An Evaluation of the Application of Minimum Chi-Square Procedures to Stochastic Models of Brand Choice

1973 ◽  
Vol 10 (4) ◽  
pp. 421 ◽  
Author(s):  
Robert C. Blattberg ◽  
Subrata K. Sen
Keyword(s):  
Stochastic Models ◽  
Brand Choice ◽  
Chi Square

An Evaluation of the Application of Minimum Chi-Square Procedures to Stochastic Models of Brand Choice

1973 ◽  
Vol 10 (4) ◽  
pp. 421-427 ◽  
Author(s):  
Robert C. Blattberg ◽  
Subrata K. Sen
Keyword(s):  
Stochastic Models ◽  
Brand Choice ◽  
Choice Models ◽  
Statistical Test ◽  
Small Sample ◽  
The Other ◽  
Chi Square ◽  
Small Sample Properties

This paper investigates the small sample properties of minimum chi-square estimates of the parameters of stochastic brand choice models. It also describes and evaluates a statistical test which is appropriate for discriminating between two stochastic brand choice models when one is a constrained version of the other.

A New Method for Evaluating Stochastic Models of Brand Choice

1970 ◽  
Vol 7 (3) ◽  
pp. 300-306 ◽  
Author(s):  
David A. Aaker
Keyword(s):  
Stochastic Models ◽  
Goodness Of Fit ◽  
Value Function ◽  
Choice Model ◽  
Brand Choice ◽  
Mean Value ◽  
Model Fit ◽  
New Method ◽  
Structural Comparison ◽  
Goodness Of Fit Test

This article explores the use of a brand choice stochastic model's mean value function in evaluating two models empirically, using a common set of purchase data. The linear learning model fit the data well, but its mean value function was not capable of making reasonable predictions of successive, aggregate purchasing statistics. Another brand choice model, the new trier model, was found to perform much better. The results suggest that model tests should not be restricted to the usual goodness-of-fit test, especially in situations of non-stationarity. A structural comparison of the two models focuses on their different approaches to nonstationarity.

Determination of Optimal Sample Sizes in the Beta-Binomial Brand Choice Model

1980 ◽  
Vol 17 (1) ◽  
pp. 58-62 ◽  
Author(s):  
Moshe M. Givon
Keyword(s):  
Stochastic Models ◽  
Choice Behavior ◽  
Choice Model ◽  
Brand Choice ◽  
Binomial Model ◽  
Sample Sizes ◽  
Estimation Of Parameters ◽  
Optimal Sample

Estimation of parameters in stochastic models of brand choice behavior requires the determination of two sample sizes: the number of consumers and the number of purchase occasions observed for each consumer. A method for determination of optimal sample sizes is presented and illustrated by an application to the beta-binomial model.

A Model of Intermediate Market Response

1972 ◽  
Vol 9 (4) ◽  
pp. 378-384 ◽  
Author(s):  
Douglas L. Maclachlan
Keyword(s):  
Markov Model ◽  
Market Share ◽  
Stochastic Models ◽  
Brand Choice ◽  
Econometric Models ◽  
Market Response ◽  
Marketing Decision ◽  
Decision Variables ◽  
Traditional Market

Traditional market share models can be elaborated by combining nonstationary stochastic models of brand choice with econometric models. A variable Markov model is described which allows assessment of marketing decision variables’ influence on the underlying composition of market share.

Pemodelan Data Multi-Label dengan Pendekatan Multivariate Generalized Linear Mixed Model (MGLMM)

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Dairul Fuhron ◽  
. Indahwati ◽  
Farit Mochamad Afendi
Keyword(s):  
Fixed Effects ◽  
Market Research ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Random Effect ◽  
Brand Choice ◽  
Transformation Method ◽  
Time Frame ◽  
Chi Square ◽  
Label Data

Multi-label data refers to a type of categorical data where an object may has more than one corresponding label or possible values. Multi-label data are commonly found in many fields, one of them is market research of the sweetened condensed milk (SCM) and sweetened condensed creamer (SCC) products. According to product characteristic, market research for the aforementioned product is appropriately conducted on the outlet level. An outlet may use more than one product’s brand in the same time frame. That condition inflict brand choice information to be represented under multi-label data. This research used problem transformation method by tranforming a multi-label variable into several single-label variables. Multivariate Generalized Linear Mixed Modeling or MGLMM was selected under consideration of binary multiple responses and correlated responses presumption. Five responses of SCM and SCC brand choice modeling resulted correct model without overdispersion and the scaled pearson chi square statistic is 0.99. Tests of fixed effects indicate three factor significantly affect SCM and SCC brand choice at the 5% level. They are purchase total, province, and type of business. The variance of the random effect intercept is 1.53×10−18 or insignificant, hence MGLMM based model was similar compare to separated GLM based model.

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