• Communications for Statistical Applications and Methods
• /
• v.16 no.4
• /
• pp.723-730
• /
• 2009

Missing values in time series can be treated as unknown parameters and estimated by maximum likelihood or as random variables and predicted by the expectation of the unknown values given the data. The purpose of this study is to impute missing values which are regarded as the maximum likelihood estimator and random variable in incomplete data and to compare with two methods using ARMA model. For illustration, the Mumps data reported from the national capital region monthly over the years 2001 ${\sim}$ 2006 are used, and results from two methods are compared with using SSF(Sum of square for forecasting error).

• Communications for Statistical Applications and Methods
• /
• v.17 no.2
• /
• pp.263-273
• /
• 2010

Missing values in time series can be treated as unknown parameters and estimated by maximum likelihood or as random variables and predicted by the conditional expectation of the unknown values given the data. The purpose of this study is to impute missing values which are regarded as the maximum likelihood estimator and random variable in incomplete data and to compare with two methods using ARMA and STAR model. For illustration, the Mumps data reported from the national capital region monthly over the years 2001~2009 are used, and estimate precision of missing values and forecast precision of future data are compared with two methods.

• The Korean Journal of Applied Statistics
• /
• v.27 no.7
• /
• pp.1163-1170
• /
• 2014

The concepts and structure of intermittent panel time series data are introduced. We suggest a Wald test statistic for the test of homogeneity for intermittent panel first order autoregressive model and its limit distribution is derived. We consider the fitting the model with pooling data using sample mean at the time point if homogeneity for intermittent panel AR(1) is satisfied. We performed simulations to examine the limit distribution of the homogeneity test statistic for intermittent panel AR(1). In application, we fit the intermittent panel AR(1) for panel Mumps data and investigate the test of homogeneity.

• The Korean Journal of Applied Statistics
• /
• v.26 no.3
• /
• pp.521-529
• /
• 2013

The acceptance of the test of the homogeneity for panel time series models allows for the pooling of the series to achieve parsimony. In this paper, we introduce a panel bilinear time series model as well as derive the stationary condition and the limiting distribution of the test statistic of the homogeneity test for the model. For the applications study, we use Korea Mumps data from January 2001 to December 2008. Finally, we perform test of homogeneity for the panel data with 8 independent bilinear time series.

• The Korean Journal of Applied Statistics
• /
• v.29 no.1
• /
• pp.257-266
• /
• 2016

Random Coefficient Autoregressive models (RCA) have attracted increased interest due to the wide range of applications in biology, economics, meteorology and finance. We consider an RCA as an appropriate model for non-linear properties and better than an AR model for linear properties. We study the methods of RCA parameter estimation. Especially we proposed the special case that an random coefficient ${\phi}(t)$ has the initial value ${\phi}(0)$ in the RCA model. In practical study, we estimated the parameters and compared Prediction Error Sum of Squares (PRESS) criterion between AR and RCA using Korean Mumps data.

• Communications for Statistical Applications and Methods
• /
• v.16 no.1
• /
• pp.31-40
• /
• 2009

Spatial time series data can be viewed either as a set of time series collected simultaneously at a number of spatial locations. In this paper, We estimate the parameters of spatial time autoregressive moving average (SIARMA) process by method of Gibbs sampling. Finally, We apply this method to a set of U.S. Mumps data over a 12 states region.

• The Korean Journal of Applied Statistics
• /
• v.20 no.1
• /
• pp.91-102
• /
• 2007

The major purpose of this article is to formulate a class of Space Time Autoregressive Moving Average(STARMA) model and Space Time Bilinear model(STBL), to discuss some of the their statistical properties such as model, identification approaches, some procedure for estimation and the predictions, and to compare the STARMA model with the STBL model. For illustration, The Mumps data reported from eight city & provinces monthly over the years 2001-2006 are used and the result from STARMA and STBL model are compared with using SSF(Sum of Square Prediction Error).

• Journal of the Korean Data and Information Science Society
• /
• v.28 no.1
• /
• pp.11-20
• /
• 2017

In this paper, we introduced linear spatial time series (space-time autoregressive and moving average model) and nonlinear spatial time series (space-time bilinear model). Also we estimated the parameters by Kalman Filter method and made comparative studies of power of forecast in the final model. We proposed several weight matrices such as equal proportion allocation, reciprocal proportion between distances, and proportion of population sizes. For applications, we collected Mumps data at Korea Center for Disease Control and Prevention from January 2001 until August 2008. We compared three approaches of weight matrices using the Mumps data. Finally, we also decided the most effective model based on sum of square forecast error.

• Journal of agricultural medicine and community health
• /
• v.37 no.4
• /
• pp.233-245
• /
• 2012

Objectives: Understanding the predictor of immunization status in childhood is critical issue to improve National Immunization Program (NIP). The aims of this study were to verify the status of up-to-date or complete immunization coverage and to investigate its related factors. Methods: As of 2005, according to local residence registry data, there were 2,188 children who aged 12 to 35 months in Nonsan city, Korea. We conducted household survey for aged 12 to 35 months children, using questionnaires to obtain data on the status of immunization such as BCG, DTaP (diphtheria, tetanus, and pertussis), Polio, and MMR (mump, measles, rubella). Finally 1,472 participated in the survey. The operating definitions used in this study were following; "Complete immunization rate" refers to the rate of children who received all immunization within recommended age intervals fully "on-time"; "The 4:3:1 series" means status of receiving the fourth diphtheria-tetanus-pertussis (4 DTP), the third Polio (3 Polio), and the first measles-mumps-rubella (1 MMR) doses. Multivariate logistic regression analyses were used to determine factors affecting complete vaccination coverage of children. Results: Immunization rates of vaccine based on the vaccination card were from 92.7% to 96.4% except 4th DTaP (79.3%). Complete immunization rate of Korea NIP was 74.0% and that of the 4:3:1 series was 77.1%. A parent as primary caregiver (OR 0.59, 95% CI 0.39-0.87 at 19-35 months of children's age) and first-born children (OR 1.79, 95% CI 1.05-3.03 at 24-35 months of children's age) were significantly related to complete immunization coverage of Korea NIP. And a parent as primary care giver (OR 0.58, 95% CI 0.38-0.88 at 19-35 months of children's age) and first-born children (OR 1.94, 95% CI 1.21-3.14 at 19-35 months, OR 2.23, 95% CI 1.27-3.91 at 24-35 months of children's age) were significantly related to complete immunization rate of 4:3:1 series. Conclusions: Government should take actions to increase complete immunization rate. In particular, intervention on the secondary caregiver and non-first-born children should be needed.