Results 201 to 210 of about 27,081 (245)

Semiparametric Bayesian networks

Information Sciences, 2022
We introduce semiparametric Bayesian networks that combine parametric and nonparametric conditional probability distributions. Their aim is to incorporate the advantages of both components: the bounded complexity of parametric models and the flexibility of nonparametric ones.
Concha Bielza, Pedro Larranaga, David Atienza   +2 more
exaly   +4 more sources

Semiparametric Duration Models [PDF]

Journal of Business and Economic Statistics, 2004
In this article we consider semiparametric duration models and efficient estimation of the parameters in a non-iid environment. In contrast to classical time series models where innovations are assumed to be iid we show that in, for example, the often-used autoregressive conditional duration (ACD) model, the assumption of independent innovations is too
Bas J M Werker, Feike C Drost
exaly   +7 more sources

Semiparametric Models [PDF]

, 2001
Much empirical research is concerned with estimating conditional mean, median, or hazard functions. For example, labor economists are interested in estimating the mean wages of employed individuals conditional on characteristics such as years of work experience and education.
Joseph G. Ibrahim, Ming-Hui Chen, Debajyoti Sinha   +2 more
openaire   +3 more sources

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Semiparametric Density Deconvolution

Scandinavian Journal of Statistics, 2010
The authors consider density \(f\) estimation by i.i.d. observations with the density \[ g(x)=f*\pi(x)=\int f(x-z)\pi(z)dz, \] where \(\pi\) is a known density of an independent measurement error. The idea is to use a parametric working model \(f(x\,|\,\vartheta)=w(x\,|\,\vartheta)g(x)\), where \(w(x\,|\,\vartheta)\), \(\vartheta\in\Theta\), is a model
Hazelton, M.L., Turlach, B.A.
openaire   +1 more source

Introduction to Semiparametric Methods

Journal of Statistical Theory and Practice, 2008
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kedem, Benjamin, Lu, Guanhua
openaire   +2 more sources

Semiparametric Analysis of Truncated Data

Lifetime Data Analysis, 2001
Randomly truncated data are frequently encountered in many studies where truncation arises as a result of the sampling design. In the literature, nonparametric and semiparametric methods have been proposed to estimate parameters in one-sample models.
Qin, Jing, Wang, Mei-Cheng
openaire   +3 more sources

Semiparametric econometrics: A survey

Journal of Applied Econometrics, 1988
AbstractSemiparametric econometric models contain both parametric and nonparametric components, reflecting in some fashion what has been learned from economic theory and previous empirical experience, and what remains unknown. They raise such questions as how well the parametric component can be estimated, and how to construct rules of inference with ...
openaire   +1 more source

Semiparametric efficiency bounds

Journal of Applied Econometrics, 1990
AbstractSemiparametric models are those where the functional form of some components is unknown. Efficiency bounds are of fundamental importance for such models. They provide a guide to estimation methods and give an asymptotic efficiency standard.
openaire   +1 more source

Bayesian semiparametric regression [PDF]

, 2012
We consider Bayesian estimation of restricted conditional moment models with linear regression as a particular example. The standard practice in the Bayesian literature for semiparametric models is to use flexible families of distributions for the errors and assume that the errors are independent from covariates.
openaire   +1 more source