The application of the Bootstrap-Metropolis-Hastings algorithm is limited to fixed dimension models. In various fields, data often has a variable dimension model. The Laplacian autoregressive (AR) model includes a variable dimension model so that the Bootstrap-Metropolis-Hasting algorithm cannot be applied. This article aims to develop a Bootstrap reversible jump Markov Chain Monte Carlo (MCMC) algorithm to estimate the Laplacian AR model. The parameters of the Laplacian AR model were estimated using a Bayesian approach. The posterior distribution has a complex structure so that the Bayesian estimator cannot be calculated analytically. The Bootstrap-reversible jump MCMC algorithm was applied to calculate the Bayes estimator. This study provides a procedure for estimating the parameters of the Laplacian AR model. Algorithm performance was tested using simulation studies. Furthermore, the algorithm is applied to the finance sector to predict stock price on the stock market. In general, this study can be useful for decision makers in predicting future events. The novelty of this study is the triangulation between the bootstrap algorithm and the reversible jump MCMC algorithm. The Bootstrap-reversible jump MCMC algorithm is useful especially when the data is large and the data has a variable dimension model. The study can be extended to the Laplacian Autoregressive Moving Average (ARMA) model.
This study discusses the customer experience that supposedly affects customer satisfaction of tourists in the province of Riau Islands of Bintan Regency. In this study the variable dimension measured through experinece sense, feel, think, act and relate. Research data processed with using their analysis of multiple linear regression and factor. The results of this research show that customer experience significant influential variable and positive towards consumer satisfaction (customer satisfaction). The result of the research shows that experience variable with dimensions of sense, feel, think, act and relate simultaneously against influential customer satisfaction. In partial dimensions factor sense, feel, think and relate that give may influence against the satisfaction and sense of dominant influence dimensions against customer satisfaction.
Usually scientists build physical models depending on how they perceive the world. But the current state of affairs in science has shown that where the scale is very small compared to our usual world, it is not justified to use models that could be used in the macro world. One of the options that can take place in the micro world, but has no analogues in our ordinary world, which we observe every day, is that space can change or have a fractional dimension. It is possible that the dimension of space will have certain values, depending on the conditions in which our complex system is observed in space, or depending on the frame of reference of the observer. And thus the calculations in the mathematical modeling of complex systems must be adjusted in accordance with the dimension of space.
Clustering and Prediction with Variable Dimension Covariates