Estimation Issues for Copulas Applied to Marketing Data

Danaher, Peter J.; Smith, Michael S.
January 2011
Marketing Science;Jan/Feb2011, Vol. 30 Issue 1, p25
Academic Journal
Estimating copula models using Bayesian methods presents some subtle challenges, ranging from specification of the prior to computational tractability. There is also some debate about what is the most appropriate copula to employ from those available. We address these issues here and conclude by discussing further applications of copula models in marketing.


Related Articles

  • Bayesian Inference and Prediction of Burr Type XII Distribution for Progressive First Failure Censored Sampling. Soliman, Ahmed A.; Ellah, A. H. Abd; Abou-Elheggag, N. A.; Modhesh, A. A. // Intelligent Information Management;Sep2011, Vol. 3 Issue 5, p175 

    This paper deals with Bayesian inference and prediction problems of the Burr type XII distribution based on progressive first failure censored data. We consider the Bayesian inference under a squared error loss function. We propose to apply Gibbs sampling procedure to draw Markov Chain Monte...

  • THE POSTERIOR DISTRIBUTION OF THE LIKELIHOOD RATIO AS A MEASURE OF EVIDENCE. Smith, I.; Ferrari, A. // AIP Conference Proceedings;3/14/2011, Vol. 1305 Issue 1, p391 

    This paper deals with simple versus composite hypothesis testing under Bayesian and frequentist settings. The Posterior distribution of the Likelihood Ratio (PLR) concept is proposed in [1] for significance testing. The PLR is shown to be equal to 1 minus the p-value in a simple case. The PLR is...

  • Modeling Multivariate Distributions Using Copulas: Applications in Marketing. Danaher, Peter J.; Smith, Michael S. // Marketing Science;Jan/Feb2011, Vol. 30 Issue 1, p4 

    In this research we introduce a new class of multivariate probability models to the marketing literature. Known as "copula models," they have a number of attractive features. First, they permit the combination of any univariate marginal distributions that need not come from the same...

  • Estimation of R = P [Y < X] for two-parameter Burr Type XII Distribution. Panahi, H.; Asadi, S. // World Academy of Science, Engineering & Technology;Mar2011, Issue 51, p509 

    No abstract available.

  • Bayesian Separation of Non-Stationary Mixtures of Dependent Gaussian Sources. Gençağa, Deniz; Kuruoğlu, Ercan E.; Ertüzün, Ayşın // AIP Conference Proceedings;2005, Vol. 803 Issue 1, p257 

    In this work, we propose a novel approach to perform Dependent Component Analysis (DCA). DCA can be thought as the separation of latent, dependent sources from their observed mixtures which is a more realistic model than Independent Component Analysis (ICA) where the sources are assumed to be...

  • Genome-wide evaluation for quantitative trait loci under the variance component model. Lide Han; Shizhong Xu // Genetica;Oct2010, Vol. 138 Issue 9/10, p1099 

    The identity-by-descent (IBD) based variance component analysis is an important method for mapping quantitative trait loci (QTL) in outbred populations. The interval-mapping approach and various modified versions of it may have limited use in evaluating the genetic variances of the entire genome...

  • Markov Chain Monte Carlo: an introduction for epidemiologists. Hamra, Ghassan; MacLehose, Richard; Richardson, David // International Journal of Epidemiology;Apr2013, Vol. 42 Issue 2, p627 

    Markov Chain Monte Carlo (MCMC) methods are increasingly popular among epidemiologists. The reason for this may in part be that MCMC offers an appealing approach to handling some difficult types of analyses. Additionally, MCMC methods are those most commonly used for Bayesian analysis. However,...

  • Inference for discretely observed stochastic kinetic networks with applications to epidemic modeling. Choi, Boseung; Rempala, Grzegorz A. // Biostatistics;Jan2012, Vol. 13 Issue 1, p153 

    We present a new method for Bayesian Markov Chain Monte Carlo–based inference in certain types of stochastic models, suitable for modeling noisy epidemic data. We apply the so-called uniformization representation of a Markov process, in order to efficiently generate appropriate...

  • A Bayesian two-level model for fluctuation assay. Zheng, Qi // Genetica;Nov2011, Vol. 139 Issue 11/12, p1409 

    The fluctuation experiment is an essential tool for measuring microbial mutation rates in the laboratory. When inferring the mutation rate from an experiment, one assumes that the number of mutants in each test tube follows a common distribution. This assumption conceptually restricts the scope...


Read the Article


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

Try another library?
Sign out of this library

Other Topics