Time-Varying Transition Probability Matrix Estimation and Its Application to Brand Share Analysis.
PLoS One
; 12(1): e0169981, 2017.
Article
en En
| MEDLINE
| ID: mdl-28076383
ABSTRACT
In a product market or stock market, different products or stocks compete for the same consumers or purchasers. We propose a method to estimate the time-varying transition matrix of the product share using a multivariate time series of the product share. The method is based on the assumption that each of the observed time series of shares is a stationary distribution of the underlying Markov processes characterized by transition probability matrices. We estimate transition probability matrices for every observation under natural assumptions. We demonstrate, on a real-world dataset of the share of automobiles, that the proposed method can find intrinsic transition of shares. The resulting transition matrices reveal interesting phenomena, for example, the change in flows between TOYOTA group and GM group for the fiscal year where TOYOTA group's sales beat GM's sales, which is a reasonable scenario.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Automóviles
/
Algoritmos
/
Estadística como Asunto
/
Comercio
/
Comportamiento del Consumidor
Tipo de estudio:
Health_economic_evaluation
Límite:
Humans
Idioma:
En
Revista:
PLoS One
Asunto de la revista:
CIENCIA
/
MEDICINA
Año:
2017
Tipo del documento:
Article
País de afiliación:
Japón