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Estimación paramétrica bayesiana no paramétrica de funciones de supervivencia con observaciones parcialmente censuradas.

Domingo Morales, Vicente Quesada, Leandro Pardo (1986)

Trabajos de Estadística

The problem of nonparametric estimation of a survival function based on a partially censored on the right sample is established in a Bayesian context, using parametric Bayesian techniques. Estimates are obtained considering neutral to the right processes, they are particularized to some of them, and their asymptotic properties are studied from a Bayesian point of view. Finally, an application to a Dirichlet process is simulated.

Estimates of reliability for the normal distribution

Jan Hurt (1980)

Aplikace matematiky

The minimum variance unbiased, the maximum likelihood, the Bayes, and the naive estimates of the reliability function of a normal distribution are studied. Their asymptotic normality is proved and asymptotic expansions for both the expectation and the mean squared error are derived. The estimates are then compared using the concept of deficiency. In the end an extensive Monte Carlo study of the estimates in small samples is given.

Estimating quantiles with Linex loss function. Applications to VaR estimation

Ryszard Zieliński (2005)

Applicationes Mathematicae

Sometimes, e.g. in the context of estimating VaR (Value at Risk), underestimating a quantile is less desirable than overestimating it, which suggests measuring the error of estimation by an asymmetric loss function. As a loss function when estimating a parameter θ by an estimator T we take the well known Linex function exp{α(T-θ)} - α(T-θ) - 1. To estimate the quantile of order q ∈ (0,1) of a normal distribution N(μ,σ), we construct an optimal estimator in the class of all estimators of the form...

Estimating the shape parameter of the Topp-Leone distribution based on Type I censored samples

Husam Awni Bayoud (2015)

Applicationes Mathematicae

The shape parameter of the Topp-Leone distribution is estimated from classical and Bayesian points of view based on Type I censored samples. The maximum likelihood and the approximate maximum likelihood estimates are derived. The Bayes estimate and the associated credible interval are approximated by using Lindley's approximation and Markov Chain Monte Carlo using the importance sampling technique. Monte Carlo simulations are performed to compare the performances of the proposed methods. Real and...

Estimation and bimodality testing in the cusp model

Jan Voříšek (2018)

Kybernetika

The probability density function of the stochastic cusp model belongs to the class of generalized exponential distributions. It accommodates variable skewness, kurtosis, and bimodality. A statistical test for bimodality of the stochastic cusp model using the maximum likelihood estimation and delta method for Cardan's discriminant is introduced in this paper, as is a necessary condition for bimodality, which can be used for simplified testing to reject bimodality. Numerical maximum likelihood estimation...

Estimation for misspecified ergodic diffusion processes from discrete observations

Masayuki Uchida, Nakahiro Yoshida (2011)

ESAIM: Probability and Statistics

The joint estimation of both drift and diffusion coefficient parameters is treated under the situation where the data are discretely observed from an ergodic diffusion process and where the statistical model may or may not include the true diffusion process. We consider the minimum contrast estimator, which is equivalent to the maximum likelihood type estimator, obtained from the contrast function based on a locally Gaussian approximation of the transition density. The asymptotic normality of the...

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