6 edition of **Maximum likelihood estimation of misspecified models** found in the catalog.

- 117 Want to read
- 28 Currently reading

Published
**2003** by Elsevier/JAI in Amsterdam, Boston .

Written in English

- Econometrics.,
- Econometric models.

**Edition Notes**

Includes bibliographical references.

Statement | edited by Tom Fomby, R. Carter Hill. |

Series | Advances in econometrics,, 17 |

Contributions | Fomby, Thomas B., Hill, R. Carter. |

Classifications | |
---|---|

LC Classifications | HB139 .M397 2003 |

The Physical Object | |

Pagination | xiii, 249 p. : |

Number of Pages | 249 |

ID Numbers | |

Open Library | OL3326332M |

ISBN 10 | 0762310758 |

LC Control Number | 2004300128 |

OCLC/WorldCa | 53030593 |

Such models exploit, in a parsimonious way, the ordered scale of the outcomes. An important example for a cumulative model is the well--known proportional odds model. This and other ordinal response models have been discussed in detail by Fahrmeir and Tutz (, ch. 3).

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MAXIMUM LIKELIHOOD ESTIMATION OF MISSPECIFIED MODELS BY HALBERT WHITE' This paper examines the consequences and detection of model misspecification when using maximum likelihood techniques for estimation and inference. The quasi-maximum likelihood estimator (QMLE) converges to a well defined limit, and may or may not be.

MAXIMUM LIKELIHOOD ESTIMATION OF MISSPECIFIED MODELS This paper examines the consequences and detection of model misspecification when using maximum likelihood techniques for estimation and inference.

The quasi-maximum likelihood estimator (QMLE) converges to a well defined limit, and may or may not beCited by: Review of Maximum Likelihood Estimation of Misspeci ed Models by Halbert White: Results Jim Harmon University of Washington Jim Harmon (University of Washington) MLE of Misspeci ed Models 1 / 34File Size: KB.

Maximum-likelihood estimation of misspecified models Gregory C. Chow Misspecified models occur frequently in econometric practice. It is therefore important to study the sampling distribution of maximum-likelihood estimators of parameters of misspecified by: 8.

Get this from a library. Maximum likelihood estimation of misspecified models: twenty years later. [Thomas B Fomby; R Maximum likelihood estimation of misspecified models book Hill;] -- This volume is the result of an Advances in Econometrics conference held in November of at Louisiana State Maximum likelihood estimation of misspecified models book in recognition of Halbert White's pioneering work published in Econometrica.

Maximum Likelihood Estimation of Misspecified Models by Maximum likelihood estimation of misspecified models book.

Fomby,available at Book Depository with free delivery worldwide. Kim, TH & White, HESTIMATION, INFERENCE, AND SPECIFICATION TESTING FOR POSSIBLY MISSPECIFIED QUANTILE REGRESSION. in Maximum Likelihood Estimation of Maximum likelihood estimation of misspecified models book Models: Twenty Years Later.

Advances in Econometrics, vol. 17, pp. Cited by: Professor White first explores the underlying motivation for maximum-likelihood estimation, treats the interpretation of the maximum-likelihood estimator (MLE) for misspecified probability models and gives the conditions under which parameters of interest can be consistently estimated despite misspecification and the consequences of.

White, H. () Maximum Likelihood Estimation of Misspecified Models. Econometrica, 50, Corrections. All material on this site has been provided by the respective publishers and authors.

You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ecm:emetrp:vyipSee general information about how to correct material in Maximum likelihood estimation of misspecified models book.

For technical questions regarding this item, or to correct its authors, title. estimation, inference, and specification testing for possibly misspecified quantile regression; quasi–maximum likelihood estimation with bounded symmetric errors; consistent quasi-maximum likelihood estimation with limited information; an examination of the sign and volatility switching arch models under alternative distributional assumptions.

White H. () Maximum Likelihood Estimation of Misspecified Dynamic Models. In: Dijkstra T.K. (eds) Misspecification Analysis. Lecture Notes in Economics and Mathematical Systems, vol Cited by: In statistics, maximum likelihood estimation (MLE) is a method of estimating the parameters of a probability distribution by maximizing a likelihood function, so that under the assumed statistical model the observed data is most probable.

The point in the parameter space that maximizes the likelihood function is called the maximum likelihood estimate. The logic of maximum likelihood is both. Professor White first explores the underlying motivation for maximum-likelihood estimation, treats the interpretation of the maximum-likelihood estimator (MLE) for misspecified probability models and gives the conditions under which parameters of interest can be consistently estimated despite misspecification and the consequences of 5/5(1).

Particular attention is addressed to (quasi) maximum likelihood estimation and misspecified models, along to phenomena due to heavy-tailed innovations.

The used methods are based on techniques applied to the analysis of stochastic recurrence by: Get this from a library. Maximum Likelihood Estimation of Misspecified Models: Twenty Years Later.

[T Fomby; R Carter Hill] -- This volume is the result of an Advances in Econometrics conference held in November of at Louisiana State University in recognition of Halbert White's pioneering work published in Econometrica.

I focused on ordinary least squares in terms of multivariate statistics when in graduate school. We did not discuss very much alternative perspectives. I was a multiple regression afficianado. But there is another approach, maximum likelihood estimation (MLE).

This book does a nice job of presenting a lucid explanation of MLE/5. regression models’, Journal of the American Statistical Associat – White, H. (), ‘Maximum likelihood estimation of misspeciﬁed models’, Econometrica 50(1), 1– Yuan, K.-H.

& Hayashi, K. (), ‘Standard errors in covariance structure models: Asymptotics versus bootstrap’, British Journal of.

Pseudo-Maximum Likelihood Estimation of ARCH(8) Models results hold for both correctly specified and misspecified models. The practical relevance of the theory is highlighted in a set of. Two‐Stage maximum likelihood estimation in the misspecified restricted latent class model Article in British Journal of Mathematical and Statistical Psychology 71(1) October with 28 Reads.

Bibliography Stu Maximum Likelihood Estimation of Misspeci ed Models by Halbert White Econometrica, Vol. 50, No.

1 (Jan., ), pp. Jim Harmon (University of Washington, Department of Statistics)MLE of Misspeci ed Models Ap 2 / 16File Size: KB. In statistics a quasi-maximum likelihood estimate (QMLE), also known as a pseudo-likelihood estimate or a composite likelihood estimate, is an estimate of a parameter θ in a statistical model that is formed by maximizing a function that is related to the logarithm of the likelihood function, but in discussing the consistency and (asymptotic) variance-covariance matrix, we assume some parts of.

Sell Maximum Likelihood Estimation of Misspecified Models: Twenty Years Later, Volume 17 (Advances in Econometrics) - ISBN - Ship for free.

- Bookbyte. This book examines the consequences of misspecifications ranging from the fundamental to the nonexistent for the interpretation of likelihood-based methods of statistical estimation and interference.

Professor White first explores the underlying motivation for maximum-likelihood estimation, treats the interpretation of the maximum-likelihood estimator (MLE) for misspecified probability models.

12 provide analogous results for the AR(p) and ARMA(p, q) models respectively. 1) Properties of Maximum Likelihood Estimation (MLE) Once an appropriate model or distribution has been specified to describe the characteristics of a set of data, the immediate issue is.

The Principle of Maximum Likelihood Objectives In this section, we present a simple example in order 1 To introduce the notations 2 To introduce the notion of likelihood and log-likelihood.

3 To introduce the concept of maximum likelihood estimator 4 To introduce the concept of maximum likelihood estimate. This work was further developed by White in Ref.

[2,3], where the term “quasi maximum likelihood” (QML) estimator was introduced. In particular, Huber and White have shown that the asymptotic distribution of the ML estimator under misspecified models is a Gaussian by: 1.

Maximum likelihood estimation of misspecified models. Econometrica 50 1– Mathematical Reviews (MathSciNet): MR Digital Object Identifier: doi/Cited by: The quasi-maximum likelihood estimator (OMLE) converges to a well defined limit, and may or may not be consistent for particular parameters of interest.

Standard tests (Wald, Lagrange Multiplier, or Likelihood Ratio) are invalid in the presence of misspecification, but more general statistics are given which allow inferences to be drawn robustly.

Particular attention is addressed to (quasi) maximum likelihood estimation and misspecified models, along to phenomena due to heavy-tailed innovations. The used methods are based on techniques applied to the analysis of stochastic recurrence equations.

Professor White first explores the underlying motivation for maximum-likelihood estimation, treats the interpretation of the maximum-likelihood estimator (MLE) for misspecified probability models and gives the conditions under which parameters of interest can be consistently estimated despite misspecification and the consequences of Price: $ In the case where we model the mean correctly, the psedudolikelihood estimates converge to the effects of interest.

They are two faces of the same problem, misspecified likelihoods in nonlinear models estimated using maximum likelihood. Reference. White, H.

Estimation, Inference and Specification Analysis. Cambridge: Cambridge University. Maximum likelihood estimation for linear mixed models Rasmus Waagepetersen Department of Mathematics Aalborg University Denmark Febru 1/28 Outline for today I linear mixed models I the likelihood function I maximum likelihood estimation I restricted maximum likelihood estimation 2/28 Linear mixed models Consider mixed model: Y ij.

Targeted maximum likelihood estimation implemented with ensemble and machine‐learning algorithms has advantages over other methods, but surprisingly there is limited guidance for the application of the technique for the estimation of the ATE and MOR when dealing with binary outcomes.

32 By using a reproducible example, we have demonstrated Cited by: 8. Maximum Likelihood Estimation (MLE) 1 Specifying a Model Typically, we are interested in estimating parametric models of the form yi» f(µ;yi) (1) where µ is a vector of parameters and f is some speciﬂc functional form (probability density or mass function).1 Note that this setup is quite general since the speciﬂc functional form, f, provides an almost unlimited choice of speciﬂc Size: KB.

Consistent parameter estimation of the true parameter values of the researcher’s model in the complete data case may also occur for misspecified models where: (i) heteroscedasticity is present (e.g., Verbeeksec. ), (ii) the random effects distribution is misspecified in linear hierarchical models (e.g., Verbeke and Lesaffre Cited by: 1.

Maximum Likelihood Estimates Cl Jeremy Orlo and Jonathan Bloom 1 Learning Goals 1. Be able to de ne the likelihood function for a parametric model given data. Be able to compute the maximum likelihood estimate of unknown parameter(s). 2 Introduction Suppose we know we have data consisting of values x 1;;x n drawn from an.

log-likelihood function, lnLðwjyÞ: This is because the twofunctions,lnLðwjyÞ andLðwjyÞ; aremonotonically related to each other so the same MLE estimate isCited by: models.

In maximum likelihood, two nested models may also be compared but the comparison involves diﬀerent statistics. One major test is the likelihood ratio test (LRT). Here, we have two models, term the “full” model and the “reduced” model. (ML enthusiasts often use the term “general” for the full model.)1 ToFile Size: KB.

Now, with that example behind us, let us take a look at formal definitions of the terms (1) likelihood function, (2) maximum likelihood estimators, and (3) maximum likelihood estimates.

Definition. Let X 1, X 2, X n be a random sample from a distribution that depends on one or more unknown parameters θ 1, θ 2, θ m with. deterministic estimation) are provided. Since two distinct CRBs exist pdf the true parametric probability model is known, a quasi-efﬁciency denomination is introduced.

I. INTRODUCTION Since its introduction by R.A. Fisher in deterministic es-timation [1][2], the method of maximum likelihood (ML) estimation has become one of the most.Maximum Likelihood; An Introduction* L. Le Cam Department of Statistics University of California Berkeley, California 1 Introduction One of the most widely used methods of statistical estimation is that of maximum likelihood.

Opinions on who was the ﬁrst to propose the method differ. However.The maximum likelihood estimation includes both regression coefficients and the ebook components, that is, both fixed-effects and random-effects terms in the likelihood function.

For a linear mixed-effects model defined above, the conditional response of the response variable y given β, b, θ, and σ 2 is.