• Journal of the Korean Data and Information Science Society
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• 제25권1호
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• pp.211-218
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• 2014

Qu et. al. (2000) proposed the quadratic inference functions (QIF) method to marginal model analysis of longitudinal data to improve the generalized estimating equations (GEE). It yields a substantial improvement in efficiency for the estimators of regression parameters when the working correlation is misspecified. But for the longitudinal data with time-dependent covariates, when the implicit full covariates conditional mean (FCCM) assumption is violated, the QIF can not provide more consistent and efficient estimator than GEE (Cho and Dashnyam, 2013). Lai and Small (2007) divided time-dependent covariates into three types and proposed generalized method of moment (GMM) for longitudinal data with time-dependent covariates. They showed that their GMM type II and GMM moment selection methods can be more ecient than GEE with independence working correlation (GEE-ind) in the case of type II time-dependent covariates. We develop upgraded QIF method for type II time-dependent covariates. We show that this upgraded QIF method can provide substantial gains in efficiency over QIF and GEE-ind in the case of type II time-dependent covariates.

• ETRI Journal
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• 제44권4호
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• pp.624-640
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• 2022

Taxi-demand forecasting and hotspot prediction can be critical in reducing response times and designing a cost effective online taxi-booking model. Taxi demand in a region can be predicted by considering the past demand accumulated in that region over a span of time. However, other covariates-like neighborhood influence, sociodemographic parameters, and point-of-interest data-may also influence the spatiotemporal variation of demand. To study the effects of these covariates, in this paper, we propose three models that consider different covariates in order to select a set of independent variables. These models predict taxi demand in spatial units for a given temporal resolution using linear and ensemble regression. We eventually combine the characteristics (covariates) of each of these models to propose a robust forecasting framework which we call the combined covariates model (CCM). Experimental results show that the CCM performs better than the other models proposed in this paper.

• 응용통계연구
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• 제24권1호
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• pp.161-167
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• 2011

소수의 피험자로 이루어지는 생물학적 동등성 시험에서 제제의 특성으로 인해 측정된 생체이용률이 크게 영향을 받는 경우 교차설계법을 사용하더라도 적절한 통제가 어려워지는 경우가 많다. 이 때 적절한 공변량을 통해 이러한 영향력을 통제할 수 있다면 공변량을 고려한 교차설계법 모형이 고려될 필요가 있다. 본 연구논문에서는 공변량을 갖는 $2{\times}2$ 교차설계법을 고려하고 제제간의 생물학적 동등성 평가를 위한 통계적 추론을 제안한다. 제안된 방법은 실제 사례를 통해 논의한다.

• Journal of the Korean Data and Information Science Society
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• 제24권4호
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• pp.877-883
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• 2013

The quadratic inference functions (QIF) method proposed by Qu et al. (2000) and the generalized method of moments (GMM) for marginal regression analysis of longitudinal data with time-dependent covariates proposed by Lai and Small (2007) both are the methods based on generalized method of moment (GMM) introduced by Hansen (1982) and both use generalized estimating equations (GEE). Lai and Small (2007) divided time-dependent covariates into three types such as: Type I, Type II and Type III. In this paper, we compared these methods in the case of Type II and Type III in which full covariates conditional mean assumption (FCCM) is violated and interested in whether they can improve the results of GEE with independence working correlation. We show that in the marginal regression model with Type II time-dependent covariates, GMM Type II of Lai and Small (2007) provides more ecient result than QIF and for the Type III time-dependent covariates, QIF with independence working correlation and GMM Type III methods provide the same results. Our simulation study showed the same results.

• Journal of Preventive Medicine and Public Health
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• 제43권2호
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• pp.159-165
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• 2010

Objectives: We evaluated the reliability of the possible covariates of the baseline survey data collected for the Epidemiological Investigation on Cancer Risk Among Residents Who Reside Near the Nuclear Power Plants in Korea. Methods: Follow-up surveys were conducted for 477 participants of the cohort at less than 1 year after the initial survey. The mean interval between the initial and follow-up surveys was 282.5 days. Possible covariates were identified by analyzing the correlations with the exposure variable and associations with the outcome variables for all the variables. Logistic regression analysis with stepwise selection was further conducted among the possible covariates to select variables that have covariance with other variables. We considered that these variables can be representing other variables. Seven variables for the males and 3 variables for the females, which had covariance with other possible covariates, were selected as representative variables. The Kappa index of each variable was calculated. Results: For the males, the Kappa indexes were as follow; family history of cancer was 0.64, family history of liver diseases in parents and siblings was 0.56, family history of hypertension in parents and siblings was 0.51, family history of liver diseases was 0.50, family history of hypertension was 0.44, a history of chronic liver diseases was 0.53 and history of pulmonary tuberculosis was 0.36. For females, the Kappa indexes were as follow; family history of cancer was 0.58, family history of hypertension in parents and siblings was 0.56 and family history of hypertension was 0.47. Conclusions: Most of the possible covariates showed good to moderate agreement.

• Communications for Statistical Applications and Methods
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• 제30권1호
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• pp.95-107
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• 2023

Purpose: The statistical analysis of point processes on linear networks is a recent area of research that studies processes of events happening randomly in space (or space-time) but with locations limited to reside on a linear network. For example, traffic accidents happen at random places that are limited to lying on a network of streets. This paper applies techniques developed for point processes on linear networks and the tools available in the R-package spatstat to estimate the intensity of traffic accidents in Leon County, Florida. Methods: The intensity of accidents on the linear network of streets is estimated using log-linear Poisson models which incorporate cubic basis spline (B-spline) terms which are functions of the x and y coordinates. The splines used equally-spaced knots. Ten different models are fit to the data using a variety of covariates. The models are compared with each other using an analysis of deviance for nested models. Results: We found all covariates contributed significantly to the model. AIC and BIC were used to select 9 as the number of knots. Additionally, covariates have different effects such as increasing the speed limit would decrease traffic accident intensity by 0.9794 but increasing the number of lanes would result in an increase in the intensity of traffic accidents by 1.086. Conclusion: Our analysis shows that if other conditions are held fixed, the number of accidents actually decreases on roads with higher speed limits. The software we currently use allows our models to contain only spatial covariates and does not permit the use of temporal or space-time covariates. We would like to extend our models to include such covariates which would allow us to include weather conditions or the presence of special events (football games or concerts) as covariates.

• Asian-Australasian Journal of Animal Sciences
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• 제18권6호
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• pp.772-779
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• 2005

The objectives of this study were to examine problems with using F$_1$ data by simulation, association of marker loci and QTL from crosses of inbred parental lines and to enumerate the preliminary characterization of genetic superiority within inbred parental lines. In this study, the association between markers for QTL used as covariates and estimates of variance components due to effects of lines was investigated through computer simulation. The effects of size of population to develop inbred lines and initial frequencies and magnitudes of effects of QTL were also considered. Results show that estimates of variance components due to line effects are influenced by including marker information as covariates in the model for analysis. Estimates of line variance were increased by adding marker information into the analysis, because negative covariances between effects associated with the markers and the remaining effects associated with other loci existed. However, the fit of the model as indicated by the log likelihood improved by adding more markers as covariates into the analysis. Marker assisted selection will be beneficial when markers explain unexplained genetic difference during selection procedure. Markers can be used to identify QTLs affecting traits, and to select for favorable QTL alleles. To efficiently use genetic markers, location of markers at the genome must be identified. The estimates of variance due to effects of with and without marker information used as covariates in the analysis were investigated. The estimates of line variances were always increased when markers were included as covariates for the model because a negative covariance were existed.

• Communications for Statistical Applications and Methods
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• 제21권4호
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• pp.349-361
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• 2014

This paper compares of lasso type estimators in various high-dimensional data situations with sparse parameters. Lasso, adaptive lasso, fused lasso and elastic net as lasso type estimators and ridge estimator are compared via simulation in linear models with correlated and uncorrelated covariates and binary regression models with correlated covariates and discrete covariates. Each method is shown to have advantages with different penalty conditions according to sparsity patterns of regression parameters. We applied the lasso type methods to Arabidopsis microarray gene expression data to find the strongly significant genes to distinguish two groups.

• Journal of the Korean Statistical Society
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• 제27권3호
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• pp.279-288
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• 1998

We propose a multiprocess dynamic Poisson model for the analysis of Poisson process with the covariates. The algorithm for the recursive estimation of the parameter vector modeling time-varying effects of covariates is suggested. Also the algorithm for forecasting of numbers of events at the next time point based on the information gathered until the current time is suggested.

• Journal of the Korean Statistical Society
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• 제34권4호
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• pp.345-366
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• 2005

This paper considers the cointegrating vector estimator in the error correction model with stationary covariates, which combines the stationary vector autoregressive model and the nonstationary error correction model. The cointegrating vector estimator is shown to follow the locally asymptotically mixed normal distribution. The variance of the estimator depends on the co­variate effect of stationary regressors, and the asymptotic efficiency improves as the magnitude of the covariate effect increases. An economic application of the money demand equation is provided.