A class of stopping rules for fixed precision sequential estimates
A class of unbiased kernel estimates of a probability density function
We propose a class of unbiased and strongly consistent nonparametric kernel estimates of a probability density function, based on a random choice of the sample size and the kernel function. The expected sample size can be arbitrarily small and mild conditions on the local behavior of the density function are imposed.
A discrete theory of search. I
A modification of Sudakov's lemma and efficient sequential plans for some jump Markov processes
A modification of Sudakov's lemma and efficient sequential plans for the Ornstein-Uhlenbeck process
A note on confidence intervals for the Kiefer-Wolfowitz problem
A Note on Three-Stage Confidence Intervals for the Difference of Locations: The Exponential Case.
A sequential Bayesian approach to estimating the dimension of a multinomial distribution
A survey of sequential estimation in processes with independent increments
A survey of sequential statistical analysis
About a fixed precision estimation of the parameter of uniform density
Accelerated Monte Carlo estimation of exceedance probabilities under monotonicity constraints
The problem of estimating the probability is considered when represents a multivariate stochastic input of a monotonic function . First, a heuristic method to bound , originally proposed by de Rocquigny (2009), is formally described, involving a specialized design of numerical experiments. Then a statistical estimation of is considered based on a sequential stochastic exploration of the input space. A maximum likelihood estimator of build from successive dependent Bernoulli data is defined...