6 citations · 6 across the 11 of their papers we have counts for
11 papers
Consistency of the Bayes Estimator of a Regression Curve
Agustin G. Nogales
Strong consistency of the Bayes estimator of a regression curve for the -squared loss function is proved. It is also shown the convergence to 0 of the Bayes risk of this estim…
The Bayes Estimator of a Conditional Density: Consistency
Agustin G. Nogales
In a Bayesian framework we prove that the optimal estimator of a conditional density is consistent.
A Note on Consistency of the Bayes Estimator of the Density
A. G. Nogales
Under mild conditions, it is shown the strong consistency of the Bayes estimator of the density. Moreover, the Bayes risk (for some common loss functions) of the Bayes estimator of…
Optimal Bayesian Estimation of a Regression Curve, a Conditional Density and a Conditional Distribution
A. G. Nogales
In this paper several related estimation problems are addressed from a Bayesian point of view and optimal estimators are obtained for each of them when some natural loss functions…
On Bayesian Estimation of Densities and Sampling Distributions: the Posterior Predictive Distribution as the Bayes Estimator
A. G. Nogales
Optimality results for two outstanding Bayesian estimation problems are given in this paper: the estimation of the sampling distribution for the squared total variation function an…
A Note on Conditional Expectation for Markov Kernels
A. G. Nogales
A known property of conditional expectation is extended to the framework of Markov kernels. Its meaning in terms of densities is provided. Some examples located in the field of cli…
A Note on the Relationship Between Conditional and Unconditional Independence, and its Extensions for Markov Kernels
A. G. Nogales, P. Pérez
Two known results on the relationship between conditional and unconditional independence are obtained as a consequence of the main result of this paper, a theorem that uses indepen…
Minimum Risk Equivariant Estimation of the Parameters of the General Half-Normal Distribution by Means of a Monte Carlo Method to Approximate Conditional Expectations
A. G. Nogales, P. Pérez, P. Monfort
This work addresses the problem of estimating the parameters of the general half-normal distribution. Namely, the problem of determining the minimum risk equi\-va\-riant (MRE) esti…
A Monte Carlo Method to Approximate Conditional Expectations based on a Theorem of Besicovitch: Application to Equivariant Estimation of the Parameters of the General Half-Normal Distribution
Agustín G. Nogales, P. Pérez, P. Monfort
A natural Monte Carlo method to approximate conditional expectations in a probabilistic framework is justified by a general result inspired on the Besicovitch covering theorem on d…
Conditional Expectation of a Markov Kernel Given Another with Some Applications in Statistical Inference and Disease Diagnosis
Agustín G. Nogales
Markov kernels play a decisive role in probability and mathematical statistics theories, and are an extension of the concepts of sigma-field and statistic. Concepts such as indepen…
A note on kernel density estimation at a parametric rate
J. E. Chacón, J. Montanero, A. G. Nogales
In the context of kernel density estimation, we give a characterization of the kernels for which the parametric mean integrated squared error rate may be obtained, where $…