This paper continues our earlier investigations into the inversion of random functions in a general (abstract) setting. In Section 2 we investigate a concept of invertibility and the invertibility of the composition of random functions. In Section 3 we resolve some questions concerning the number of samples required to ensure the accuracy of parametric maximum likelihood estimation (MLE). A direct application to phylogeny reconstruction is given
This thesis explores combinatorial methods in random vector balancing, nonparametric estimation, and...
Evaluating the likelihood function of parameters in complex population genetic models from extant de...
summary:A linear moving average model with random coefficients (RCMA) is proposed as more general al...
In this paper we study inverting randomfunctions under the maximumlik elihood estimation (MLE) crit...
In this paper we study how to invert random functions under different criteria. The motivation for ...
The basic ingredient of random variate generation is, of course, the uniform random number and, in p...
Recent interest in polynomial moving average models has raised the question of their invertibility. ...
We present a Bayesian approach to the problem of inferring the history of inversions separating homo...
The success of the phylogenetic approach within its broad range of potential applications requires t...
International audienceAs Dempster-Shafer theory spreads in different application fields, and as mass f...
In this thesis, we explore three techniques which could be used to increase the efficiency of analys...
Abstract. This chapter provides a survey of the main methods in non-uniform random variate generatio...
One can apply transformations of random variables to conduct inference for multiple distributions in...
We establish the consistency of a nonparametric maximum likelihood estimator for a class of stochast...
textabstractThis paper discusses inferential procedures for the family of stable distributions, when...
This thesis explores combinatorial methods in random vector balancing, nonparametric estimation, and...
Evaluating the likelihood function of parameters in complex population genetic models from extant de...
summary:A linear moving average model with random coefficients (RCMA) is proposed as more general al...
In this paper we study inverting randomfunctions under the maximumlik elihood estimation (MLE) crit...
In this paper we study how to invert random functions under different criteria. The motivation for ...
The basic ingredient of random variate generation is, of course, the uniform random number and, in p...
Recent interest in polynomial moving average models has raised the question of their invertibility. ...
We present a Bayesian approach to the problem of inferring the history of inversions separating homo...
The success of the phylogenetic approach within its broad range of potential applications requires t...
International audienceAs Dempster-Shafer theory spreads in different application fields, and as mass f...
In this thesis, we explore three techniques which could be used to increase the efficiency of analys...
Abstract. This chapter provides a survey of the main methods in non-uniform random variate generatio...
One can apply transformations of random variables to conduct inference for multiple distributions in...
We establish the consistency of a nonparametric maximum likelihood estimator for a class of stochast...
textabstractThis paper discusses inferential procedures for the family of stable distributions, when...
This thesis explores combinatorial methods in random vector balancing, nonparametric estimation, and...
Evaluating the likelihood function of parameters in complex population genetic models from extant de...
summary:A linear moving average model with random coefficients (RCMA) is proposed as more general al...