• Journal of the Korean Data and Information Science Society
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• v.17 no.2
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• pp.475-486
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• 2006

Space time series data can be viewed either as a set of time series collected simultaneously at a number of spatial locations or as sets of spatial data collected at a number of time points. The major purpose of this article is to formulate a class of space time autoregressive moving average (STARMA) model, to discuss some of the their statistical properties such as model identification approaches, some procedure for estimation and the predictions. For illustration, we apply this STARMA model to the mumps data. The data set of mumps cases consists of the number of cases of mumps reported from twelve states monthly over the years 1969-1988.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.108-110
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• 2019

A Database is a collection of data that does not have redundancy, and it is essential to easily use and share information in an information society where the amount of information is increasing. A typical structure of a Database is a relational database and a tree-structure Database. This research studies the programming language MUMPS, which is a tree structure database. This language constructs the database by storing arrays in a dynamic or B-Tree format. Unlike SQL, which must be used in languages such as Java and C #, MUMPS supports language and database independently and can manage data, so the data porting rate is high. In fact, in U.S. hospitals, the MUMPS-based platform has a high market share.

• Clinical and Experimental Pediatrics
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• v.57 no.9
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• pp.396-402
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• 2014

Purpose: The introduction of the mumps vaccine has dramatically reduced the number of mumps cases, but outbreaks have recently occurred among highly vaccinated populations in developed countries. Epidemiological and clinical characteristics of patients with mumps admitted between 1989 and 2012 in a single hospital in Korea are described in the present study. Methods: We retrospectively evaluated inpatients with mumps between 1989 and 2012 and outpatients and inpatients with mumps in 2011-2012. Results: A total of 152 patients with mumps were admitted between 1989 and 2012, and 163 patients were recorded in 2011-2012. The highest number of admitted cases occurred in 1998 and 2012 (35 and 34 cases, respectively). Among the patients admitted in 2011-2012, the highest frequency was observed among people aged 15-19 years, and low frequency was observed in those aged <4 years and >20 years, compatible to the city data and national data. In patients admitted to our department in 1998 (35 cases) and in 2010-2012 (27 cases), there were significant differences in the mean age and the rate of secondary measles-mumps-rubella (MMR) vaccination, but had similar clinical features, including complications, except aseptic meningitis. Antimumps immunoglobulin (Ig) G was positive in 83% and 100%, and IgM was positive in 67% and 41%, respectively, in the two periods. Conclusion: In Korea, recent mumps outbreaks have occurred mainly among secondary school students who received two doses of the MMR vaccine. The vaccinees might have a modified immune reaction to viral insults, manifesting modified epidemiological and clinical features.

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

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

• The Korean Journal of Applied Statistics
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• v.26 no.3
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• pp.521-529
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• 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
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• v.27 no.7
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• pp.1163-1170
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• 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
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• v.29 no.1
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• pp.257-266
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• 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.

• Journal of Preventive Medicine and Public Health
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• v.33 no.3
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• pp.313-322
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• 2000

Objectives : To estimate mumps incidence during the study period and to evaluate the completeness of case reporting. Methods : Capture-recapture methods, originally developed for counting wildlife animals, were used. The data sources were 1) the National Notifiable Communicable Disease Reporting System (NNCDRS; 848 cases), 2) the School Health Reporting System, temporarily administered by the Division of Education (SHRS; 1,026 cases), and 3) a survey of students (785 cases). We estimated the number of unobserved mumps cases by matching the three data sources and fitting loglinear models to the data. We then determined the estimated total number of mumps cases by adding this to the number of observed cases. Completeness was defined as the proportion of observed cases from each source to the total of estimated cases. Results : The total number of observed cases was 1,844 and the total number of estimated cases was 1,935 (95%, CI: $1,878\sim2,070$). The overall completeness was 43.8% of the NNCDRS, 53.0% of the SHRS, and 40.6% of the survey. However, completeness varied by area and age. Conclusion : Although the completeness of NNCDRS data appeared higher than in the past, it is difficult to generalize this result In Korea, it is possible to estimate the size of health hazards relatively cheaply and quickly, by applying capture-recapture methods to various data using a multiple data collection system.

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