Araveeporn, Autcha
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Item type:Publication, The Estimating Parameter and Number of Knots for Nonparametric Regression Methods in Modelling Time Series Data(2024-12-01)This research aims to explore and compare several nonparametric regression techniques, including smoothing splines, natural cubic splines, B-splines, and penalized spline methods. The focus is on estimating parameters and determining the optimal number of knots to forecast cyclic and nonlinear patterns, applying these methods to simulated and real-world datasets, such as Thailand’s coal import data. Cross-validation techniques are used to control and specify the number of knots, ensuring the curve fits the data points accurately. The study applies nonparametric regression to forecast time series data with cyclic patterns and nonlinear forms in the dependent variable, treating the independent variable as sequential data. Simulated data featuring cyclical patterns resembling economic cycles and nonlinear data with complex equations to capture variable interactions are used for experimentation. These simulations include variations in standard deviations and sample sizes. The evaluation criterion for the simulated data is the minimum average mean square error (MSE), which indicates the most efficient parameter estimation. For the real data, monthly coal import data from Thailand is used to estimate the parameters of the nonparametric regression model, with the MSE as the evaluation metric. The performance of these techniques is also assessed in forecasting future values, where the mean absolute percentage error (MAPE) is calculated. Among the methods, the natural cubic spline consistently yields the lowest average mean square error across all standard deviations and sample sizes in the simulated data. While the natural cubic spline excels in parameter estimation, B-splines show strong performance in forecasting future values. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, An estimating parameter of nonparametric regression model based on smoothing techniques(2019-01-01)This paper studies the estimating parameter of a nonparametric regression model that consists of the function of independent variables and observation of dependent variables. The smoothing spline, penalized spline, and B-spline methods in a class of smoothing techniques are considered for estimating the unknown parameter on nonparametric regression model. These methods use a smoothing parameter to control the smoothing performance on data set by using a cross-validation method. We also compare these methods by fitting a nonparametric regression model on simulation data and real data. The nonlinear model is a simulation data which is generated in two different models in terms of mathematical function based on statistical distribution. According to the results, the smoothing spline, the penalized spline, and the B-spline methods have a good performance to fit nonlinear data by considering the hypothesis testing of biased estimator. However the penalized spline method shows the minimum mean square errors on two models. As real data, we use the data from a light detection and ranging (LIDAR) experiment that contained the range distance travelled before the light as an independent variable and the logarithm of the ratio of received light from two laser sources as a dependent variable. From the mean square errors of fitting data, the penalized spline again shows the minimum values. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Improved Probability-Weighted Moments and Two-Stage Order Statistics Methods of Generalized Extreme Value Distribution(2025-07-01)This study evaluates six parameter estimation methods for the generalized extreme value (GEV) distribution: maximum likelihood estimation (MLE), two probability-weighted moments (PWM-UE and PWM-PP), and three robust two-stage order statistics estimators (TSOS-ME, TSOS-LMS, and TSOS-LTS). Their performance was assessed using simulation experiments under varying tail behaviors, represented by three types of GEV distributions: Weibull (short-tailed), Gumbel (light-tailed), and Fréchet (heavy-tailed) distributions, based on the mean squared error (MSE) and mean absolute percentage error (MAPE). The results showed that TSOS-LTS consistently achieved the lowest MSE and MAPE, indicating high robustness and forecasting accuracy, particularly for short-tailed distributions. Notably, PWM-PP performed well for the light-tailed distribution, providing accurate and efficient estimates in this specific setting. For heavy-tailed distributions, TSOS-LTS exhibited superior estimation accuracy, while PWM-PP showed a better predictive performance in terms of MAPE. The methods were further applied to real-world monthly maximum PM2.5 data from three air quality stations in Bangkok. TSOS-LTS again demonstrated superior performance, especially at Thon Buri station. This research highlights the importance of tailoring estimation techniques to the distribution’s tail behavior and supports the use of robust approaches for modeling environmental extremes. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, An Enhanced Discriminant Analysis Approach for Multi-Classification with Integrated Machine Learning-Based Missing Data Imputation(2025-11-01); This study addresses the challenge of accurate classification under missing data conditions by integrating multiple imputation strategies with discriminant analysis frameworks. The proposed approach evaluates six imputation methods (Mean, Regression, KNN, Random Forest, Bagged Trees, MissRanger) across several discriminant techniques. Simulation scenarios varied in sample size, predictor dimensionality, and correlation structure, while the real-world application employed the Cirrhosis Prediction Dataset. The results consistently demonstrate that ensemble-based imputations, particularly regression, KNN, and MissRanger, outperform simpler approaches by preserving multivariate structure, especially in high-dimensional and highly correlated settings. MissRanger yielded the highest classification accuracy across most discriminant analysis methods in both simulated and real data, with performance gains most pronounced when combined with flexible or regularized classifiers. Regression imputation showed notable improvements under low correlation, aligning with the theoretical benefits of shrinkage-based covariance estimation. Across all methods, larger sample sizes and high correlation enhanced classification accuracy by improving parameter stability and imputation precision. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Developing nonparametric conditional heteroscedastic autoregressive nonlinear model by using maximum likelihood method(2011-01-01)The goal of this work is to develop a nonparametric conditional heteroscedastic autoregressive nonlinear (NCHARN) model by using maximum likelihood method that not only account for possibly non-linear trend but also account for possibly non-linear conditional variance of response as a function of predictor variables in the presence of auto-correlated errors. The trend and the heteroscedasticity are modeled using a class of penalized spline and the residuals are modeled as a autoregressive process (AR) by selecting an appropriate number of lag residuals. Both classical penalized spline and AR process of penalized spline under NCHARN model are developed to obtain the smooth estimates of the conditional mean and variance functions. The resulting estimated values are then used the maximum likelihood method to fi t a trend, volatility, and a coeffi cient of AR process by suitably choosing the order of AR using the Akaike Information Criteria (AIC). The forecasting performance of the proposed methods is then applied to the series of monthly observations of the Stock Exchange Rate of Thailand (SERT) to illustrate the methodology. The forecasts these methods are compared with those obtained based on future six months of withheld observations. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Bayesian Approach for Confidence Intervals of Variance on the Normal Distribution(2022-01-01)This research aims to compare estimating the confidence intervals of variance based on the normal distribution with the primary method and the Bayesian approach. The maximum likelihood is the well-known method to approximate variance, and the Chi-squared distribution performs the confidence interval. The central Bayesian approach forms the posterior distribution that makes the variance estimator, which depends on the probability and prior distributions. Most introductory prior information looks for the availability of the prior distribution, informative prior distribution, and noninformative prior distribution. The gamma, Chi-squared, and exponential distributions are defined in the prior distribution. The informative prior distribution uses the Markov Chain Monte Carlo (MCMC) method to draw the random sample from the posterior distribution. The Fisher information performs the Wald confidence interval as the noninformative prior distribution. The interval estimation of the Bayesian approach is obtained from the central limit theorem. The performance of these methods considers the coverage probability and minimum value of the average width. The Monte Carlo process simulates the data from a normal distribution with the true parameter of mean and several variances and the sample sizes. The R program generates the simulated data repeated 10,000 times in each situation. The results showed that the maximum likelihood method employed on the small sample sizes. The best confidence interval estimation was when sample sizes increased the Bayesian approach with an available prior distribution. Overall, the Wald confidence interval tended to outperform the large sample sizes. For application in real data, we expressed the reported airborne particulate matter of 2.5 in Bangkok, Thailand. We used the 10-1000 records to estimate the confidence interval of variance and evaluated the interval width. The results are similar to those of the simulation study. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Comparing random coefficient autoregressive model with and without autocorrelated errors by Bayesian analysis(2017-01-01)We proposed a Bayesian analysis for estimating an unknown parameter in a Random Coefficient Autoregressive (RCA) model and its AutoRegressive (AR) process errors. We called this model an RCA model with autocorrelated errors (RCA-AR). A Markov Chain Monte Carlo (MCMC) method was used to generate samples from a posterior distribution which, after having been averaged, gave the estimated value of the unknown parameter. We used a Gibbs sampling algorithm in our MCMC calculation. To compare the performances of the RCA and the RCA-AR models, a simulation was performed with a set of test data and then the mean square errors obtained were used to indicate their performance. The result was that the RCA-AR model worked better than the RCA model in every case. Lastly, we tried both models with real data. They were used to estimate a series of monthly averages of the Stock Exchange of Thailand (SET) index. The result was that the RCA-AR still worked better than the RCA model, similar to the simulation of test data. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Empirical Comparison of Forecasting Methods for Air Travel and Export Data in Thailand(2024-12-01) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Estimating conditional heteroscedastic nonlinear autoregressive model by using smoothing spline and penalized spline methods(2019-07-01)We propose smoothing spline (SS) and penalized spline (PS) methods in a class of nonparametric regression methods for estimating the unknown functions in a conditional heteroscedastic nonlinear autoregressive (CHNLAR) model. The CHNLAR model consists of a trend and heteroscedastic functions in terms of past data at lag 1. The SS and PS methods were tested in estimating the unknown functions used to transform data so that it fits the trend and the heteroscedastic functions. In a simulation study, time series data were generated and hypothesis testing of the bias was used to check the accuracy. The SS and PS methods exhibit a good power estimation in most cases of generated data. As real data, gold price was modeled by using SS and PS methods in the CHNLAR model. The results show that the SS method performed better than the PS method. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Comparing parameter estimation of random coefficient autoregressive model by frequentist method(2020-01-01)This paper compares the frequentist method that consisted of the least-squares method and the maximum likelihood method for estimating an unknown parameter on the Random Coefficient Autoregressive (RCA) model. The frequentist methods depend on the likelihood function that draws a conclusion from observed data by emphasizing the frequency or proportion of the data namely least squares and maximum likelihood methods. The method of least squares is often used to estimate the parameter of the frequentist method. The minimum of the sum of squared residuals is found by setting the gradient to zero. The maximum likelihood method carries out the observed data to estimate the parameter of a probability distribution by maximizing a likelihood function under the statistical model, while this estimator is obtained by a differential parameter of the likelihood function. The efficiency of two methods is considered by average mean square error for simulation data, and mean square error for actual data. For simulation data, the data are generated at only the first-order models of the RCA model. The results have shown that the least-squares method performs better than the maximum likelihood. The average mean square error of the least-squares method shows the minimum values in all cases that indicated their performance. Finally, these methods are applied to the actual data. The series of monthly averages of the Stock Exchange of Thailand (SET) index and daily volume of the exchange rate of Baht/Dollar are considered to estimate and forecast based on the RCA model. The result shows that the least-squares method outperforms the maximum likelihood method.