TITLE

"ON BAYESIAN ESTIMATION OF MODEL PARAMETERS" : COMMENTARY

AUTHOR(S)
Vanhonacker, Wilfried R.
PUB. DATE
January 1990
SOURCE
Marketing Science;Winter90, Vol. 9 Issue 1, p54
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
This article presents comments of the author on the article "On Bayesian Estimation of Model Parameters," by Peter J. Lenk and Ambar G. Rao, professors at University of Michigan and New York University respectively. The purpose of this comment is to (a) position the contribution in a broader framework of Bayesian approaches to the problem, (b) highlight a key assumption of Hierarchical Bayes (HB), and (c) provide some directions for further research on this important problem. The author states that the Bayesian estimation procedures have received limited attention in the marketing science community despite the fact that it is typically used in prior information building, estimating, testing, and using models. The author is of the opinion that the research conducted by Lenk and Rao re-emphasizes the importance and appropriateness of the Bayesian approach to solving marketing problems. It has helped to obtain reliable and valid forecasts of the diffusion process of product innovations early into their market cycle. He states that the introduction to HB procedures and their usefulness in early forecasting is timely and appropriate.
ACCESSION #
4479110

 

Related Articles

  • "ON BAYESIAN ESTIMATION OF MODEL PARAMETERS": REPLY. Lenk, Peter J.; Rao, Ambar G. // Marketing Science;Winter90, Vol. 9 Issue 1, p56 

    This article presents a reply by the authors in response to comments made by researcher Wilfried R. Vanhonacker on their article "On Bayesian Estimation of Model Parameters." The authors wish to clarify minor points of misunderstanding. They state that Hierarchical Bayes (HB) models do not...

  • Using the empirical Bayes method to estimate and evaluate bycatch rates of seabirds from individual fishing vessels. Kimura, Daniel K. // Fishery Bulletin;Oct2007, Vol. 105 Issue 4, p577 

    The article highlights a study on the use of empirical Bayesian (EB) method to estimate and evaluate bycatch rates of seabirds from individual fishing vessels. The data used on EB analysis were the bycatches of seabirds from individual longline fishing vessels in the eastern Bering Sea in 2003...

  • Bayesian Classifier based on the Multivariate Normal Distribution. Iatan, Iuliana Florentina // Journal of Computational Analysis & Applications;Jan2008, Vol. 10 Issue 1, p197 

    The aim of this paper is the Bayesian estimation techniques to obtain the form of the a posteriori density p (μ∣D) and the desired probability density p(X∣D) in the case when p(X∣μ) ∼ N(μ, Σ). The treatment of the multivariate case in which Σ is known but μ...

  • How robust are Bayesian posterior inferences based on a Ricker model with regards to measurement errors and prior assumptions about parameters? Rivot, E; Prévost, E; Parent, E // Canadian Journal of Fisheries & Aquatic Sciences;Nov2001, Vol. 58 Issue 11, p2284 

    We present a Bayesian approach of a Ricker stock-recruitment (S/R) analysis accounting for measurement errors on S/R data. We assess the sensitivity of posterior inferences to (i) the choice of Ricker model parameterizations, with special regards to management-related ones, and (ii) prior...

  • SEQUENTIAL DECISION PROBLEMS: A MODEL TO EXPLOIT EXISTING FORECASTERS. Hausman, Warren H. // Management Science;Oct69, Vol. 16 Issue 2, pB-93 

    A sequential decision problem is partitioned into two parts: a stochastic model describing the transition probability density function of the state variable, and a separate framework of decision choices and payoffs. If a particular sequential decision problem is a recurring one, then there may...

  • The equivalence of Bayes and robust Bayes estimators for various loss functions. Kamínska, Agnieszka // Statistical Papers;Jan2010, Vol. 51 Issue 1, p179 

    The problem of Bayes and robust Bayes estimation for various bounded and/or symmetric loss functions in a normal model with conjugate and non-informative prior distributions is considered. The prior distribution is not fully specified and covers the conjugate family of priors. It is of interest...

  • BM-BC: a Bayesian method of base calling for Solexa sequence data. Ji, Yuan; Mitra, Riten; Quintana, Fernando; Jara, Alejandro; Mueller, Peter; Ping Liu; Yue Lu; Shoudan Liang // BMC Bioinformatics;2012, Vol. 13 Issue Suppl 13, p1 

    Base calling is a critical step in the Solexa next-generation sequencing procedure. It compares the position-specific intensity measurements that reflect the signal strength of four possible bases (A, C, G, T) at each genomic position, and outputs estimates of the true sequences for short reads...

  • STATISTICS OF THE THREE-PARAMETER WEIBULL DISTRIBUTION. Smith, R. L.; Naylor, J. C. // Annals of Operations Research;1987, Vol. 9 Issue 1-4, p577 

    We consider the estimation of the parameters of the three-parameter Weibull distribution, with particular emphasis on the unknown endpoint of the distribution. We summarize recent results on the asymptotic behaviour of maximum likelihood estimators. We continue with an example in which maximum...

  • Weibull inference using trimmed samples and prior information. Fernández, Arturo J. // Statistical Papers;Jan2009, Vol. 50 Issue 1, p119 

    Trimmed samples are commonly used in several branches of statistical methodology, especially when the presence of contaminated data is suspected. Assuming that certain proportions of the smallest and largest observations from a Weibull sample are unknown or have been eliminated, a Bayesian...

Share

Read the Article

Courtesy of VIRGINIA BEACH PUBLIC LIBRARY AND SYSTEM

Sorry, but this item is not currently available from your library.

Try another library?
Sign out of this library

Other Topics