Stein estimation for infinitely divisible laws
Unbiased risk estimation, à la Stein, is studied for infinitely divisible laws with finite second moment.
Stopping problems as special statistical decision problems
Subset selection of the largest location parameter based on -estimates
The problem of selecting a subset of polulations containing the population with the largest location parameter is considered. As a generalization of selection rules based on sample means and on sample medians, a rule based on -estimates of location is proposed. This rule is strongly monotone and minimax, the risk being the expected subset size, provided the underlying density has monotone likelihood ratio. The problem of fulfilling the -condition is solved explicitly only asymptotically, under...
Systematic estimation and prediction for processes with bounded sum
Tests de détection de valeurs aberrantes multidimensionnelles
Tests optimaux sur les paramètres des lois de von Mises
The Bayes choice of an experiment in estimating a success probability
A Bayesian method of estimation of a success probability p is considered in the case when two experiments are available: individual Bernoulli (p) trials-the p-experiment-or products of r individual Bernoulli (p) trials-the -experiment. This problem has its roots in reliability, where one can test either single components or a system of r identical components. One of the problems considered is to find the degree r̃ of the -experiment and the size m̃ of the p-experiment such that the Bayes estimator...
The Bayes estimator of the variance components and its admissibility
The Bayes sequential estimation of a normal mean from delayed observations
The problem of estimating the mean of a normal distribution is considered in the special case when the data arrive at random times. Certain classes of Bayes sequential estimation procedures are derived under LINEX and reflected normal loss function and with the observation cost determined by a function of the stopping time and the number of observations up to this time.
The Bayesian approach to the combination of forecasts: some extensions into a skewed environment.
Where a decision-maker has to rely on expert opinions a need for a normative model to combine these forecasts appears. This can be done using Bayes' formula and by making some assumptions on the prior distribution and the distribution of the expert assessments. We extend the case to skewed distributions of these assessments. By using an Edgeworth expansion of the density function including the skewness parameter, we are able to obtain the formula to combine the forecasts in a Bayesian way.
The Necessity of Strongly Subadditive Capacities for Neyman-Pearson Minimax Tests.
The optimum sequential test of a finite number of hypotheses for statistically dependent observations
The predictive distribution in decision theory: A case study.
The Restricted Regularity of Admissible Minimax Criteria for many well known Parametric Statistical Problems.
The revolution of testimonies in the calculus of probabilities. (La révolution des témoignages dans le calcul des probabilités.)
The sum-product algorithm: algebraic independence and computational aspects
The sum-product algorithm is a well-known procedure for marginalizing an “acyclic” product function whose range is the ground set of a commutative semiring. The algorithm is general enough to include as special cases several classical algorithms developed in information theory and probability theory. We present four results. First, using the sum-product algorithm we show that the variable sets involved in an acyclic factorization satisfy a relation that is a natural generalization of probability-theoretic...
Third-Order Optimum Properties of Estimator-Sequences.
Three methods for constructing reference prior distributions.
Three methods are proposed for constructing reference prior densities for certain biparametric distribution families. These densities represent approximations to the Bayesian concept of noninformative distribution.
Topologies associated to some decision statistical functions
Toward Optimal Feature Selection Using Ranking Methods and Classification Algorithms