• Journal of Nutrition and Health
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• v.18 no.1
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• pp.46-54
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• 1985

Three types of infant food were experimentally prepared based on the average caloric requirement for, Koreans : Formula A, with 1/3 RDA for 4-6 month, formula B, with 1/2 RDA for 7-9 month, and formula C, with 2/3 RDA for 10-12 month old infants. Into each formula was added approximately 50% of rice. Analysis of the nutritive values on these formula showed no deviance Com the expected values in case of general nutrients while the percent of essential amino acids in protein was muck higher except methionine. Since iron content was found to be below the half of except values, a sufficient amount of iron as required in RDA should be additionally supplied, for example, in iron drops. The infant preference test on each formula showed much favorable acceptability : 50% of them responded as moderate, 33.3% as favorable, 12.5% as disliking, and 4.2% as disgusting. The test also showed no noticeable change in both feces and appetite.

• Journal of Fisheries and Marine Sciences Education
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• v.25 no.2
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• pp.321-340
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• 2013

The purpose of this study was to investigate elementary school teachers'concern and implementation of differentiated instruction of mathematics. To achieve the purpose, this study applied measurements of CBAM, including Stages of Concern Questionnaire and Innovation Configurations Checklist, to 133 elementary school teachers. The results indicated that most teachers were in awareness stage, which meant they had little concern on differentiated mathematics instruction. As well as, analysis on innovation configurations revealed that b and c variation, each referred to fidelity to and deviance from national curriculum standard relatively, were dominant in their instruction. Based on the results, the study suggested implications on future policies and teacher training for differentiated mathematics instruction.

• The Korean Journal of Applied Statistics
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• v.6 no.1
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• pp.41-52
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• 1993

In this paper we study numerical behavior of the adjustments for the variances of the pearson and deviance type dispersion statistics in two overdispersed mixture models; negative binomial and beta-binomial distribution. They are important families of an extended quasi-likelihood model which is very useful for the joint modelling of mean and dispersion. Comparisons are done for two types of dispersion statistics for various mean and dispersion parameters by simulation studies.

• Communications for Statistical Applications and Methods
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• v.16 no.4
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• pp.697-705
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• 2009

The Pearson chi-squared statistic or the deviance statistic is widely used in assessing the goodness-of-fit of the generalized linear models. But these statistics are not proper in the situation of continuous explanatory variables which results in the sparseness of cell frequencies. We propose a goodness-of-fit test statistic for the cumulative logit models with ordinal responses. We consider the grouping of a dataset based on the ordinal scores obtained by fitting the assumed model. We propose the Pearson chi-squared type test statistic, which is obtained from the cross-classified table formed by the subgroups of ordinal scores and the response categories. Because the limiting distribution of the chi-squared type statistic is intractable we suggest the parametric bootstrap testing procedure to approximate the distribution of the proposed test statistic.

• Management Science and Financial Engineering
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• v.12 no.2
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• pp.19-35
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• 2006

We use the hierarchical Bayesian approach to describe the transition probabilities of a binary nonhomogeneous Markov chain. The Markov chain is used for describing the transition behavior of emotionally disturbed children in a treatment program. The effects of covariates on transition probabilities are assessed using a logit link function. To describe the time evolution of transition probabilities, we consider two modeling strategies. The first strategy is based on the concept of exchangeabiligy, whereas the second one is based on a first order Markov property. The deviance information criterion (DIC) measure is used to compare models with two different time dependent structures. The inferences are made using the Markov chain Monte Carlo technique. The developed methodology is applied to some real data.

• The Korean Journal of Applied Statistics
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• v.16 no.2
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• pp.365-376
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• 2003

In this paper we reviewed a variety of non-Gaussian time series models, and studied the model selection criteria such as AIC and BIC to select proper models. We also considered the likelihood ratio test and applied it to analysis of Polio data set.

• Communications for Statistical Applications and Methods
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• v.24 no.6
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• pp.583-604
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• 2017

The most popular regression model for the analysis of time-to-event data is the Cox proportional hazards model. While the model specifies a parametric relationship between the hazard function and the predictor variables, there is no specification regarding the form of the baseline hazard function. A critical assumption of the Cox model, however, is the proportional hazards assumption: when the predictor variables do not vary over time, the hazard ratio comparing any two observations is constant with respect to time. Therefore, to perform credible estimation and inference, one must first assess whether the proportional hazards assumption is reasonable. As with other regression techniques, it is also essential to examine whether appropriate functional forms of the predictor variables have been used, and whether there are any outlying or influential observations. This article reviews diagnostic methods for assessing goodness-of-fit for the Cox proportional hazards model. We illustrate these methods with a case-study using available R functions, and provide complete R code for a simulated example as a supplement.

• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1225-1239
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• 2016

We investigate pediatric tumor incidence data collected by the Florida Association for Pediatric Tumor program using various models commonly used in disease mapping analysis. Particularly, we consider Poisson normal models with various conditional autoregressive structure for spatial dependence, a zero-in ated component to capture excess zero counts and a spatio-temporal model to capture spatial and temporal dependence, together. We found that intrinsic conditional autoregressive model provides the smallest Deviance Information Criterion (DIC) among the models when only spatial dependence is considered. On the other hand, adding an autoregressive structure over time decreases DIC over the model without time dependence component. We adopt weighted ranks squared error loss to identify high risk regions which provides similar results with other researchers who have worked on the same data set (e.g. Zhang et al., 2014; Wang and Rodriguez, 2014). Our results, thus, provide additional statistical support on those identied high risk regions discovered by the other researchers.

• Korean Journal of Ichthyology
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• v.20 no.2
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• pp.105-111
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• 2008

A pelagic tuna longline research cruise in the eastern and central Pacific Ocean from September to October of 2006 was conducted to compare catch rates with the use of different hook type and bait combinations. Traditional tuna hooks (J 4) and three circle hook types (C15, C16, C18), along with five bait types (chub mackerel (CM), jack mackerel (JM), milkfish (MF), sardine (SD), and squid (SQ)) and hook number as a proxy for hook depth were evaluated for their effect on bigeye tuna catch rates (fish per 1,000 hooks) using Generalized Linear Models (GLMs). Results from 28 sets indicated significant differences in bigeye catch rates between individual longline sets and hook number. The GLM explained 33% of the deviance in bigeye catch rates with these two factors. An alternative model formulation included bait type which had a small effect (explaining 2.7% of the deviance) on catch rates. Hook type had a negligible and non-significant effect in the GLMs. These results indicate that all of the hooks and baits tested are equally effective at catching bigeye tuna and that hook number (depth) was the paramount operational factor in explaining bigeye tuna catch rates.

• The Korean Journal of Applied Statistics
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• v.33 no.1
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• pp.47-59
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• 2020

In this paper, we present the longitudinal Markov binary regression model with t-link function when its transition order is known or unknown. It is assumed that logit or probit models are considered in binary regression models. Here, t-link function can be used for more flexibility instead of the probit model since the t distribution approaches to normal distribution as the degree of freedom goes to infinity. A Markov regression model is considered because of the longitudinal data of each individual data set. We propose Bayesian method to determine the transition order of Markov regression model. In particular, we use the deviance information criterion (DIC) (Spiegelhalter et al., 2002) of possible models in order to determine the transition order of the Markov binary regression model if the transition order is known; however, we compute and compare their posterior probabilities if unknown. In order to overcome the complicated Bayesian computation, our proposed model is reconstructed by the ideas of Albert and Chib (1993), Kuo and Mallick (1998), and Erkanli et al. (2001). Our proposed method is applied to the simulated data and real data examined by Sommer et al. (1984). Markov chain Monte Carlo methods to determine the optimal model are used assuming that the transition order of the Markov regression model are known or unknown. Gelman and Rubin's method (1992) is also employed to check the convergence of the Metropolis Hastings algorithm.