• Journal of applied mathematics & informatics
• /
• 제9권2호
• /
• pp.825-835
• /
• 2002

In this paper, we extend the conditional unrelated question model which was suggested by Lee and Hong(2000) to two-sample case when there is no information about the true proportion of the unrelated character Y. Conditions are obtained under which the proposed model is more efficient than Carr et al.\`s conditional modal and Greenberg et al.'s two-sample unrelated question model.

• Communications for Statistical Applications and Methods
• /
• 제12권2호
• /
• pp.359-364
• /
• 2005

This paper considers a specification test of conditional Poisson regression model for time series count data. Although conditional models for count data have received attention and proposed in several ways, few studies focused on checking its adequacy. Motivated by the test of martingale difference assumption, a specification test via Ljung-Box statistic is proposed in the conditional model of the time series count data. In order to illustrate the performance of Ljung- Box test, simulation results will be provided.

• Communications for Statistical Applications and Methods
• /
• 제7권3호
• /
• pp.721-729
• /
• 2000

In this paper, we propose a new conditional randomized response model that has improved the Carr et al.'s model in view of he variance and the protection of privacy of respondents. We show that he suggested model is more effective and protective than the Loynes' model and Carr et al.' model.

• Journal of applied mathematics & informatics
• /
• 제8권1호
• /
• pp.253-260
• /
• 2001

In this paper we suggest a conditional unrelated question randomized response model by using the Carr et. al.’s model(1982) and Greenberg et. al.’s model(1969). Our model can obtain more comprehensive information about the sensitive character A. We suggest the conditions that make our model efficient compared with models of Greenberg et. al. and Carr et al..

• 한국연소학회:학술대회논문집
• /
• 한국연소학회 2000년도 제21회 KOSCO SYMPOSIUM 논문집
• /
• pp.24-32
• /
• 2000

A brief introduction is given on the conditional moment closure model for turbulent nonpremixed combustion. It is based on the transport equations derived through a rigorous mathematical procedure for the conditionally averaged quantities and appropriate modeling forms for conditional scalar dissipation rate, conditional mean velocity and reaction rate. Examples are given for prediction of NO and OH in bluffbody flames, soot distribution in jet flames and autoignition of a methane/ethane jet to predict the ignition delay with respect to initial temperature, pressure and fuel composition. Conditional averaging may also be a powerful modeling concept in other approaches involved in turbulent combustion problems in various different regimes.

• 한국연소학회지
• /
• 제5권2호
• /
• pp.9-17
• /
• 2000

A brief introduction is given on the conditional moment closure model for turbulent nonpremixed combustion. It is based on the transport equations derived through a rigorous mathematical procedure for the conditionally averaged quantities and appropriate modeling forms for conditional scalar dissipation rate, conditional mean velocity and reaction rate. Examples are given for prediction of NO and OR in bluffbody flames, soot distribution in jet flames and autoignition of a methane/ethane jet to predict the ignition delay with respect to initial temperature, pressure and fuel composition. Conditional averaging may also be a powerful modeling concept in other approaches involved in turbulent combustion problems in various different regimes.

• Communications for Statistical Applications and Methods
• /
• 제8권3호
• /
• pp.753-765
• /
• 2001

We suggest a quantitative conditional unrelated question model that can be used in obtaining more sensitive information. For whom say "yes" about the less 7han sensitive question .B we ask only about the more sensitive variable X. We extend our model to two sample case when there is no information about the true mean of the unrelated variable Y. Finally we compare the efficiency of our model with that of Greenberg et al.′s.

• Journal of the Korean Data and Information Science Society
• /
• 제21권5호
• /
• pp.831-839
• /
• 2010

최소제곱 서포트벡터기계는 비선형회귀분석과 분류에 널리 쓰이는 커널기법이다. 본 논문에서는 금융시계열자료의 평균 및 변동성을 추정하기 위하여 평균의 추정 방법으로는 가중최소제곱 서포트벡터기계, 변동성의 추정 방법으로는 최소제곱 서포트벡터기계를 사용하는 비선형 평균 일반화 이분산 자기회귀모형을 제안한다. 제안된 모형은 선형 일반화 이분산 자기회귀모형 및 선형 평균 일반화 이분산 자기회귀모형보다 더 나은 추정 능력을 가진다는 것을 실제자료의 추정을 통하여 보였다.

• Food Science and Biotechnology
• /
• 제17권5호
• /
• pp.1052-1059
• /
• 2008

Sequential Sensitivity Analysis (SSA) and conditional stimulus model have been developed to describe sequence effects in difference tests and proposed to generate prediction of differences in sensitivity between various test protocols and to assist the appropriate selection of difference test. Yet, such models did not furnish a complete explanation of the relative sensitivity in 4 different versions of 3-alternative forced choice (AFC) tests where various interstimulus rinses were introduced. In the present study, the vector of the contrasts between various conditional stimuli were measured using same-different and 2-AFC and a new 16-distribution conditional stimulus model was developed by refining Lee and O'Mahony's contrast model. This new model gave superior predictions than previous models.

• Journal of the Korean Data and Information Science Society
• /
• 제27권5호
• /
• pp.1225-1239
• /
• 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.