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76
Dirichlet Prior Sieves in Finite Normal Mixtures
 Statistica Sinica
, 2002
"... Abstract: The use of a finite dimensional Dirichlet prior in the finite normal mixture model has the effect of acting like a Bayesian method of sieves. Posterior consistency is directly related to the dimension of the sieve and the choice of the Dirichlet parameters in the prior. We find that naive ..."
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Cited by 52 (1 self)
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Abstract: The use of a finite dimensional Dirichlet prior in the finite normal mixture model has the effect of acting like a Bayesian method of sieves. Posterior consistency is directly related to the dimension of the sieve and the choice of the Dirichlet parameters in the prior. We find that naive use of the popular uniform Dirichlet prior leads to an inconsistent posterior. However, a simple adjustment to the parameters in the prior induces a random probability measure that approximates the Dirichlet process and yields a posterior that is strongly consistent for the density and weakly consistent for the unknown mixing distribution. The dimension of the resulting sieve can be selected easily in practice and a simple and efficient Gibbs sampler can be used to sample the posterior of the mixing distribution. Key words and phrases: BoseEinstein distribution, Dirichlet process, identification, method of sieves, random probability measure, relative entropy, weak convergence.
Polynomial Learning of Distribution Families
"... Abstract—The question of polynomial learnability of probability distributions, particularly Gaussian mixture distributions, has recently received significant attention in theoretical computer science and machine learning. However, despite major progress, the general question of polynomial learnabili ..."
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Cited by 43 (0 self)
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Abstract—The question of polynomial learnability of probability distributions, particularly Gaussian mixture distributions, has recently received significant attention in theoretical computer science and machine learning. However, despite major progress, the general question of polynomial learnability of Gaussian mixture distributions still remained open. The current work resolves the question of polynomial learnability for Gaussian mixtures in high dimension with an arbitrary fixed number of components. Specifically, we show that parameters of a Gaussian mixture distribution with fixed number of components can be learned using a sample whose size is polynomial in dimension and all other parameters. The result on learning Gaussian mixtures relies on an analysis of distributions belonging to what we call “polynomial families” in low dimension. These families are characterized by their moments being polynomial in parameters and include almost all common probability distributions as well as their mixtures and products. Using tools from real algebraic geometry, we show that parameters of any distribution belonging to such a family can be learned in polynomial time and using a polynomial number of sample points. The result on learning polynomial families is quite general and is of independent interest. To estimate parameters of a Gaussian mixture distribution in high dimensions, we provide a deterministic algorithm for dimensionality reduction. This allows us to reduce learning a highdimensional mixture to a polynomial number of parameter estimations in low dimension. Combining this reduction with the results on polynomial families yields our result on learning arbitrary Gaussian mixtures in high dimensions. Index Terms—Gaussian mixture learning, polynomial learnability I.
Gender Discrimination Estimation in a Search Model with Matching and
 Bargaining,” IZA Discussion Paper 1764
, 2005
"... This article develops a search model of the labor market with matching, bargaining, and employers ’ taste discrimination in which—under necessary but standard distributional assumption—it is possible to separately identify gender discrimination and unobserved productivity differences. The equilibri ..."
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Cited by 23 (6 self)
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This article develops a search model of the labor market with matching, bargaining, and employers ’ taste discrimination in which—under necessary but standard distributional assumption—it is possible to separately identify gender discrimination and unobserved productivity differences. The equilibrium shows that both prejudiced and unprejudiced employers wage discriminate. Maximum likelihood estimates on CPS data indicate that half of the employers are prejudiced, average female productivity is 6.5 % lower, and twothird of the gender earning differential may be explained by prejudice. An affirmative action policy is implemented resulting in a redistribution of welfare from men to women at no cost for employers ’ welfare. 1.
The Random Coefficients Logit Model is Identified
 Journal of Econometrics
, 2011
"... The random coefficients multinomial choice logit model, also known as the mixed logit, has been widely used in empirical choice analysis for the last thirty years. We prove that the distribution of random coefficients in the multinomial logit model is nonparametrically identified. Our approach requi ..."
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Cited by 18 (1 self)
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The random coefficients multinomial choice logit model, also known as the mixed logit, has been widely used in empirical choice analysis for the last thirty years. We prove that the distribution of random coefficients in the multinomial logit model is nonparametrically identified. Our approach requires variation in product characteristics only locally and does not rely on the special regressors with large supports used in related papers. One of our two identification arguments is constructive. Both approaches may be applied to other choice models with random coefficients.
A Simple Nonparametric Estimator for the Distribution of Random Coefficients in Discrete Choice Models
, 2008
"... We propose an estimator for discrete choice models, such as the logit, with a nonparametric distribution of random coefficients. The estimator is linear regression subject to linear inequality constraints and is robust, simple to program and quick to compute compared to alternative estimators for mi ..."
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Cited by 17 (3 self)
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We propose an estimator for discrete choice models, such as the logit, with a nonparametric distribution of random coefficients. The estimator is linear regression subject to linear inequality constraints and is robust, simple to program and quick to compute compared to alternative estimators for mixture models. We discuss three methods for proving identification of the distribution of heterogeneity for any given economic model. We prove the identification of the logit mixtures model, which, surprisingly given the wide use of this model over the last 30 years, is a new result. We also derive our estimator’s nonstandard asymptotic distribution and demonstrate its excellent small sample properties in a Monte Carlo. The estimator we propose can be extended to allow for endogenous prices. The estimator can also be used to reduce the computational burden of nested fixed point methods for complex models like dynamic programming discrete choice.
2007): “Defining and estimating intervention effects for groups that will develop an auxiliary outcome
 Statistical Science
"... Abstract. It has recently become popular to define treatment effects for subsets of the target population characterized by variables not observable at the time a treatment decision is made. Characterizing and estimating such treatment effects is tricky; the most popular but naive approach inappropri ..."
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Cited by 17 (1 self)
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Abstract. It has recently become popular to define treatment effects for subsets of the target population characterized by variables not observable at the time a treatment decision is made. Characterizing and estimating such treatment effects is tricky; the most popular but naive approach inappropriately adjusts for variables affected by treatment and so is biased. We consider several appropriate ways to formalize the effects: principal stratification, stratification on a single potential auxiliary variable, stratification on an observed auxiliary variable and stratification on expected levels of auxiliary variables. We then outline identifying assumptions for each type of estimand. We evaluate the utility of these estimands and estimation procedures for decision making and understanding causal processes, contrasting them with the concepts of direct and indirect effects. We motivate our development with examples from nephrology and cancer screening, and use simulated data and real data on cancer screening to illustrate the estimation methods. Key words and phrases: Causality, direct effects, interaction, effect modification, bias, principal stratification.
Mixed normal conditional heteroskedasticity
 Journal of Financial Econometrics
, 2004
"... Both unconditional mixednormal distributions and GARCH models with fattailed conditional distributions have been employed for modeling financial return data. We consider a mixednormal distribution coupled with a GARCHtype structure which allows for conditional variance in each of the components ..."
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Cited by 15 (3 self)
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Both unconditional mixednormal distributions and GARCH models with fattailed conditional distributions have been employed for modeling financial return data. We consider a mixednormal distribution coupled with a GARCHtype structure which allows for conditional variance in each of the components as well as dynamic feedback between the components. Special cases and relationships with previously proposed specifications are discussed and stationarity conditions are derived. An empirical application to NASDAQindex data indicates the appropriateness of the model class and illustrates that the approach can generate a plausible disaggregation of the conditional variance process, in which the components ’ volatility dynamics have a clearly distinct behavior that is, for example, compatible with the wellknown leverage effect.
2009): Identifying Heterogeneity in Economic Choice Models, Discussion paper
"... We show how to nonparametrically identify the distribution that characterizes heterogeneity among agents in a general class of structural choice models. We introduce an axiom that we term separability and prove that separability of a structural model ensures identification. The main strength of sepa ..."
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Cited by 13 (1 self)
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We show how to nonparametrically identify the distribution that characterizes heterogeneity among agents in a general class of structural choice models. We introduce an axiom that we term separability and prove that separability of a structural model ensures identification. The main strength of separability is that it makes verifying the identification of nonadditive models a tractable task because it is a condition that is stated directly in terms of the choice behavior of agents in the model. We use separability to prove several new results. We prove the identification of the distribution of random functions and marginal effects in a nonadditive regression model. We also identify the distribution of utility functions in the multinomial choice model. Finally, we extend 2SLS to have random functions in both the first and second stages. This instrumental variables strategy applies equally to multinomial choice models with endogeneity.