**Commenced**in January 2007

**Frequency:**Monthly

**Edition:**International

**Paper Count:**4

# Search results for: Y. Areepong

##### 4 Numerical Approximation to the Performance of CUSUM Charts for EMA (1) Process

**Authors:**
K. Petcharat,
Y. Areepong,
S. Sukparungsri,
G. Mititelu

**Abstract:**

**Keywords:**
Cumulative Sum Chart,
Moving Average
Observation,
Average Run Length,
Numerical Approximations.

##### 3 The Performance of Predictive Classification Using Empirical Bayes

**Authors:**
N. Deetae,
S. Sukparungsee,
Y. Areepong,
K. Jampachaisri

**Abstract:**

This research is aimed to compare the percentages of correct classification of Empirical Bayes method (EB) to Classical method when data are constructed as near normal, short-tailed and long-tailed symmetric, short-tailed and long-tailed asymmetric. The study is performed using conjugate prior, normal distribution with known mean and unknown variance. The estimated hyper-parameters obtained from EB method are replaced in the posterior predictive probability and used to predict new observations. Data are generated, consisting of training set and test set with the sample sizes 100, 200 and 500 for the binary classification. The results showed that EB method exhibited an improved performance over Classical method in all situations under study.

**Keywords:**
Classification,
Empirical Bayes,
Posterior predictive probability.

##### 2 Optimal Parameters of Double Moving Average Control Chart

**Authors:**
Y. Areepong

**Abstract:**

**Keywords:**
Optimal parameters,
Average Run Length,
Average Delay time,
Double Moving Average chart.

##### 1 Optimal Design for SARMA(P,Q)L Process of EWMA Control Chart

**Authors:**
Y. Areepong

**Abstract:**

The main goal of this paper is to study Statistical Process Control (SPC) with Exponentially Weighted Moving Average (EWMA) control chart when observations are serially-correlated. The characteristic of control chart is Average Run Length (ARL) which is the average number of samples taken before an action signal is given. Ideally, an acceptable ARL of in-control process should be enough large, so-called (ARL_{0}). Otherwise it should be small when the process is out-of-control, so-called Average of Delay Time (ARL_{1}) or a mean of true alarm. We find explicit formulas of ARL for EWMA control chart for Seasonal Autoregressive and Moving Average processes (SARMA) with Exponential white noise. The results of ARL obtained from explicit formula and Integral equation are in good agreement. In particular, this formulas for evaluating (ARL_{0}) and (ARL_{1}) be able to get a set of optimal parameters which depend on smoothing parameter (λ) and width of control limit (H) for designing EWMA chart with minimum of (ARL_{1}).

**Keywords:**
Average Run Length1,
Optimal parameters,
Exponentially Weighted Moving Average (EWMA) control chart.