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Parametric inference for mixed models defined by stochastic differential equations

Sophie Donnet, Adeline Samson (2008)

ESAIM: Probability and Statistics

Non-linear mixed models defined by stochastic differential equations (SDEs) are considered: the parameters of the diffusion process are random variables and vary among the individuals. A maximum likelihood estimation method based on the Stochastic Approximation EM algorithm, is proposed. This estimation method uses the Euler-Maruyama approximation of the diffusion, achieved using latent auxiliary data introduced to complete the diffusion process between each pair of measurement instants. A tuned...

Periodic autoregression with exogenous variables and periodic variances

Jiří Anděl (1989)

Aplikace matematiky

The periodic autoregressive process with non-vanishing mean and with exogenous variables is investigated in the paper. It is assumed that the model has also periodic variances. The statistical analysis is based on the Bayes approach with a vague prior density. Estimators of the parameters and asymptotic tests of hypotheses are derived.

Posterior odds ratios for selected regression hypotheses.

Arnold Zellner, Aloysius Siow (1980)

Trabajos de Estadística e Investigación Operativa

Bayesian posterior odds ratios for frequently encountered hypotheses about parameters of the normal linear multiple regression model are derived and discussed. For the particular prior distributions utilized, it is found that the posterior odds ratios can be well approximated by functions that are monotonic in usual sampling theory F statistics. Some implications of these finding and the relation of our work to the pioneering work of Jeffreys and others are considered. Tabulations of odd ratios...

Posterior regret Γ-minimax estimation in a normal model with asymmetric loss function

Agata Boratyńska (2002)

Applicationes Mathematicae

The problem of posterior regret Γ-minimax estimation under LINEX loss function is considered. A general form of posterior regret Γ-minimax estimators is presented and it is applied to a normal model with two classes of priors. A situation when the posterior regret Γ-minimax estimator, the most stable estimator and the conditional Γ-minimax estimator coincide is presented.

Predictive sample reuse techniques for censored data.

Seymour Geisser (1980)

Trabajos de Estadística e Investigación Operativa

Predictive sample reuse methods usually applied in low structure aparametric paradigms are shown to be useful in certain high structure situations when conjoined with a Bayesian approach. Particular attention is focused on the incomplete data situation for which two alternative sample reuse approaches are devised. The first involves differential weighting and the second a recursive sample reuse algorithm. These are applied to censored exponential survival data. The exponential approach appears to...

Problemas de óptimo que relacionan la información de Kullback y el conjunto de riesgos de Neyman-Pearson.

Ramiro Melendreras Gimeno (1983)

Trabajos de Estadística e Investigación Operativa

Consideramos la conexión que existe entre la información de Kullback y los tests admisibles óptimos en el conjunto de riesgos de Neyman-Pearson, usando para ello el estudio de problemas de programación matemática de tipo infinito. Se obtienen resultados que caracterizan un subconjunto de soluciones Bayes como consecuencia del conocimiento de la información, así como una medida de discriminación entre hipótesis para el conjunto de riesgos.

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