This paper discusses the increasing importance of probability simulation methods in the context of Maximum Simulated Likelihood. Three probability simulators are analyzed following their chronological order of appearance. This analysis provides an intuitive approach to the basic idea behind probability simulation, the successive improvements and probable future developments. The paper pays special attention to the role of simulation noise in Maximum Simulated Likelihood
In this thesis we will describe the maximum likelihood method, method of estima- ting unknown parame...
This article has considered methods of simulated moments for estimation of discrete response models....
Abstract This chapter discusses maximum simulated likelihood estimation when construction of the lik...
This paper discusses the increasing importance of probability simulation methods in the context of M...
Input data modeling is a critical component of a successful simulation application. A perspective of...
When a part of data is unobserved the marginal likelihood of parameters given the observed data ofte...
This book takes a fresh look at the popular and well-established method of maximum likelihood for st...
Abstract: This paper develops the important distinction between tilted and simple importance samplin...
We discuss methods for calculating multivariate normal probabilities by simulation and two new Stata...
We discuss methods for calculating multivariate normal probabilities by simulation and two new Stata...
We discuss methods for calculating multivariate normal probabilities by simulation and two new Stata...
We discuss methods for calculating multivariate normal probabilities by simulation and two new Stata...
A major stumbling block in multivariate discrete data analysis is the problem of evaluating the outc...
The method of maximum likelihood is widely used in epidemiology, yet many epidemiologists receive li...
Abstract: This paper develops the important distinction between tilted and simple importance sampli...
In this thesis we will describe the maximum likelihood method, method of estima- ting unknown parame...
This article has considered methods of simulated moments for estimation of discrete response models....
Abstract This chapter discusses maximum simulated likelihood estimation when construction of the lik...
This paper discusses the increasing importance of probability simulation methods in the context of M...
Input data modeling is a critical component of a successful simulation application. A perspective of...
When a part of data is unobserved the marginal likelihood of parameters given the observed data ofte...
This book takes a fresh look at the popular and well-established method of maximum likelihood for st...
Abstract: This paper develops the important distinction between tilted and simple importance samplin...
We discuss methods for calculating multivariate normal probabilities by simulation and two new Stata...
We discuss methods for calculating multivariate normal probabilities by simulation and two new Stata...
We discuss methods for calculating multivariate normal probabilities by simulation and two new Stata...
We discuss methods for calculating multivariate normal probabilities by simulation and two new Stata...
A major stumbling block in multivariate discrete data analysis is the problem of evaluating the outc...
The method of maximum likelihood is widely used in epidemiology, yet many epidemiologists receive li...
Abstract: This paper develops the important distinction between tilted and simple importance sampli...
In this thesis we will describe the maximum likelihood method, method of estima- ting unknown parame...
This article has considered methods of simulated moments for estimation of discrete response models....
Abstract This chapter discusses maximum simulated likelihood estimation when construction of the lik...