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A two-disorder detection problem

Krzysztof Szajowski (1997)

Applicationes Mathematicae

Suppose that the process X = { X n , n } is observed sequentially. There are two random moments of time θ 1 and θ 2 , independent of X, and X is a Markov process given θ 1 and θ 2 . The transition probabilities of X change for the first time at time θ 1 and for the second time at time θ 2 . Our objective is to find a strategy which immediately detects the distribution changes with maximal probability based on observation of X. The corresponding problem of double optimal stopping is constructed. The optimal strategy is found...

About stability of risk-seeking optimal stopping

Raúl Montes-de-Oca, Elena Zaitseva (2014)

Kybernetika

We offer the quantitative estimation of stability of risk-sensitive cost optimization in the problem of optimal stopping of Markov chain on a Borel space X . It is supposed that the transition probability p ( · | x ) , x X is approximated by the transition probability p ˜ ( · | x ) , x X , and that the stopping rule f ˜ * , which is optimal for the process with the transition probability p ˜ is applied to the process with the transition probability p . We give an upper bound (expressed in term of the total variation distance: sup x X p ( · | x ) - p ˜ ( · | x ) ) for...

Accelerated Monte Carlo estimation of exceedance probabilities under monotonicity constraints

Nicolas Bousquet (2012)

Annales de la faculté des sciences de Toulouse Mathématiques

The problem of estimating the probability p = P ( g ( X ) 0 ) is considered when X represents a multivariate stochastic input of a monotonic function g . First, a heuristic method to bound p , originally proposed by de Rocquigny (2009), is formally described, involving a specialized design of numerical experiments. Then a statistical estimation of p is considered based on a sequential stochastic exploration of the input space. A maximum likelihood estimator of p build from successive dependent Bernoulli data is defined...

Adaptive biased-coin designs for clinical trials with several treatments

Anthony C. Atkinson (2004)

Discussiones Mathematicae Probability and Statistics

Adaptive designs are used in phase III clinical trials for skewing the allocation pattern towards the better treatments. We use optimum design theory to provide a skewed biased-coin procedure for sequential designs with continuous responses. The skewed designs are used to provide adaptive designs, the performance of which is studied numerically for designs with three treatments. Important properties are loss and the proportion of allocation to inferior treatments. Regularisation to provide consistent...

Adaptive control for sequential design

Roland Gautier, Luc Pronzato (2000)

Discussiones Mathematicae Probability and Statistics

The optimal experiment for estimating the parameters of a nonlinear regression model usually depends on the value of these parameters, hence the problem of designing experiments that are robust with respect to parameter uncertainty. Sequential designpermits to adapt the experiment to the value of the parameters, and can thus be considered as a robust design procedure. By designing theexperiments sequentially, one introduces a feedback of information, and thus dynamics, into the design procedure....

Approximate maximum likelihood estimation for a spatial point pattern.

Jorge Mateu, Francisco Montes (2000)

Qüestiió

Several authors have proposed stochastic and non-stochastic approximations to the maximum likelihood estimate for a spatial point pattern. This approximation is necessary because of the difficulty of evaluating the normalizing constant. However, it appears to be neither a general theory which provides grounds for preferring a particular method, nor any extensive empirical comparisons. In this paper, we review five general methods based on approximations to the maximum likelihood estimate which have...

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