This article proposes simulated likelihood approaches which take into account the presence of sufficient statistics in a qualitative response model. The proposed simulated likelihood estimators can be computationally less expensive and statistically efficient relative to existing ones. Besides theoretical analysis, Monte Carlo results provide some evidence. (C) 1997 Elsevier Science S.A
This chapter compares the performance of the maximum simulated likelihood (MSL) approach with the co...
A procedure for calculating critical level and power of likelihood ratio test, based on a Monte-Carl...
Simulator models are a type of stochastic model that is often used to approximate a real-life proces...
A major stumbling block in multivariate discrete data analysis is the problem of evaluating the outc...
This article has considered methods of simulated moments for estimation of discrete response models...
This article has considered methods of simulated moments for estimation of discrete response models....
This paper looks at the problem of performing likelihood inference for limited dependent processes. ...
In this paper, we develop a likelihood approach for quantification of qualitative survey data on exp...
This paper looks at the problem of performing likelihood inference for limited dependent processes. ...
Complex statistical models pose a great challenge to practitioners because of methodological and com...
This paper discusses the increasing importance of probability simulation methods in the context of M...
Response time has become increasingly important for analyzing the relationship between the proficien...
Our paper deals with inferring simulator-based statistical models given some observed data. A simula...
In the process of trying to estimate a behavioral equation (either structural or reduced from) deriv...
Approximate Bayesian computation has become an essential tool for the analysis of complex stochastic...
This chapter compares the performance of the maximum simulated likelihood (MSL) approach with the co...
A procedure for calculating critical level and power of likelihood ratio test, based on a Monte-Carl...
Simulator models are a type of stochastic model that is often used to approximate a real-life proces...
A major stumbling block in multivariate discrete data analysis is the problem of evaluating the outc...
This article has considered methods of simulated moments for estimation of discrete response models...
This article has considered methods of simulated moments for estimation of discrete response models....
This paper looks at the problem of performing likelihood inference for limited dependent processes. ...
In this paper, we develop a likelihood approach for quantification of qualitative survey data on exp...
This paper looks at the problem of performing likelihood inference for limited dependent processes. ...
Complex statistical models pose a great challenge to practitioners because of methodological and com...
This paper discusses the increasing importance of probability simulation methods in the context of M...
Response time has become increasingly important for analyzing the relationship between the proficien...
Our paper deals with inferring simulator-based statistical models given some observed data. A simula...
In the process of trying to estimate a behavioral equation (either structural or reduced from) deriv...
Approximate Bayesian computation has become an essential tool for the analysis of complex stochastic...
This chapter compares the performance of the maximum simulated likelihood (MSL) approach with the co...
A procedure for calculating critical level and power of likelihood ratio test, based on a Monte-Carl...
Simulator models are a type of stochastic model that is often used to approximate a real-life proces...
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