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197 results on '"Qin, Yongsong"'

1. Hyper-Class Representation of Data

Author

Zhang, Shichao, Li, Jiaye, Zhang, Wenzhen, and Qin, Yongsong

Subjects
Computer Science - Information Retrieval
Abstract

Data representation is usually a natural form with their attribute values. On this basis, data processing is an attribute-centered calculation. However, there are three limitations in the attribute-centered calculation, saying, inflexible calculation, preference computation, and unsatisfactory output. To attempt the issues, a new data representation, named as hyper-classes representation, is proposed for improving recommendation. First, the cross entropy, KL divergence and JS divergence of features in data are defined. And then, the hyper-classes in data can be discovered with these three parameters. Finally, a kind of recommendation algorithm is used to evaluate the proposed hyper-class representation of data, and shows that the hyper-class representation is able to provide truly useful reference information for recommendation systems and makes recommendations much better than existing algorithms, i.e., this approach is efficient and promising.

Published
2022
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4. Empirical likelihood for linear models with spatial errors

Author

Qin, Yongsong

Subjects
Statistics - Methodology
Abstract

For linear models with spatial errors, the empirical likelihood ratio statistics are constructed for the parameters of the models. It is shown that the limiting distributions of the empirical likelihood ratio statistics are chi-squared distributions, which are used to construct confidence regions for the parameters of the models.

Published
2018

5. Adjusted empirical likelihood for probability density functions under strong mixing samples.

Author

Tang, Jie and Qin, Yongsong

Subjects
*PROBABILITY density function, *CONFIDENCE intervals, *SAMPLE size (Statistics)
Abstract

To obtain a profile empirical likelihood ratio statistic is essentially to address a constrained maximization problem. However, when sample size is small, the optimal function may have no numerical solution, as the convex hull of the sample points may not contain the zero vector 0 as its interior point. The adjusted empirical likelihood (AEL) method introduced by Chen, Variyath, and Abraham (2008) is very useful to solve this problem. The innovation of this article is that we extend the AEL method to probability density functions (p.d.f.) under dependent samples, as there are few literatures using this method under dependent samples. It is shown that the AEL ratio statistic for a p.d.f. is asymptotically χ 2 -type distributed under a mixing sample, which is used to obtain an AEL-based confidence interval for the p.d.f. Our simulations show that the AEL is faster to compute than unadjusted empirical likelihood and the coverage probability based on the AEL method is superior to the other two usual methods, namely the unadjusted empirical likelihood and the normal approximation methods, particularly under small sample sizes. [ABSTRACT FROM AUTHOR]

Published
2024
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11. Empirical Likelihood for Mixed Regressive, Spatial Autoregressive Model Based on GMM

Author

Qin, Yongsong and Lei, Qingzhu

Abstract

For the parameters in a mixed regressive, spatial autoregressive (MRSAR) model, it is shown, in this article, that for every generalized method of moments (GMM) estimator, there is an empirical likelihood (EL) estimator which has the same asymptotic variance as the GMM estimator. Specifically, we show that there exists an EL estimator which is asymptotically efficient as the best GMM estimator and the EL confidence region for the parameters in the MRSAR model is constructed without estimating the asymptotic variance.

Published
2024
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12. Empirical Likelihood for Spatial Autoregressive Models with Spatial Autoregressive Disturbances

Author

Qin, Yongsong

Abstract

The empirical likelihood ratio statistics are constructed for the parameters in spatial autoregressive models with spatial autoregressive disturbances. It is shown that the limiting distributions of the empirical likelihood ratio statistics are chi-squared distributions, which are used to construct confidence regions for the parameters in the models.

Published
2024
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13. Empirical likelihood for spatial dynamic panel data models with spatial lags and spatial errors.

Author

Rong, Jianrong, Liu, Yan, and Qin, Yongsong

Subjects
PANEL analysis, CONFIDENCE regions (Mathematics), DATA modeling, CHI-square distribution, GENERALIZED method of moments
Abstract

We study spatial dynamic panel data models with both spatial lags and spatial errors. The empirical likelihood (EL) ratio statistics are constructed for the parameters of the models. It is shown that the limiting distributions of the EL ratio statistics are chi-square distributions, which are used to construct confidence regions for the parameters of the models. A simulation study is conducted to compare the performances of the EL based and the normal approximation (NA) based confidence regions. Simulation results show that the EL can be computationally easier than the NA method to implement in practice. [ABSTRACT FROM AUTHOR]

Published
2023
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17. Missing Value Imputation Based on Data Clustering

Author

Zhang, Shichao, Zhang, Jilian, Zhu, Xiaofeng, Qin, Yongsong, Zhang, Chengqi, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Gavrilova, Marina L., editor, and Tan, C. J. Kenneth, editor

Published
2008
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19. Difference Detection Between Two Contrast Sets

Author

Huang, Hui-jing, Qin, Yongsong, Zhu, Xiaofeng, Zhang, Jilian, Zhang, Shichao, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Tjoa, A Min, editor, and Trujillo, Juan, editor

Published
2006
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20. Optimized Parameters for Missing Data Imputation

Author

Zhang, Shichao, Qin, Yongsong, Zhu, Xiaofeng, Zhang, Jilian, Zhang, Chengqi, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Yang, Qiang, editor, and Webb, Geoff, editor

Published
2006
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27. Joint Modelling of Longitudinal-Time-To-Event with Categorical Variable Indicators of Latent Classes: Application to Tuberculosis Data

Author

Adeboye Azeez, Ndege James, Qin Yongsong, and Mutambayi Ruffin

Subjects
Statistics and Probability, Proportional hazards model, General Mathematics, Repeated measures design, Markov chain Monte Carlo, Bivariate analysis, Latent class model, symbols.namesake, Covariate, Statistics, symbols, Categorical variable, Mathematics, Event (probability theory)
Abstract

In many clinical and reliability research reports, the outcomes of basic interest is the time to a particular event happens in order to indicate the person’s “true” state of health or survival status. Different models have been used to analyze such data separately, but may be unsuitable if the longitudinal and health status measures are correlated. In this study, mixed effect and Cox model of latent class are jointly modelled for the correlation between the covariates, observed and unobserved health status variable with binary latent class indicators. A Bayesian approach for Maximum likelihood estimates is implemented using Markov Chain Monte Carlo (MCMC) techniques. The repeated and survival measures are independently assumed to be a Gaussian process for latent bivariate. The joint model is applied to TB cohort study for the HIV comorbidity effect on event time for Tuberculosis patients. R package is used for curvilinear repeated measures of latent class model and joint latent class models for both repeated measures and survival time event.

Published
2020

47. Disease mapping of tuberculosis prevalence in Eastern Cape Province, South Africa

Author

Davies Obaromi, James Ndege, and Qin Yongsong

Subjects
medicine.medical_specialty, Tuberculosis, Public health, Public Health, Environmental and Occupational Health, Context (language use), Random effects model, medicine.disease, symbols.namesake, Geography, Environmental health, Relative risk, Cape, Epidemiology, symbols, medicine, Poisson regression
Abstract

This is an applied study to investigate the association of selected socio-economic and demographic factors with the relative risk of tuberculosis (TB) prevalence in the Eastern Cape Province of South Africa and to produce disease maps for the spatial outlines of the disease in the province. This is an ecological spatial study of TB prevalence in the Eastern Cape, a province in South Africa, during the year 2014. Three socio-economic indicators and three demographic factors, all calculated per sub-district, were used to assess their relationship with tuberculosis prevalence, using a Poisson regression model. From the analysis, the best model included all the selected covariates of the proximal model with the spatial random effects. The improvement in the goodness-of-fit statistic when the spatial structure was included confirms the spatial pattern of population density and average household size. The idea of assessing both the impact of covariates at the ecological level and spatial outlines in the same context should be encouraged in epidemiology to help with creating epidemiological surveillance systems (ESS) on a provincial basis for planning interventions and improvement of control programme efficiency.

Published
2018

48. Empirical likelihood for panel data models with spatial errors.

Author

Li, Yinghua, Li, Yuan, and Qin, Yongsong

Subjects
CONFIDENCE regions (Mathematics), CHI-square distribution, CONFIDENCE intervals, DATA modeling, LIKELIHOOD ratio tests, PANEL analysis
Abstract

For panel data models with spatial errors, the empirical likelihood ratio statistics are constructed for the parameters of the models. It is shown that the limiting distributions of the empirical likelihood ratio statistics are chi-squared distributions, which are used to construct confidence regions for the parameters of the models. A simulation study is conducted to show the performance of the proposed method. [ABSTRACT FROM AUTHOR]

Published
2022
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