Download or read book Asymptotic Properties of Maximum likelihood estimators in Diffusion Type Models written by H. M. Dietz and published by . This book was released on 1989 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Asymptotic properties of maximum likelihood estimators in diffusion type models written by Hans M. Dietz and published by . This book was released on 1989 with total page 30 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Asymptotic properties of maximum likelihood estimators in diffusion type models written by Hans M. Dietz and published by . This book was released on 1990 with total page 21 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Asymptotics for the Maximum Likelihood Estimators of Diffusion Models written by Minsoo Jeong and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper I derive the asymptotics of the exact, Euler, and Milstein ML estimators for diffusion models, including general nonstationary diffusions. Though there have been many estimators for the diffusion model, their asymptotic properties were generally unknown. This is especially true for the nonstationary processes, even though they are usually far from the standard ones. Using a new asymptotics with respect to both the time span T and the sampling interval [delta], I find the asymptotics of the estimators and also derive the conditions for the consistency. With this new asymptotic result, I could show that this result can explain the properties of the estimators more correctly than the existing asymptotics with respect only to the sample size n. I also show that there are many possibilities to get a better estimator utilizing this asymptotic result with a couple of examples, and in the second part of the paper, I derive the higher order asymptotics which can be used in the bootstrap analysis.
Download or read book Asymptotic Properties of Maximum Likelihood Estimators in the General Sampling Framework and Some Results in Non normal Linear Regression written by Robert Ernest Tarone and published by . This book was released on 1974 with total page 190 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Optimal Asymptotic Properties of Maximum Likelihood Estimators of Parameters of Some Econometric Models written by Mary Kathleen Vickers and published by . This book was released on 1977 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt: Four theorems are proven, which simplify the application to econometric models of Weiss's theorem on asymptotic properties of maximum likelihood estimators in nonstandard cases. The theorems require, roughly: the uniform convergence in any compact sets of the unknown parameters of the expection of the Hessian matrix of the log likelihood function; and the uniform convergence to 0 in the same sense of the variance of the same quantities. The fourth theorem allows one to conclude that the optimal properties hold on an image set of the parameters when the map satisfies certain smoothness conditions, and the first three theorems are satisfied for the original parameter set. These four theorems are applied to autoregressive models, nonlinear models, systems of equations, and probit and logit models to infer optimal asymptotic properties. (Author).
Download or read book Maximum Likelihood Estimation of Discretely Sampled Diffusions written by Yacine Ait-Sahalia and published by . This book was released on 2000 with total page 52 pages. Available in PDF, EPUB and Kindle. Book excerpt: When a continuous-time diffusion is observed only at discrete dates, not necessarily close together, the likelihood function of the observations is in most cases not explicitly computable. Researchers have relied on simulations of sample paths in between the observation points, or numerical solutions of partial differential equations, to obtain estimates of the function to be maximized. By contrast, we construct a sequence of fully explicit functions which we show converge under very general conditions, including non-ergodicity, to the true (but unknown) likelihood function of the discretely-sampled diffusion. We document that the rate of convergence of the sequence is extremely fast for a number of examples relevant in finance. We then show that maximizing the sequence instead of the true function results in an estimator which converges to the true maximum-likelihood estimator and shares its asymptotic properties of consistency, asymptotic normality and efficiency. Applications to the valuation of derivative securities are also discussed.
Download or read book Optimal Asymptotic Properties of Maximum Likelihood Estimators of Parameters of Some Ecometric Models written by Mary Kathleen Vickers and published by . This book was released on 1979 with total page 140 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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.
Download or read book Asymptotic Properties and Computation of Maximum Likelihood Estimates in the Mixed Model of the Analysis of Variance written by Stanford University. Department of Statistics and published by . This book was released on 1973 with total page 556 pages. Available in PDF, EPUB and Kindle. Book excerpt: The problem considered is the estimation of the parameters in the mixed model of the analysis of variance, assuming normality of the random effects and errors. Both asymptotic properties of such estimates as the size of the design increases and numerical procedures for their calculation are discussed. Estimation is carried out by the method of maximum likelihood. It is shown that there is a sequence of roots of the likelihood equations which is consistent, asymptotically normal and asymptotically efficient in the sense of attaining the Cramer-Rao lower bound for the covariance matrix as the size of the design increases. This is accomplished using a Taylor series expansion of the log-likelihood. (Modified author abstract).
Download or read book Asymptotic Properties of maximum Likelihood estimators based on conditional specification written by Pranab Kumar Sen and published by . This book was released on 1978 with total page 24 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Asymptotic Properties of Conditional Maximum Likelihood Estimators written by Erling Bernhard Andersen and published by . This book was released on 1968 with total page 34 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book General statements written by Hans M. Dietz and published by . This book was released on 1989 with total page 30 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Parameter Estimation in Stochastic Differential Equations written by Jaya P. N. Bishwal and published by Springer. This book was released on 2007-09-26 with total page 271 pages. Available in PDF, EPUB and Kindle. Book excerpt: Parameter estimation in stochastic differential equations and stochastic partial differential equations is the science, art and technology of modeling complex phenomena. The subject has attracted researchers from several areas of mathematics. This volume presents the estimation of the unknown parameters in the corresponding continuous models based on continuous and discrete observations and examines extensively maximum likelihood, minimum contrast and Bayesian methods.
Download or read book Asymptotic Properties of Maximum Likelihood Estimators in the Nonlinear Regression Model when the Errors are Neither Independent Nor Identically Distributed written by R. D. H. Heijmans and published by . This book was released on 1982 with total page 84 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Large Sample Properties of Maximum Likelihood Estimators written by Nicholas Herbert Stern and published by . This book was released on 1980 with total page 28 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Asymptotic Properties of Induced Maximum Likelihood Estimates of Nonlinear Models for Item Response Variables The Finite Generic Item Pool Case written by Douglas H. Jones and published by . This book was released on 1985 with total page 38 pages. Available in PDF, EPUB and Kindle. Book excerpt: The progress of modern mental test theory depends very much on the techniques of maximum likelihood estimation, and many popular applications make use of likelihoods induced by logistic item response models. While, in reality, item responses are nonreplicate within a single examinee and the logistic models are only ideal, practitioners make inferences using the asymptotic distribution of the maximum likelihood estimator derived as if item responses were replicated and satisfied their ideal model. This article proposes a sample space acknowledging these two realities and derives the asymptotic distribution of the induced maximum likelihood estimator. This article assumes that items, while sampled from an infinite set of items, have but a finite domain of alternate response functions: this situation is the case of the finite-generic-item-pool. Using the proposed sample space, the article applies the statistical functional approach of von Mises to derive the influence curve of the maximum likelihood estimator; to discuss related robustness properties; and to derive new classes of resistent estimators. The aim is revealing the value of these methods for uncovering the relative merits of different item response functions. Proofs and mathematical derivations are minimized to increase the accessability of this complex subject. (Author).
