This paper is concerned with the estimation of change-point in a binary response model with the assumption that the conditional median of the error term, given the explanatory variable, is zero. We construct an estima...This paper is concerned with the estimation of change-point in a binary response model with the assumption that the conditional median of the error term, given the explanatory variable, is zero. We construct an estimation of change-point based on the maximum score function and give its exponential convergence rate under some mild conditions.展开更多
This paper studies the large sample properties of the change point estimates in binary response models. The estimate is obtained by maximizing the smoothed score function when the median of the latent error variable i...This paper studies the large sample properties of the change point estimates in binary response models. The estimate is obtained by maximizing the smoothed score function when the median of the latent error variable is assumed to be zero. An exponential convergence rate of the change point estimate is also established.展开更多
For news video images, caption recognizing is a useful and important step for content understanding. Caption locating is usually the first step of caption recognizing and this paper proposes a simple but effective cap...For news video images, caption recognizing is a useful and important step for content understanding. Caption locating is usually the first step of caption recognizing and this paper proposes a simple but effective caption locating algorithm called maximum feature score region （MFSR） based method, which mainly consists of two stages： In the first stage, up/down boundaries are attained by turning to edge map projection. Then, maximum feature score region is defined and left/right boundaries are achieved by utilizing MFSR. Experiments show that the proposed MFSR based method has superior and robust performance on news video images of different types.展开更多
Background: Bivariate count data are commonly encountered in medicine, biology, engineering, epidemiology and many other applications. The Poisson distribution has been the model of choice to analyze such data. In mos...Background: Bivariate count data are commonly encountered in medicine, biology, engineering, epidemiology and many other applications. The Poisson distribution has been the model of choice to analyze such data. In most cases mutual independence among the variables is assumed, however this fails to take into accounts the correlation between the outcomes of interests. A special bivariate form of the multivariate Lagrange family of distribution, names Generalized Bivariate Poisson Distribution, is considered in this paper. Objectives: We estimate the model parameters using the method of maximum likelihood and show that the model fits the count variables representing components of metabolic syndrome in spousal pairs. We use the likelihood local score to test the significance of the correlation between the counts. We also construct confidence interval on the ratio of the two correlated Poisson means. Methods: Based on a random sample of pairs of count data, we show that the score test of independence is locally most powerful. We also provide a formula for sample size estimation for given level of significance and given power. The confidence intervals on the ratio of correlated Poisson means are constructed using the delta method, the Fieller’s theorem, and the nonparametric bootstrap. We illustrate the methodologies on metabolic syndrome data collected from 4000 spousal pairs. Results: The bivariate Poisson model fitted the metabolic syndrome data quite satisfactorily. Moreover, the three methods of confidence interval estimation were almost identical, meaning that they have the same interval width.展开更多
基金The research is partially supported by the National Natural Science Foundation of China under Grant No.10471136Ph.D.Program Foundation of the Ministry of Education of ChinaSpecial Foundations of the Chinese Academy of Sciences and University of Science and Technology of China.
文摘This paper is concerned with the estimation of change-point in a binary response model with the assumption that the conditional median of the error term, given the explanatory variable, is zero. We construct an estimation of change-point based on the maximum score function and give its exponential convergence rate under some mild conditions.
基金Supported by the National Natural Science Foundation of China(No.10471136)Ph.D.Program Foundation of the Ministry of Education of China and Special Foundations of the Chinese Academy of Science and University of Science and Technology of China
文摘This paper studies the large sample properties of the change point estimates in binary response models. The estimate is obtained by maximizing the smoothed score function when the median of the latent error variable is assumed to be zero. An exponential convergence rate of the change point estimate is also established.
基金supported by National Natural Science Foundation of China(Nos.61272394,61201395 and61472119)the program for Science&Technology Innovation Talents in Universities of Henan Province(No.13HASTIT039)+1 种基金Henan Polytechnic University Innovative Research Team(No.T2014-3)Henan Polytechnic University Fund for Distinguished Young Scholars(No.J2013-2)
文摘For news video images, caption recognizing is a useful and important step for content understanding. Caption locating is usually the first step of caption recognizing and this paper proposes a simple but effective caption locating algorithm called maximum feature score region （MFSR） based method, which mainly consists of two stages： In the first stage, up/down boundaries are attained by turning to edge map projection. Then, maximum feature score region is defined and left/right boundaries are achieved by utilizing MFSR. Experiments show that the proposed MFSR based method has superior and robust performance on news video images of different types.
文摘Background: Bivariate count data are commonly encountered in medicine, biology, engineering, epidemiology and many other applications. The Poisson distribution has been the model of choice to analyze such data. In most cases mutual independence among the variables is assumed, however this fails to take into accounts the correlation between the outcomes of interests. A special bivariate form of the multivariate Lagrange family of distribution, names Generalized Bivariate Poisson Distribution, is considered in this paper. Objectives: We estimate the model parameters using the method of maximum likelihood and show that the model fits the count variables representing components of metabolic syndrome in spousal pairs. We use the likelihood local score to test the significance of the correlation between the counts. We also construct confidence interval on the ratio of the two correlated Poisson means. Methods: Based on a random sample of pairs of count data, we show that the score test of independence is locally most powerful. We also provide a formula for sample size estimation for given level of significance and given power. The confidence intervals on the ratio of correlated Poisson means are constructed using the delta method, the Fieller’s theorem, and the nonparametric bootstrap. We illustrate the methodologies on metabolic syndrome data collected from 4000 spousal pairs. Results: The bivariate Poisson model fitted the metabolic syndrome data quite satisfactorily. Moreover, the three methods of confidence interval estimation were almost identical, meaning that they have the same interval width.
