• Journal of Ecology and Environment
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• v.43 no.2
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• pp.191-201
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• 2019

Macroecologists and biogeographers continue to predict the distribution of species across space based on the relationship between biotic processes and environmental variables. This approach uses data related to, for example, species abundance or presence/absence, climate, geomorphology, and soils. Researchers have acknowledged in their statistical analyses the importance of accounting for the effects of spatial autocorrelation (SAC), which indicates a degree of dependence between pairs of nearby observations. It has been agreed that residual spatial autocorrelation (rSAC) can have a substantial impact on modeling processes and inferences. However, more attention should be paid to the sources of rSAC and the degree to which rSAC becomes problematic. Here, we review previous studies to identify diverse factors that potentially induce the presence of rSAC in macroecological and biogeographical models. Furthermore, an emphasis is put on the quantification of rSAC by seeking to unveil the magnitude to which the presence of SAC in model residuals becomes detrimental to the modeling process. It turned out that five categories of factors can drive the presence of SAC in model residuals: ecological data and processes, scale and distance, missing variables, sampling design, and assumptions and methodological approaches. Additionally, we noted that more explicit and elaborated discussion of rSAC should be presented in species distribution modeling. Future investigations involving the quantification of rSAC are recommended in order to understand when rSAC can have an adverse effect on the modeling process.

• Survey Research
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• v.9 no.2
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• pp.1-27
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• 2008

Problems of incomplete data are pervasive in statistical analysis. In particular, incomplete data have been an important challenge in repeated measures studies. The objective of this study is to give a brief introduction to missing data mechanisms and conventional/recent missing data methods and to assess the performance of various missing data methods under ignorable and non-ignorable missingness mechanisms. Given the inadequate attention to longitudinal studies with missing data, this study applied recent advances in missing data methods to repeated measures models and investigated the performance of various missing data methods, such as FIML (Full Information Maximum Likelihood Estimation) and MICE(Multivariate Imputation by Chained Equations), under MCAR, MAR, and MNAR mechanisms. Overall, the results showed that listwise deletion and mean imputation performed poorly compared to other recommended missing data procedures. The better performance of EM, FIML, and MICE was more noticeable under MAR compared to MCAR. With the non-ignorable missing data, this study showed that missing data methods did not perform well. In particular, this problem was noticeable in slope-related estimates. Therefore, this study suggests that if missing data are suspected to be non-ignorable, developmental research may underestimate true rates of change over the life course. This study also suggests that bias from non-ignorable missing data can be substantially reduced by considering rich information from variables related to missingness.

• 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.

• The Korean Journal of Applied Statistics
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• v.19 no.1
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• pp.57-67
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• 2006

In sampling survey, nonresponse tend to occur inevitably. If we use information from respondents only, the estimates will be baised. To overcome this, various non-response imputation methods have been studied. If there are few auxiliary variables for replacing missing imputation or spatial autocorrelation exists between respondents and nonrespondents, spatial autocorrelation can be used for missing imputation. In this paper, we apply several nonresponse imputation methods including spatial imputation for the analysis of farm household economy data of the Gangwon-Do in 2002 as an example. We show that spatial imputation is more efficient than other methods through the numerical simulations.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.2
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• pp.67-78
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• 2019

Accurate electric load forecasting is very important in the efficient operation of the smart grid. Recently, due to the development of IT technology, many works for constructing accurate forecasting models have been developed based on big data processing using artificial intelligence techniques. These forecasting models usually utilize external factors such as temperature, humidity and historical electric load as independent variables. However, due to diverse internal and external factors, historical electrical load contains many missing data, which makes it very difficult to construct an accurate forecasting model. To solve this problem, in this paper, we propose a random forest-based missing data recovery scheme and construct an electric load forecasting model based on multilayer perceptron using the estimated values of missing data and external factors. We demonstrate the performance of our proposed scheme via various experiments.

• Journal of Korean society of Dental Hygiene
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• v.15 no.6
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• pp.947-961
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• 2015

Objectives: The purpose of the study was to identify the relationship between metabolic syndrome and oral diseases in the middle aged and elderly in Korea. Methods: The study subjects were 6,390 people over 40 years old from 2010 and 2012 Korea National Health and Nutrition Examination Survey. The survey questionnaire consisted of health, nutrition, and oral examination surveys. The independent variables included general characteristics, health behavior, oral health behavior, and metabolic syndrome. The dependent variables included dental caries experience and periodontal disease. The oral examination was carried out by the dentist based on World Health Organization standard. Results: The average prevalence rate of metabolic syndrome MS was 23.79%, including 54.84% of risk group and 21.37% of normal group. The missing teeth rate was 82.38%, DMFT rate was 90.28% and the periodontal disease rate was 33.15%. Those having abnormal fasting blood glucose had 1.17 fold(95% CI: 1.00~1.37) higher periodontal disease than the normal group. The abnormal HDL cholesterol group had 1.25 times higher odds ratio(95% CI: 1.07~1.46) and the obese group had 1.27 times higher odds ratio(95% CI: 1.07~1.51). The risk group had 1.20 times higher odds ration(95% CI: 1.00~1.44) and that of the metabolic syndrome group was 1.60 times higher(95% CI: 1.29~1.97) in periodontal disease. The high blood pressure group had 1.25 times of missing teeth prevalence rate(95% CI: 1.00~1.37). The metabolic syndrome group had 1.47 times of missing teeth prevalence rate(95% CI: 1.11~1.94). Conclusions: The middle aged and elderly people in Korea had higher rate of metabolic syndrome and oral disease. It is necessary to implement the preventive oral health examination for the control of metabolic syndrome and oral diseases prevalence.

• Journal of the korean academy of Pediatric Dentistry
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• v.21 no.2
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• pp.611-616
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• 1994

A major cause of missing permanent incisors is congenital abscence and extraction because of trauma and pathologic condition. The request for restoration of missing or spaced anterior teeth is common in dental practice. Problems, such as the tilting, drifting, and rotation of teeth adjacent to the space, complicate the restoration of apperance, and a normally simple restorative dental procedure may become difficult. There are two primary treatment alternatives to improving a dentition's irregular and spaced apperance-closing the space by orthodontic means or providing a prosthesis to disguise the space. The treatment choice depends on many variables, but, as a general rule, patients with a normal overbite, overjet, and buccal relationship are better treated by maintaining the sapce and providing a prosthesis, either fixed or removable. This case report presents two cases : Traumatic loss of maxillary right and left central incisors, Extraction of malformed mandibular right central inciosr. The loss of central incisor space was regained by the fixed-removable and fixed orthodontic appliance, and then Maryland bridge was cemented.

• Journal of Korean Society for Quality Management
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• v.34 no.2
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• pp.129-135
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• 2006

In the advance of computer technology, it is possible to keep all the related informations for monitoring equipments in control and huge amount of real time manufacturing data in a data base. Thus, the statistical analysis of large data sets with hundreds of thousands observations and hundred of independent variables whose some of values are missing at many observations is needed even though it is a formidable computational task. A tree structured approach to classification is capable of screening important independent variables and their interactions. In a Six Sigma project handling large amount of manufacturing data, one of the goals is to screen vital few variables among trivial many variables. In this paper we have reviewed and summarized CART, C4.5 and CHAID algorithms and proposed a simple method of screening vital few variables by selecting common variables screened by all the three algorithms. Also how to develop a logistics regression model on a large data set is discussed and illustrated through a large finance data set collected by a credit bureau for th purpose of predicting the bankruptcy of the company.

• Journal of Korean Society for Quality Management
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• v.24 no.1
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• pp.32-43
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• 1996

In this paper we develop nonparametric methods for regression analysis when the response variable is subject to censoring that arises naturally in quality engineering. This development is based on a general missing information principle that enables us to apply, via an iterative scheme, nonparametric regression techniques for complete data to iteratively reconstructed data from a given sample with censored observations. In particular, additive regression models are extended to right-censored data. This nonparametric regression method is applied to a simulated data set and the estimated smooth functions provide insights into the relationship between failure time and explanatory variables in the data.

• Publications of The Korean Astronomical Society
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• v.7 no.1
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• pp.155-166
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• 1992

We observed a total of 14 Mira variables as well as 4 late type variable stars for their SiO ${\nu}= 1$, J = 2 - 1 maser lines from April 1989 to November 1990 with the 13.7 m radio telescope at Daeduk Radio Astronomy Observatory. The maser intensity variations were the prime objective of the observations which well covered the periods of the variations. The origion of the variations were studied by comparing wi th those previousely measured in optical and infrared(IR) wavelengths and we confirmed that the intensity variations were in good correlation with those in V magnitude and IR intensity as previousely found in former investigators in general. However, for a few sources, we could find the missing maxima. The intensities themselves also were in good correlation with SiO ${\nu}\;=\;1$, J = 1 - 0 maser intensities observed in Yebes as expected. The good correlations indicate that the pumping source of the SiO maser is likely to be the IR emission in the masing regions and the "missing maxima" that are apparent in two particular sources are considered to relate wi th the strength of shocks arising from the eruptive mass-loss from central stars.