download or read book Asymptotic Properties of Some Estimators in Moving Average Models written by Stanford University. Department of Statistics and published by . This book was released on 1975 with total page 318 pages. Available in PDF, EPUB and Kindle. Book excerpt: The author considers estimation procedures for the moving average model of order q. Walker's method uses k sample autocovariances (k> or = q). Assume that k depends on T in such a way that k nears infinity as T nears infinity. The estimates are consistent, asymptotically normal and asymptotically efficient if k = k (T) dominates log T and is dominated by (T sub 1/2). The approach in proving these theorems involves obtaining an explicit form for the components of the inverse of a symmetric matrix with equal elements along its five central diagonals, and zeroes elsewhere. The asymptotic normality follows from a central limit theorem for normalized sums of random variables that are dependent of order k, where k tends to infinity with T. An alternative form of the estimator facilitates the calculations and the analysis of the role of k, without changing the asymptotic properties.
