• ETRI Journal
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• v.44 no.4
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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.

• Communications for Statistical Applications and Methods
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• v.15 no.5
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• pp.793-798
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• 2008

Extreme value inference deals with fitting the generalized extreme value distribution model and the generalized Pareto distribution model, which are recently combined to give a single model, namely a two-dimensional non-homogeneous Poisson exceedance point process model. In this paper, we extend the two-dimensional non-homogeneous Poisson process model to include non-stationary effect or dependence on covariates and then derive the likelihood for the extended model.

• ETRI Journal
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• v.43 no.6
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• pp.1058-1080
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• 2021

Crime is not a completely random event but rather shows a pattern in space and time. Capturing the dynamic nature of crime patterns is a challenging task. Crime prediction models that rely only on neighborhood influence and demographic features might not be able to capture the dynamics of crime patterns, as demographic data collection does not occur frequently and is static. This work proposes a novel approach for crime count and hotspot prediction to capture the dynamic nature of crime patterns using taxi data along with historical crime and demographic data. The proposed approach predicts crime events in spatial units and classifies each of them into a hotspot category based on the number of crime events. Four models are proposed, which consider different covariates to select a set of independent variables. The experimental results show that the proposed combined subset model (CSM), in which static and dynamic aspects of crime are combined by employing the taxi dataset, is more accurate than the other models presented in this study.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.14
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• pp.5889-5897
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• 2015

Background: The performance of combined testing visual inspection with acetic acid (VIA) and cervical cytology tests might differ from one setting to another. The average estimate of the testing accuracy across studies is informative, but no meta-analysis has been carried out to assess this combined method. Objective: The objective of this study was to estimate the average sensitivity and specificity of the combined VIA and cervical cytology tests for the detection of cervical precancerous lesions. Materials and Methods: We conducted a systematic review and a meta-analysis, according to the Cochrane Handbook for Systematic Review of Diagnostic Test Accuracy. We considered two cases. In the either-positive result case, a positive result implies positivity in at least one of the tests. A negative result implies negativity in both tests. In the both-positive case, a positive result implies having both tests positive. Eligible studies were identified using Pubmed, Embase, Website of Science, CINHAL and COCRANE databases. True positive, false positive, false negative and true negative values were extracted. Estimates of sensitivity and specificity, positive and negative likelihood (LR) and diagnostic odds ratios (DOR) were pooled using a hierarchical random effect model. Hierarchical summary receiver operating characteristics (HSROC) were generated and heterogeneity was verified through covariates potentially influencing the diagnostic odds ratio. Findings: Nine studies fulfilled inclusion criteria and were included in the analysis. Pooled estimates of the sensitivities of the combined tests in either-positive and both-positive cases were 0.87 (95% CI: 0.83-0.90) and 0.38 (95% CI: 0.29-0.48), respectively. Corresponding specificities were 0.79 (95% CI: 0.63-0.89) and 0.98 (95% CI: 0.96-0.99) respectively. The DORs of the combined tests in either-positive or both-positive result cases were 27.7 (95% CI: 12.5-61.5) and 52 (95% CI: 22.1-122.2), respectively. When including only articles without partial verification bias and also a high-grade cervical intraepithelial neoplasia as a threshold of the disease, DOR of combined test in both-positive result cases remained the highest. However, DORs decreased to 12.1 (95% CI: 6.05-24.1) and 13.8 (95% CI: 7.92-23.9) in studies without partial verification bias for the combined tests in the either-positive and both-positive result cases, respectively. The screener, the place of study and the size of the population significantly influenced the DOR of combined tests in the both-positive result case in restriction analyses that considered only articles with CIN2+ as disease threshold. Conclusions: The combined test in the either-positive result case has a high sensitivity, but a low specificity. These results suggest that the combined test should be considered in developing countries as a primary screening test if facilities exist to confirm, through colposcopy and biopsy, a positive result.

• Research in Community and Public Health Nursing
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• v.29 no.3
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• pp.349-358
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• 2018

Purpose: This study was conducted to investigate the combined effects of abdominal obesity and alcohol drinking on the risk of hypertension in Korean adults (aged ${\geq}30yrs$). Methods: Data of 13,885 subjects from the sixth Korea National Health and Nutrition Examination Survey were analyzed. The multiple logistic regression tests were used for the analysis, including potential covariates of the model. Results: Frequency of drinking, typical drinking quantity, and frequency of binge drinking had a positive relation to hypertension. The odds ratio of hypertension for risk drinkers with abdominal obesity was 4.81 compared to non-risk drinkers with normal waist circumstance, whereas the odds ratios of hypertension for risk drinkers with normal waist circumstance and non-risk drinkers with abdominal obesity were 1.58 and 2.37 respectively. Conclusion: Both abdominal obesity and alcohol drinking patterns were strong risk factors of hypertension in the Korean adults. Risk drinkers with abdominal obesity showed a marked high risk in hypertension compared to those with a single condition alone.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.14-14
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• 2017

The discipline of plant breeding is experiencing a renaissance impacting crop improvement as a result of new technologies, however fundamental questions remain for predicting the phenotype and how the environment and genetics shape it. Inexpensive DNA sequencing, genotyping, new statistical methods, high throughput phenotyping and gene-editing are revolutionizing breeding methods and strategies for improving both quantitative and qualitative traits. Genomic selection (GS) models use genome-wide markers to predict performance for both phenotyped and non-phenotyped individuals. Aerial and ground imaging systems generate data on correlated traits such as canopy temperature and normalized difference vegetative index that can be combined with genotypes in multivariate models to further increase prediction accuracy and reduce the cost of advanced trials with limited replication in time and space. Design of a GS training population is crucial to the accuracy of prediction models and can be affected by many factors including population structure and composition. Prediction models can incorporate performance over multiple environments and assess GxE effects to identify a highly predictive subset of environments. We have developed a methodology for analyzing unbalanced datasets using genome-wide marker effects to group environments and identify outlier environments. Environmental covariates can be identified using a crop model and used in a GS model to predict GxE in unobserved environments and to predict performance in climate change scenarios. These new tools and knowledge challenge the plant breeder to ask the right questions and choose the tools that are appropriate for their crop and target traits. Contemporary plant breeding requires teams of people with expertise in genetics, phenotyping and statistics to improve efficiency and increase prediction accuracy in terms of genotypes, experimental design and environment sampling.

• Asian-Australasian Journal of Animal Sciences
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• v.31 no.10
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• pp.1542-1549
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• 2018

Objective: Because lactation periods in dairy cows lengthen with increasing total milk production, it is important to predict individual productivities after 305 days in milk (DIM) to determine the optimal lactation period. We therefore examined whether the random regression (RR) coefficient from 306 to 450 DIM (M2) can be predicted from those during the first 305 DIM (M1) by using a RR model. Methods: We analyzed test-day milk records from 85,690 Holstein cows in their first lactations and 131,727 cows in their later (second to fifth) lactations. Data in M1 and M2 were analyzed separately by using different single-trait RR animal models. We then performed a multiple regression analysis of the RR coefficients of M2 on those of M1 during the first and later lactations. Results: The first-order Legendre polynomials were practical covariates of RR for the milk yields of M2. All RR coefficients for the additive genetic (AG) effect and the intercept for the permanent environmental (PE) effect of M2 had moderate to strong correlations with the intercept for the AG effect of M1. The coefficients of determination for multiple regression of the combined intercepts for the AG and PE effects of M2 on the coefficients for the AG effect of M1 were moderate to high. The daily milk yields of M2 predicted by using the RR coefficients for the AG effect of M1 were highly correlated with those obtained by using the coefficients of M2. Conclusion: Milk production after 305 DIM can be predicted by using the RR coefficient estimates of the AG effect during the first 305 DIM.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.14
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• pp.5721-5727
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• 2014

Objectives: To evaluate the performance of clustering methods used in the prognostic assessment of categorical clinical data for hepatocellular carcinoma (HCC) patients in China, and establish a predictable prognostic nomogram for clinical decisions. Materials and Methods: A total of 332 newly diagnosed HCC patients treated with hepatic resection during 2006-2009 were enrolled. Patients were regularly followed up at outpatient clinics. Clustering methods including the Average linkage, k-modes, fuzzy k-modes, PAM, CLARA, protocluster, and ROCK were compared by Monte Carlo simulation, and the optimal method was applied to investigate the clustering pattern of the indices including platelet count, platelet/lymphocyte ratio (PLR) and serum aspartate aminotransferase activity/platelet count ratio index (APRI). Then the clustering variable, age group, tumor size, number of tumor and vascular invasion were studied in a multivariable Cox regression model. A prognostic nomogram was constructed for clinical decisions. Results: The ROCK was best in both the overlapping and non-overlapping cases performed to assess the prognostic value of platelet-based indices. Patients with categorical platelet-based indices significantly split across two clusters, and those with high values, had a high risk of HCC recurrence (hazard ratio [HR] 1.42, 95% CI 1.09-1.86; p<0.01). Tumor size, number of tumor and blood vessel invasion were also associated with high risk of HCC recurrence (all p< 0.01). The nomogram well predicted HCC patient survival at 3 and 5 years. Conclusions: A cluster of platelet-based indices combined with other clinical covariates could be used for prognosis evaluation in HCC.