1) La descarga del recurso depende de la página de origen
2) Para poder descargar el recurso, es necesario ser usuario registrado en Universia


Opción 1: Descargar recurso

Detalles del recurso

Descripción

Consider an experiment in which $p$ independent populations $\pi_{i}$ with corresponding unknown means $\theta_{i}$ are available, and suppose that for every $1\leq i\leq p$, we can obtain a sample $X_{i1},\ldots,X_{in}$ from $\pi_{i}$. In this context, researchers are sometimes interested in selecting the populations that yield the largest sample means as a result of the experiment, and then estimate the corresponding population means $\theta_{i}$. In this paper, we present a frequentist approach to the problem and discuss how to construct simultaneous confidence intervals for the means of the $k$ selected populations, assuming that the populations $\pi_{i}$ are independent and normally distributed with a common variance $\sigma^{2}$. The method, based on the minimization of the coverage probability, obtains confidence intervals that attain the nominal coverage probability for any $p$ and $k$, taking into account the selection procedure.

Pertenece a

Project Euclid (Hosted at Cornell University Library)  

Autor(es)

Fuentes, Claudio -  Casella, George -  Wells, Martin T. - 

Id.: 70835413

Idioma: inglés  - 

Versión: 1.0

Estado: Final

Tipo:  application/pdf - 

Palabras claveConfidence intervals - 

Tipo de recurso: Text  - 

Tipo de Interactividad: Expositivo

Nivel de Interactividad: muy bajo

Audiencia: Estudiante  -  Profesor  -  Autor  - 

Estructura: Atomic

Coste: no

Copyright: sí

: Copyright 2018 The Institute of Mathematical Statistics and the Bernoulli Society

Formatos:  application/pdf - 

Requerimientos técnicos:  Browser: Any - 

Relación: [References] 1935-7524

Fecha de contribución: 05-abr-2018

Contacto:

Localización:
* Electron. J. Statist. 12, no. 1 (2018), 58-79
* doi:10.1214/17-EJS1374

Otros recursos del mismo autor(es)

  1. Introducing Monte Carlo Methods with R Computational techniques based on simulation have now become an essential part of the statistician's...
  2. AIC, Cp and estimators of loss for elliptically symmetric distributions In this article, we develop a modern perspective on Akaike's information criterion and Mallows's Cp ...
  3. A History of Markov Chain Monte Carlo--Subjective Recollections from Incomplete Data-- In this note we attempt to trace the history and development of Markov chain Monte Carlo (MCMC) from...
  4. Introducing Monte Carlo Methods with R Solutions to Odd-Numbered Exercises 87 pages, 11 figures
  5. Optimal Confidence Sets, Bioequivalence, and the Limaçon of Pascal We begin with a decision-theoretic investigation into confidence sets that minimize expected volume ...

Otros recursos de la mismacolección

  1. Fast adaptive estimation of log-additive exponential models in Kullback-Leibler divergence We study the problem of nonparametric estimation of probability density functions (pdf) with a produ...
  2. Convex and non-convex regularization methods for spatial point processes intensity estimation This paper deals with feature selection procedures for spatial point processes intensity estimation....
  3. An MM algorithm for estimation of a two component semiparametric density mixture with a known component We consider a semiparametric mixture of two univariate density functions where one of them is known ...
  4. Supervised multiway factorization We describe a probabilistic PARAFAC/CANDECOMP (CP) factorization for multiway (i.e., tensor) data th...
  5. A strong converse bound for multiple hypothesis testing, with applications to high-dimensional estimation In statistical inference problems, we wish to obtain lower bounds on the minimax risk, that is to bo...

Aviso de cookies: Usamos cookies propias y de terceros para mejorar nuestros servicios, para análisis estadístico y para mostrarle publicidad. Si continua navegando consideramos que acepta su uso en los términos establecidos en la Política de cookies.