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Epidemiologic characteristics of diabetes in children aged 0–14 years in Busan and Gyeonnam Province, Korea (2001–2010)

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International Journal of Pediatric Endocrinology 2013年第1期2013卷 1-1页

作者： Suyoung Hong Hyunji Kim Junghyun Lee Jaeho Yoo Department of Pediatrics Maryknoll Hospital Hong Kong Department of Pediatrics Wallace Memorial Baptist Hospital Busan Korea Department of Pediatrics Kosin University Gospel Hospital Busan Korea College of Medicine Dong-A University Busan Korea

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A Monte Carlo simulation study on partially adaptive estimators of linear regression models

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JOURNAL OF APPLIED STATISTICS 2011年第8期38卷 1681-1699页

作者： Kantar, Yeliz Mert Usta, Ilhan Acitas, Sukru Anadolu Univ Dept Stat TR-26470 Eskisehir Turkey

This paper presents a comprehensive comparison of well-known partially adaptive estimators (PAEs) in terms of efficiency in estimating regression parameters. The aim is to identify the best estimators of regression parameters when error terms follow from normal, Laplace, Student's t, normal mixture, lognormal and gamma distribution via the Monte Carlo simulation. In the results of the simulation, efficient PAEs are determined in the case of symmetric leptokurtic and skewed leptokurtic regression error data. Additionally, these estimators are also compared in terms of regression applications. Regarding these applications, using certain standard error estimators, it is shown that PAEs can reduce the standard error of the slope parameter estimate relative to ordinary least squares.

关键词： linear regression model non-normal error terms partially adaptive estimator sandwich estimator Monte Carlo simulation

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The Koul-Susarla-Van Ryzin and weighted least squares estimates for censored linear regression model: A comparative study

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2007年第12期51卷 6488-6497页

作者： Bao, Yanchun He, Shuyuan Mei, Changlin Univ Manchester Sch Math Manchester M13 9PL Lancs England Peking Univ Sch Math Sci Beijing 100871 Peoples R China Xian Jiaotong Univ Sch Sci Xian 710049 Peoples R China

The Koul-Susarla-Van Ryzin (KSV) and weighted least squares (WLS) methods are simple to use techniques for handling linear regression models with censored data. They do not require any iterations and standard computer routines can be employed for model fitting. Emphasis has been given to the consistency and asymptotic normality for both estimators, but the finite sample performance of the WLS estimator has not been thoroughly investigated. The finite sample performance of these two estimators is compared using an extensive simulation study as well as an analysis of the Stanford heart transplant data. The results demonstrate that the WLS approach performs much better than the KSV method and is reliable for use with censored data. (c) 2007 Elsevier B.V. All rights reserved.

关键词： censored data linear regression model weighted least squares Stanford heart transplant data

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Estimation in a linear regression model under the Kullback-Leibler loss and its application to model selection

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JOURNAL OF STATISTICAL PLANNING AND INFERENCE 2007年第7期137卷 2487-2508页

作者： Kubokawa, Tatsuya Tsukuma, Hisayuki Univ Tokyo Fac Econ Bunkyo Ku Tokyo 1130033 Japan Toho Univ Fac Med Ota Ku Tokyo 1430012 Japan

This paper is concerned with the problem of constructing a good predictive distribution relative to the Kullback-Leibler information in a linear regression model. The problem is equivalent to the simultaneous estimation of regression coefficients and error variance in terms of a complicated risk, which yields a new challenging issue in a decision-theoretic framework. An estimator of the variance is incorporated here into a loss for estimating the regression coefficients. Several estimators of the variance and of the regression coefficients are proposed and shown to improve on usual benchmark estimators both analytically and numerically. Finally, the prediction problem of a distribution is noted to be related to an information criterion for model selection like the Akaike information criterion (AIC). Thus, several AIC variants are obtained based on proposed and improved estimators and are compared numerically with AIC as model selection procedures. (c) 2006 Elsevier B.V. All rights reserved.

关键词： Akaike information criterion decision theory error variance estimation information criterion James-Stein estimator Kullback-Leibler information linear regression model model selection regression coefficients risk function truncated procedure uniform domination

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Detecting Change Points in Segmented linear regression Heteroscedastic models by Empirical Likelihood Methods

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International Journal of Intelligent Technologies and Applied Statistics 2012年第1期5卷 75-85页

作者： Hua-Ling Zhao Han-Feng Chen Department of Statistics School of Science Wuhan University of Technology Wuhan China Department of Mathematics and Statistics Bowling Green State University Bowling Green USA

There have been increasingly keen interests in recent years in detecting change points under a segmented linear regression model with a series of observations. Central to the problem is how to detect the change point. For example, in econometrics it is an important and yet difficult problem how to determine as early as possible the starting and the ending points of a suspected ongoing recession. Existing detection procedures are mostly constructed under the assumption of homoscedasticity in parametric models or via classic rank-test statistics in nonparametric models. The inference based on these procedures are sometimes invalidated by heteroscedasticity. Another problem in the existing procedures is that the covariate values xi's are not used efficiently to construct a detection procedure. In this paper, we propose a new empirical likelihood approach to tackle these problems. The new method is an improvement over the procedure recently proposed by [5]. Empirical likelihood is a nonparametric technique for inference on functional population characteristics such as means and medians. One of the most appealing features of empirical likelihood methods is that it has large sampling properties similar to its counterpart likelihood-based parametric methods and enjoys both the robustness from its nonparametric nature and the efficiency from its likelihood construction. A bootstrap method is proposed to approximate the p-values of the new change point detection procedure. Simulation results show that the new procedure performs well with great improvement over [5]'s procedure. [ABSTRACT FROM AUTHOR]

关键词： Bootstrap Change points Empirical likelihood Heteroscedasticity linear regression model

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THE RESEARCH OF A RESOURCE-AWARE CLOUD COMPUTING ARCHITECTURE BASED ON WEB SECURITY

THE RESEARCH OF A RESOURCE-AWARE CLOUD COMPUTING ARCHITECTUR...

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2012 IEEE 2nd International Conference on Cloud Computing and Intelligence Systems

作者： Xiaoni Wang Xuedong Gao Dongling School of Economic and Management University of Science and Technology Beijing School of Applied Science Beijing Information Science and Technology University

Web hosting has a lot of problems, such as low security, waste of bandwidth resource or sudden lack of resource. A self-adaptive cloud computing architecture--RA-Cloud architecture (Resource-aware Cloud Computing Architecture) based on web security in resource-aware situation is proposed. This architecture uses cloud computing technology and self-adaptive linear regression model to estimate the dynamic migration threshold and to search the most suitable neighbor node for data migration. This platform and algorithm distributes the huge amount of computing resource to many server "end" according to the resource constrained self-adaptive policy. The new method increases the user access speed, enhances the anti-attack ability, blocks the attacks effectively and improves the web site security.

关键词： Cloud computing architecture Web security Resource-aware Self-adaptive linear regression model

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Personal optimization method to estimate mood using heart rate variability in daily life: Mood estimation using heart rate variability

Personal optimization method to estimate mood using heart ra...

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International Conference on Soft Computing and Intelligent Systems

作者： Kohzoh Yoshino Katsunori Matsuoka Health Research Institute National Institute of Advanced Industrial Science and Technology (AIST) Hokkaido Center National Institute of Advanced Industrial Science and Technology (AIST)

ISBN: (纸本)9781467327428

The aim of this study is to present a method of assessing eight types of mood that is optimized to every individual on the basis of the heart rate variability (HRV) data which, to eliminate the influence of the inter-individual variability, are measured in a long time period during daily life. Eight types of mood are happiness, tension, fatigue, anxiety, depression, anger, vigor, and confusion. HRV and body accelerations were recorded from nine normal subjects for two months of normal daily life. Fourteen HRV indices were calculated with the HRV data at 512 seconds prior to the time of every mood level report. Data to be analyzed were limited to those with body accelerations of 30 mG (0.294 m/s~2) and lower. Further, the differences from the reference values in the same time zone were calculated with both the mood score (Δmood) and HRV index values (ΔHRVI). The multiple linear regression model that estimates Amood from the scores for principal components of ΔHRVI were then constructed for each individual. The data were divided into training data set and test data set in accordance with the 2-fold cross validation method. Multiple linear regression coefficients were determined using the training data set, and with the optimized model its generalization capability was checked using the test data set. The model was most effective on estimating tension compared with other seven types of mood. The subjects' mean Pearson correlation coefficient was 0.52 with the training data set and 0.40 with the test data set. We proposed a method of assessing mood that is optimized to every individual based on HRV data measured over a long period of daily life.

关键词： Mood Heart rate variability Personal adaptation linear regression model Daily life Psychophysiology

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A Integrated Computational Approach for Protein Sub-network Detection in Parkinson's Disease

A Integrated Computational Approach for Protein Sub-network ...

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2010 3rd International Conference on Computer and Electrical Engineering(ICCEE 2010)

作者： Yue Huang Yunying Huang Institute of Signal and Information Processing Department of Communication Engineering Xiamen University Department of Electronic Engineering Xiamen University

Parkinson's disease (PD) is a typical case of neurodegenerative disorder, which often impairs the sufferer's motor skills, speech, and other functions. Combination of protein-protein interaction (PPI) network analysis and gene expression studies provides a better insight of Parkinson's disease. A computational approach was developed in our work to identify protein signal network in PD study. First, a linear regression model is setup and then a network-constrain regularization analysis was applied to microarray data from transgenic mouse model with Parkinson's disease. Then protein network was detected based on an integer linear programming model by integrating microarray data and PPI database.

关键词： Parkinson’s disease microarray data linear regression model integer linear programming protein network detection

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Application of Generalized Predictive Control to a Peristaltic Pump

Application of Generalized Predictive Control to a Peristalt...

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2012 3rd International Conference on Applied Mechanics and Mechanical Engineering(ICAMME 2012)

作者： Hu Guo-feng Chen Le-geng School of Electronic Engineering and Automation Guilin University of Electronic Technology

Peristaltic pump is widely used in medical practice and chemical industry. But in some applications, which ask for small flow and high precision, a small jump of output to the peristaltic pump is quite serious. Therefore, it restricts the use and performance of peristaltic pump. This paper tries to introduce generalized predictive control method to the system of peristaltic pump. Besides, the results of simulation and experiment show that the static error of system using the mentioned method is smaller than traditional PID control which asks for a continuous output, so as to the output error when the system works at the small titration.

关键词： Peristaltic Pump Predictive Control linear regression model Parameter Identification

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Causality Analysis of Neural Connectivity: Critical Examination of Existing Methods and Advances of New Methods

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IEEE TRANSACTIONS ON NEURAL NETWORKS 2011年第6期22卷 829-844页

作者： Hu, Sanqing Dai, Guojun Worrell, Gregory A. Dai, Qionghai Liang, Hualou Hangzhou Dianzi Univ Coll Comp Sci Hangzhou 310018 Zhejiang Peoples R China Mayo Clin Dept Neurol Div Epilepsy & Electroencephalog Rochester MN 55905 USA Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Drexel Univ Sch Biomed Engn Sci & Hlth Syst Philadelphia PA 19104 USA

Granger causality (GC) is one of the most popular measures to reveal causality influence of time series and has been widely applied in economics and neuroscience. Especially, its counterpart in frequency domain, spectral GC, as well as other Granger-like causality measures have recently been applied to study causal interactions between brain areas in different frequency ranges during cognitive and perceptual tasks. In this paper, we show that: 1) GC in time domain cannot correctly determine how strongly one time series influences the other when there is directional causality between two time series, and 2) spectral GC and other Granger-like causality measures have inherent shortcomings and/or limitations because of the use of the transfer function (or its inverse matrix) and partial information of the linear regression model. On the other hand, we propose two novel causality measures (in time and frequency domains) for the linear regression model, called new causality and new spectral causality, respectively, which are more reasonable and understandable than GC or Granger-like measures. Especially, from one simple example, we point out that, in time domain, both new causality and GC adopt the concept of proportion, but they are defined on two different equations where one equation (for GC) is only part of the other (for new causality), thus the new causality is a natural extension of GC and has a sound conceptual/theoretical basis, and GC is not the desired causal influence at all. By several examples, we confirm that new causality measures have distinct advantages over GC or Granger-like measures. Finally, we conduct event-related potential causality analysis for a subject with intracranial depth electrodes undergoing evaluation for epilepsy surgery, and show that, in the frequency domain, all measures reveal significant directional event-related causality, but the result from new spectral causality is consistent with event-related time-frequency power spectrum act

关键词： Event-related potential Granger or Granger-like causality linear regression model new causality power spectrum

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