A Monte Carlo investigation of conjoint analysis index-of-fit: Goodness of fit, significance and power

Psychometrika ◽  
1990 ◽  
Vol 55 (1) ◽  
pp. 33-44 ◽  
Author(s):  
U. N. Umesh ◽  
Sanjay Mishra
Keyword(s):  
Monte Carlo ◽  
Conjoint Analysis ◽  
Goodness Of Fit ◽  
Monte Carlo Investigation ◽  
Index Of Fit

Faking and the Validity of Personality: A Monte Carlo Investigation

2005 ◽  
Author(s):  
Shawn Komar ◽  
Jennifer Theakston ◽  
Douglas J. Brown ◽  
Chet Robie
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Investigation

Reliability and Accuracy of Bootstrap and Monte Carlo Methods for Demand Distribution Modeling

2011 ◽  
Author(s):  
Swithin S. Razu ◽  
Shun Takai
Keyword(s):  
Monte Carlo ◽  
Product Development ◽  
Conjoint Analysis ◽  
New Product Development ◽  
Demand Uncertainty ◽  
New Product ◽  
Product Development Process ◽  
Choice Data ◽  
Product Concept ◽  
Demand Distribution

Estimation of demand is one of the most important tasks in new product development. How customers come to appreciate and decide to purchase a new product impacts demand and hence profit of the product. Unfortunately, when designers select a new product concept early in the product development process, the future demand of the new product is not known. Conjoint analysis is a statistical method that has been used to estimate a demand of a new product concept from customer survey data. Although conjoint analysis has been increasingly incorporated in design engineering as a method to estimate a demand of a new product design, it has not been fully employed to model demand uncertainty. This paper demonstrates and compares two approaches that use conjoint analysis data to model demand uncertainty: bootstrap of respondent choice data and Monte Carlo simulation of utility estimation errors. Reliability of demand distribution and accuracy of demand estimation are compared for the two approaches in an illustrative example.

scholarly journals Neural Decoding Using a Parallel Sequential Monte Carlo Method on Point Processes with Ensemble Effect

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Kai Xu ◽  
Yiwen Wang ◽  
Fang Wang ◽  
Yuxi Liao ◽  
Qiaosheng Zhang ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Point Process ◽  
Goodness Of Fit ◽  
Point Processes ◽  
Sequential Monte Carlo ◽  
Neuronal Response ◽  
Position Estimation ◽  
Monte Carlo Algorithm ◽  
Sequential Monte Carlo Method ◽  
Proposed Model

Sequential Monte Carlo estimation on point processes has been successfully applied to predict the movement from neural activity. However, there exist some issues along with this method such as the simplified tuning model and the high computational complexity, which may degenerate the decoding performance of motor brain machine interfaces. In this paper, we adopt a general tuning model which takes recent ensemble activity into account. The goodness-of-fit analysis demonstrates that the proposed model can predict the neuronal response more accurately than the one only depending on kinematics. A new sequential Monte Carlo algorithm based on the proposed model is constructed. The algorithm can significantly reduce the root mean square error of decoding results, which decreases 23.6% in position estimation. In addition, we accelerate the decoding speed by implementing the proposed algorithm in a massive parallel manner on GPU. The results demonstrate that the spike trains can be decoded as point process in real time even with 8000 particles or 300 neurons, which is over 10 times faster than the serial implementation. The main contribution of our work is to enable the sequential Monte Carlo algorithm with point process observation to output the movement estimation much faster and more accurately.

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