Consistency a Posteriori

Berk, Robert H.

doi:10.1214/aoms/1177696967

Cited by 58 publications

(35 citation statements)

References 4 publications

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“…The proof is identical to that of Jennrich's [29] However, since 9 is also in -n , and since h is is an asymptotically uniform sequence of discrepancies. …”

Section: Resultsmentioning

confidence: 71%

“…We obtain an estimator as the solution to the problem min Q^(u), e) = g^ii^ie), %^) = L^((^^(e), 0), (9) to be one-to-one is the familiar order condition. This guarantees that the range space is "large" enough for i\> (6) to be one-to-one.…”

Section: Consistency Of M-estimators and Generalized Methods Of Momentmentioning

confidence: 99%

“…For probability models with a specified likelihood function, closeness can be measured using the Kullback-Leibler [31 ] Information Criteria (KLIC) which provides a measure of the closeness of the specified likelihood function f (o) , 9) to the joint density of the sample dP* / dp implied by the true data generating mechanism.…”

Section: This Is the Vector Autoregression Model Of Order 1 [Var(1))mentioning

confidence: 99%

“…(We define P*(E) = P* The study of the consequences of applying such estimators to misspecified probability models was initiated by Huber [28] and Berk [8,9]» Work by White [42,44,45] examines the implications of model misspecification for ordinary least squares, nonlinear least squares and maximum likelihood estimation respectively under convenient assiimptions less general than those of Huber, while the recent work of Burguete, Gallant and Souza [12] provides a unified framework in which estimation of misspecified models can be conveniently treated.…”

Section: This Is the Vector Autoregression Model Of Order 1 [Var(1))mentioning

confidence: 99%

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A Unified Theory of Consistent Estimation for Parametric Models

Bates

White

1985

Econom. Theory

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Ve present a general theory of consistent estimation for possibly misspecified parametric models based on recent results of Domowitz and White [17].This theory extends the iinification of Burguete, Gallant and Souza [12] by allowing for heterogeneous, time dependent data and d3mamic models. The theory is applied to yield consistency results for quasi-maximum likelihood and method of moments estimators. Of particular interest is a new generalized rank condition for identifiability. SEP 3 19851 .

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“…The proof is identical to that of Jennrich's [29] However, since 9 is also in -n , and since h is is an asymptotically uniform sequence of discrepancies. …”

Section: Resultsmentioning

confidence: 71%

Section: Consistency Of M-estimators and Generalized Methods Of Momentmentioning

confidence: 99%

Section: This Is the Vector Autoregression Model Of Order 1 [Var(1))mentioning

confidence: 99%

Section: This Is the Vector Autoregression Model Of Order 1 [Var(1))mentioning

confidence: 99%

See 2 more Smart Citations

A Unified Theory of Consistent Estimation for Parametric Models

Bates

White

1985

Econom. Theory

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“…It reduces to p(θ, ) ∝ (d+1)/2 when θ is discarded but it has nevertheless to be proscribed since by (23) …”

Section: Metropolis-hastings Approximate Sampling From P(θ |Z N )mentioning

confidence: 99%

Bayesian selection of multiresponse nonlinear regression model

Rossi¹,

Vila²

2008

Statistics

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A Bayesian method for the selection and ranking of multiresponse nonlinear regression models in a given model set is proposed. It uses an expected utility criterion based on the logarithmic score of a posterior predictive density of each model. Two approaches are proposed to get this posterior. The first is based on a general asymptotically convergent approximation of the model parameter posterior corresponding to a wide class of parameter priors. The second, a numerical one, uses well-known pure or hybrid MCMC methods. Both posterior approximation approaches allow the practical computation of the expected utility criterion on a sample-reuse basis. This leads to a class of Bayesian cross-validation (CV) procedures, aiming at finding the model having the best predictive ability among a set of models. Varied comparisons of the performances of the Bayesian procedures, with that of AIC, BIC and standard CV procedures, are proposed on a simulated and a real model selection problem case studies, of low and high parametric dimensions respectively.

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U‐Statistics

Serfling¹

2005

Encyclopedia of Statistical Sciences

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Consistency a Posteriori

Cited by 58 publications

References 4 publications

A Unified Theory of Consistent Estimation for Parametric Models

A Unified Theory of Consistent Estimation for Parametric Models

Bayesian selection of multiresponse nonlinear regression model

U‐Statistics

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