Displaying similar documents to “Estimation of random survival functions: a linear approach.”

Nonparametric estimation: the survival function.

Alfonso García Pérez (1984)

Trabajos de Estadística e Investigación Operativa

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The unknown survival function S(t) of a random variable T ≥ 0 is considered. First we study the properties of S(t) and then, we estimate it from a Bayesian point of view. We compare the estimator with the posterior mean and we finish giving Bayes rules for linear functions of S(t).

Nonparametric Bayesian estimation and goodness of fit test.

Vicente Quesada Paloma, Alfonso García Pérez (1985)

Qüestiió

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We first make a review of prior distributions neutral to the right, and then we get the Bayes rule for the survival function S(t) = 1 - F(t), with quadratic loss, with these prior distributions. We give, after that, the estimator with a special kind of processes neutral to the right, the homogeneous processes. We get in point four the linear Bayes rule and we give there an interpretation of the parameters. We finish with a Bayesian generalization of the Kolmogorov-Smirnov...

Nonparametric Bayes decision theory.

Sid R. Dalal (1980)

Trabajos de Estadística e Investigación Operativa

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A summary of the seminar with the same title is presented. Ferguson's fundamental work on the theory of Dirichlet processes is elucidated and their shortcomings are discussed. Some modifications are also proposed and illustrated. Some of the intrincate mathematical issues related to the definitions and the proofs are not discussed for the sake of clarity and brevity. The development related to unimodal processes, briefly mentioned in the last section, will appear as a joint work with...

Asymptotic distribution of the conditional regret risk for selecting good exponential populations

Shanti S. Gupta, Friedrich Liese (2000)

Kybernetika

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In this paper empirical Bayes methods are applied to construct selection rules for the selection of all good exponential distributions. We modify the selection rule introduced and studied by Gupta and Liang [10] who proved that the regret risk converges to zero with rate O ( n - λ / 2 ) , 0 < λ ≤ 2 . The aim of this paper is to study the asymptotic behavior of the conditional regret risk ℛ n . It is shown that n ℛ n tends in distribution to a linear combination of independent χ 2 -distributed random variables. As an application...