Wyniki wyszukiwania - BazTech

Ograniczanie wyników

Znaleziono wyników: 3

Liczba wyników na stronie

Wyniki wyszukiwania

Wyszukiwano:
w słowach kluczowych: importance sampling

Sortuj według:

Ogranicz wyniki do:

Optimal importance sampling for continuous Gaussian fields

Pacchiarotti Barbara

Journal of Applied Analysis

2020

Vol. 26, nr 2

161--171

We consider the problem of selecting a change of mean which minimizes the variance of Monte Carlo estimators for the expectation of a functional of a continuous Gaussian field, in particular continuous Gaussian processes. Functionals of Gaussian fields have taken up an important position in many fields including statistical physics of disordered systems and mathematical finance (see, for example, [A. Comtet, C.Monthus and M. Yor, Exponential functionals of Brownian motion and disordered systems, J. Appl. Probab. 35 (1998), no. 2, 255-271], [D. Dufresne, The integral of geometric Brownian motion, Adv. in Appl. Probab. 33 (2001), no. 1, 223-241], [N. Privault and W. I. Uy, Monte Carlo computation of the Laplace transform of exponential Brownian functionals, Methodol. Comput. Appl. Probab. 15 (2013), no. 3, 511-524] and [V. R. Fatalov, On the Laplace method for Gaussian measures in a Banach space, Theory Probab. Appl. 58 (2014), no. 2, 216-241]. Naturally, the problem of computing the expectation of such functionals, for example the Laplace transform, is an important issue in such fields. Some examples are considered, which, for particular Gaussian processes, can be related to option pricing.

Asymptotics of Monte Carlo maximum likelihood estimators

Miasojedow B., Niemiro W., Palczewski J., Rejchel W.

Probability and Mathematical Statistics

2016

Vol. 36, Fasc. 2

295--310

We describe Monte Carlo approximation to the maximum likelihood estimator in models with intractable norming constants and explanatory variables. We consider both sources of randomness (due to the initial sample and to Monte Carlo simulations) and prove asymptotical normality of the estimator.

Dobór zmiennych objaśniających metodą najlepszego podzbioru do modelu regresji logistycznej Firtha

Fijorek K., Fijorek D.

Metody Informatyki Stosowanej

2011

nr 2

15-23

The application of logistic regression to small-sized data sets results in biased estimates and often leads to a complete separation problem. Under the small sample scenario the Firth’s approach to logistic regression or its Bayesian counterpart are known to solve both issues. The main goal of this study is to explore the effectiveness of the best subset variable selection algorithm, applied to both the classical and the Bayesian logistic regression.