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Item type:Publication, Comparing the first and the second orders of random coefficient autoregressive model on time series data(2019-07-08); Banditvilai, SomsriThe random coefficient autoregressive (RCA) model develops from the autoregressive model and the hierarchical model. The RCA model has considered a constant parameter and coefficient parameter depended on past data. The least squares method is a widely used method by minimizing the sum of squared residuals and differential with respect to the unknown parameter. In this paper, the concept of the least squares method is used to estimate an unknown parameter of the first and the second orders of Random Coefficient Autoregressive (RCA) model or called RCA(1) and RCA(2) models. The efficiency of the two models is to compare by considering the minimum value of mean square error. The RCA(1) and RCA(2) are then applied to a time series data in the form of nonstationary data. The monthly averages of the Stock Exchange of Thailand (SET) index and the daily volume of exchange rate Baht/Dollar are fitted on these models. The prediction of RCA(1) and RCA(2) models is shown that the RCA(l) model outperforms the RCA(2) model, similar to two data sets. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Forecasting Models for Total Crude Palm Oil Productions in Thailand(2024-12-02) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Comparing penalized regression analysis of logistic regression model with multicollinearity(2019-07-08); Kuharatanachai, ChoojaiThe goal of this research is to estimate the parameter of the logistic regression model by penalized regression analysis which consisted of ridge regression, lasso, and elastic net method. The logistic regression is considered between a binary dependent variable and 3 and 5 independent variables. The independent variables are generated from normal distribution, contaminated normal distribution, and t distribution on correlation coefficient at 0.1, 0.5, and 0.99 or called multicollinearity problem. The maximum likelihood estimator has used as the classical method by differential the log likelihood function with respect to the coefficients. Ridge regression is to choose the unknown ridge parameter by cross-validation, so ridge estimator is evaluated by the adding ridge parameter on penalty term. Lasso (least absolute shrinkage and selection operator) is added the penalty term on scales sum of the absolute value of the coefficients. The elastic net can be mixed between ridge regression and lasso on the penalty term. The criterion of these methods is compared by percentage of predicted accuracy value. The results are found that lasso is satisfied when the independent variables are simulated from normal and t distribution in most cases, and the lasso outperforms on the contaminated normal distribution. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Comparison of Machine Learning Methods for Binary Classification of Multicollinearity Data(2024-12-02); This study examines the effectiveness of binary classification performance in multicollinearity. Four machine learning methods, namely backpropagation neural network, Naïve Bayes, support vector machine, and random forest, are compared in terms of their efficiency in handling multicollinear data. The evaluation of binary classification performance efficiency considers multicollinearity in independent variables, considering both a constant correlation model and the Toeplitz correlation. Correlation coefficients of 0.1 and 0.9 are explored in the analysis. The independent variables in this study are simulated from a multivariate normal distribution with 10, 20, 30, and 40 variables, respectively. The dependent variable is constructed using the logit function with sample sizes of 100 and 200. The simulation and data analysis are performed using the R Studio program and repeated 1,000 times for each scenario. The findings of this research reveal that the backpropagation neural network and Naïve Bayes methods exhibit superior performance in determining the mean accuracy percentage under constant correlation. On the other hand, the backpropagation neural network and support vector machine are the most effective methods in determining the mean accuracy percentage when dealing with multicollinearity in the form of Toeplitz correlation. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Bayesian Approach for Comparing Parameter Estimation of Regression Model for Outlier Data(2022-06-17); This research compares and contrasts the simple regression model's parameter estimation methods, which consisted of a dependent variable and one independent variable. Parameter estimation uses the ordinary least square method, Bayesian method, Markov Chain Monte Carlo (MCMC) method, and local weight Markov Chain Monte Carlo (LWMCMC) method. The standard method is the ordinary least square method, which uses the concept of minimum sum square error to estimate parameters for fitting the linear regression model. However, for a set of the parameter relating to the Bayesian approach, the use of prior and posterior distributions may affect the approximation of the Bayesian, MCMC, LWMCMC methods. This paper considers the ordinal least square method and Bayesian approach by estimating the parameter for outlier data while some data points are far from other observations. The independent variable is simulated from the contaminated normal distribution, and the error is simulated from the normal distribution that made the outlier data on dependent and independent variables for the several sample sizes as 20, 50, 100, and 200. The criterion of the best efficiency is considered by the minimum of the average mean square errors. Through simulation data, the Bayesian method presents the minimum of average mean square errors at the sample sizes 20 and 50. However, when the sample size value increases, the MCMC and LWMCMC method are the best efficiency method at the sample sizes 100 and 200, respectively.