Alternative Estimation Methods for Conjoint Analysis: A Monté Carlo Study

1981 ◽  
Vol 18 (1) ◽  
pp. 101-106 ◽  
Author(s):  
Dick R. Wittink ◽  
Philippe Cattin
Keyword(s):  
Monte Carlo ◽  
Conjoint Analysis ◽  
Predictive Validity ◽  
Simulation Study ◽  
Rank Order ◽  
Monte Carlo Study ◽  
Estimation Methods ◽  
Preference Rank ◽  
Estimation Procedures ◽  
Simulation Results

Conjoint analysis has been applied in a large number of commercial projects as well as in many noncommercial studies. Often MONANOVA, a nonmetric technique, is applied to a preference rank order obtained for a set of hypothetical objects. The authors report simulation results obtained for four alternative estimation procedures, ANOVA, LINMAP, LOGIT, and MONANOVA. The results suggest, within the limitations of the simulation study, that ANOVA may be the preferred procedure for compensatory models, whereas LINMAP is most likely to provide the best predictive validity for models with a dominant attribute.

Some Results on the Behavior of Alternate Covariance Structure Estimation Procedures in the Presence of Non-Normal Data

1989 ◽  
Vol 26 (2) ◽  
pp. 214-221 ◽  
Author(s):  
Subhash Sharma ◽  
Srinivas Durvasula ◽  
William R. Dillon
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Covariance Structure ◽  
Standard Errors ◽  
Parameter Estimates ◽  
Simulation Experiments ◽  
Estimation Techniques ◽  
Structure Estimation ◽  
Estimation Procedures ◽  
Simulation Results

The authors report some results on the behavior of alternative covariance structure estimation procedures in the presence of non-normal data. They conducted Monté Carlo simulation experiments with a factorial design involving three levels of skewness, three level of kurtosis, and three different sample sizes. For normal data, among all the elliptical estimation techniques, elliptical reweighted least squares (ERLS) was equivalent in performance to ML. However, as expected, for non-normal data parameter estimates were unbiased for ML and the elliptical estimation techniques, whereas the bias in standard errors was substantial for GLS and ML. Among elliptical estimation techniques, ERLS was superior in performance. On the basis of the simulation results, the authors recommend that researchers use ERLS for both normal and non-normal data.

scholarly journals Detecting Item Preknowledge Using a Predictive Checking Method

2017 ◽  
Vol 41 (4) ◽  
pp. 243-263 ◽  
Author(s):  
Xi Wang ◽  
Yang Liu ◽  
Ronald K. Hambleton
Keyword(s):  
Simulation Study ◽  
Type I Error ◽  
Predictive Distribution ◽  
Estimation Methods ◽  
Type I ◽  
Test Statistic ◽  
High Stake ◽  
Power Rate ◽  
Simulation Results ◽  
Low Exposure

Repeatedly using items in high-stake testing programs provides a chance for test takers to have knowledge of particular items in advance of test administrations. A predictive checking method is proposed to detect whether a person uses preknowledge on repeatedly used items (i.e., possibly compromised items) by using information from secure items that have zero or very low exposure rates. Responses on the secure items are first used to estimate a person’s proficiency distribution, and then the corresponding predictive distribution for the person’s responses on the possibly compromised items is constructed. The use of preknowledge is identified by comparing the observed responses to the predictive distribution. Different estimation methods for obtaining a person’s proficiency distribution and different choices of test statistic in predictive checking are considered. A simulation study was conducted to evaluate the empirical Type I error and power rate of the proposed method. The simulation results suggested that the Type I error of this method is well controlled, and this method is effective in detecting preknowledge when a large proportion of items are compromised even with a short secure section. An empirical example is also presented to demonstrate its practical use.

scholarly journals Monte Carlo Study of a Liquid-Liquid Phase Transition Using a Modified Gibbs Ensemble

2020 ◽  
Author(s):  
Douglas Barlow
Keyword(s):  
Monte Carlo ◽  
Critical Temperature ◽  
Monte Carlo Study ◽  
Low Energy ◽  
Gibbs Ensemble ◽  
Two Component System ◽  
Liquid Phase Transition ◽  
Solution Type ◽  
Two Component ◽  
Simulation Results

<div>The carbon disulfide-methanol liquid-liquid critical point is studied using a Monte</div><div>Carlo simulation of classical Stockmayer particles. A low energy configuration for the segregated</div><div>two component system is determined using standard Monte Carlo methods then a modified</div><div>Gibbs ensemble is employed to study the effect of transferring particles from one phase to</div><div>another. Rather than use the model for the entropy of mixing in the Gibbs ensemble, which is</div><div>of the regular solution type, a semi-quasi-chemical model is used which involves an interaction</div><div>energy. We are able to simulate the mixing of the two components as the temperature approaches</div><div>the critical temperature from below. Further, a method is given whereby the simulation results</div><div>can be used to predict the critical temperature.</div>

scholarly journals Monte Carlo Study of a Liquid-Liquid Phase Transition Using a Modified Gibbs Ensemble

2020 ◽  
Author(s):  
Douglas Barlow
Keyword(s):  
Monte Carlo ◽  
Critical Temperature ◽  
Monte Carlo Study ◽  
Low Energy ◽  
Gibbs Ensemble ◽  
Two Component System ◽  
Liquid Phase Transition ◽  
Solution Type ◽  
Two Component ◽  
Simulation Results

<div>The carbon disulfide-methanol liquid-liquid critical point is studied using a Monte</div><div>Carlo simulation of classical Stockmayer particles. A low energy configuration for the segregated</div><div>two component system is determined using standard Monte Carlo methods then a modified</div><div>Gibbs ensemble is employed to study the effect of transferring particles from one phase to</div><div>another. Rather than use the model for the entropy of mixing in the Gibbs ensemble, which is</div><div>of the regular solution type, a semi-quasi-chemical model is used which involves an interaction</div><div>energy. We are able to simulate the mixing of the two components as the temperature approaches</div><div>the critical temperature from below. Further, a method is given whereby the simulation results</div><div>can be used to predict the critical temperature.</div>

Stochastic Input-Output Analysis: A Simulation Study

1982 ◽  
Vol 14 (11) ◽  
pp. 1425-1435 ◽  
Author(s):  
D J Hanseman
Keyword(s):  
Monte Carlo ◽  
Simulation Study ◽  
Monte Carlo Study ◽  
Small Sample ◽  
Input Output ◽  
Output Analysis ◽  
Input Output Analysis ◽  
Regression Estimators ◽  
Stochastic Input

In the first part of the paper, the idea of a stochastic input-output model, as conceived by Gerking, is reviewed and criticized; and some extensions and simplifications are then made. The second part seeks to assess the small sample behavior of several regression estimators proposed by Gerking. Since samples available for the construction of regional tables are typically quite small, this Monte Carlo study provides evidence as to which of the proposed estimators is most feasible for the actual empirical construction of regional input-output tables.

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