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Variable selection for sparse logistic regression

METRIKA 2020年第7期83卷 821-836页

作者： Yin, Zanhua Gannan Normal Univ Ganzhou Peoples R China

We consider the variable selection problem in a sparse logistical regression model. Inspired by the square-root Lasso, we develop a weighted score Lasso for logistical regression. The new method yields the estimation l(1) error bound under similar assumptions as introduced in Bach et al. (Electron J Stat 4:384-414, 2010). Compared to standard Lasso, the weighted score Lasso provides a direct choice for the tuning parameter. Both theoretical and simulation results confirm the satisfactory performance of the proposed method. We illustrate our methodology with a real microarray data set.

关键词： Score function High dimensions Lasso logistic regression model Sparse

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Online Health Resource Use by Individuals With Inflammatory Bowel Disease: Analysis Using the National Health Interview Survey

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JOURNAL OF MEDICAL INTERNET RESEARCH 2020年第9期22卷 e15352页

作者： Yin, Rong Neyens, David M. Clemson Univ Dept Ind Engn 100 Freeman Hall Clemson SC 29634 USA

Background: The internet has enabled convenient and efficient health information searching which is valuable for individuals with chronic conditions requiring some level of self-management. However, there is little research evaluating what factors may impact the use of the internet for health-related tasks for specific clinical populations, such as individuals with inflammatory bowel diseases. Objective: Our goal was to investigate the factors that influence internet use in acquiring health information by individuals with inflammatory bowel diseases. Specifically, we identified factors associated with internet searching behavior and using the internet for completing health-related tasks. Methods: We used 2016 National Health Interview Survey weighted data to develop logistic regression models to predict the likelihood that individuals with inflammatory bowel diseases would use the internet for 2 types of tasks: seeking health information through online searches and using the internet to perform health-related tasks including scheduling appointments and emailing care providers. Results: 2016 National Health Interview Survey weighted data include more than 3 million weighted adult respondents with inflammatory bowel diseases (approximately 1.29% of adults in the weighted data set). Our results suggest that approximately 66.3% of those with inflammatory bowel diseases reported using the internet at least once a day, and approximately 14.7% reported being dissatisfied with their current health care. About 62.3% of those with inflammatory bowel diseases reported that they had looked up health information online, 16.3% of those with inflammatory bowel diseases reported that they had scheduled an appointment with a health care provider online, and 21.6% reported having used a computer to communicate with a health provider by email We found that women who were self-regulating their care were more likely to look up health information online than others. Both middle-aged and

关键词： internet searching behavior access to information inflammatory bowel disease logistic regression model National Health Interview Survey

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Incorporating spectral data into logistic regression model to classify land cover: a case study in Mt. Qomolangma (Everest) National Nature Preserve

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INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE 2012年第10期26卷 1845-1862页

作者： Gao, Jungang Zhang, Yili Chinese Acad Sci Inst Geog Sci & Nat Resources Res Dept Land Use Land Cover Change & Land Resources Beijing Peoples R China Chinese Acad Sci Grad Sch Dept Resources & Environm Beijing Peoples R China

Only few models for land-cover classification incorporated spectral data into ordinary logistic regression (OL model) in the Mt. Qomolangma (Everest) National Nature Preserve (QNNP) in China. In this study, spectral variables were incorporated into OL model and autologistic regression (AL) model to classify six main land covers. Twelve environmental variables and seven spectral variables of 10,000 stratified random sites in the QNNP were quantified and analyzed;OL model, AL model, OL model with spectral data (OLM model), and AL model with spectral data (ALM model) were estimated. The OLM and ALM models produced better estimates of regression coefficients and significantly improved model performance and overall accuracy for the grassland, sparsely vegetated land, and bare land compared with OL and AL models.

关键词： logistic regression model autologistic regression model spectral variable land cover classification Mt. Qomolangma National Nature Preserve

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Study on Management Characteristics and Earnings Forecast Disclosure Willingness Based on logistic regression model

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JOURNAL OF COMPUTERS 2012年第8期7卷 1951-1958页

作者： Nan, Xu Yanshan Univ Sch Econ & Management Qinhuangdao Peoples R China

This paper studied the relationship between the management characteristics and the earnings forecast disclosure willingness within Chinese listed companies. Management characteristics were being defined and summarized as four aspects: structure characteristics, incentive characteristics, background characteristics and external governance mechanism. The logistic regression model was applied to carry out an empirical analysis on the effect of specific variables of management characteristics on the earnings forecast disclosure willingness. The results show that the board size, managers' changes, asset-liability ratio and managers' stockholdings are the major factors. The board size relates with the earnings forecast disclosure willingness significant negative;manager's changes, assetliability ratio and managers' stockholdings relate with the earnings forecast disclosure willingness significant positive. Other variables of management characteristics have no significant influence.

关键词： management characteristics earnings forecast disclosure willingness logistic regression model

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Semiparametric estimation of logistic regression model with missing covariates and outcome

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METRIKA 2012年第5期75卷 621-653页

作者： Lee, Shen-Ming Li, Chin-Shang Hsieh, Shu-Hui Huang, Li-Hui Univ Calif Davis Div Biostat Dept Publ Hlth Sci Davis CA 95616 USA Feng Chia Univ Dept Stat Taichung 40724 Taiwan Overseas Chinese Univ Dept Int Trade Taichung Taiwan

We consider a semiparametric method to estimate logistic regression models with missing both covariates and an outcome variable, and propose two new estimators. The first, which is based solely on the validation set, is an extension of the validation likelihood estimator of Breslow and Cain (Biometrika 75:11-20, 1988). The second is a joint conditional likelihood estimator based on the validation and non-validation data sets. Both estimators are semiparametric as they do not require any model assumptions regarding the missing data mechanism nor the specification of the conditional distribution of the missing covariates given the observed covariates. The asymptotic distribution theory is developed under the assumption that all covariate variables are categorical. The finite-sample properties of the proposed estimators are investigated through simulation studies showing that the joint conditional likelihood estimator is the most efficient. A cable TV survey data set from Taiwan is used to illustrate the practical use of the proposed methodology.

关键词： Missing value logistic regression model Missing outcome Missing covariates

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Assessing gully erosion susceptibility in Mayurakshi river basin of eastern India

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ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY 2020年第2期22卷 883-914页

作者： Debanshi, Sandipta Pal, Swades Univ Gour Banga Dept Geog Malda India

The present study assessed the extent to which the gully erosion susceptibility could be successfully modeled in Mayurakshi river basin as a function of four gully controlling factor clusters (topographical, erosivity, erodibility, and resisting). Gully erosion zones have been identified through two different models (correlation matrix and logistic regression) incorporating 15 conditioning indicators. Both the models have generated quite identical result regarding gully erosion susceptibility. It illustrates that extreme erosion-susceptible zones cover almost 16% of the basin area followed by high susceptibility with almost 28% areal coverage. This susceptibility is dominantly confined within the upper catchment. Older and newer alluvial plains of the lower catchment belong to low susceptibility to relatively safe situation. models are validated by constructing ROC curve and calculating Kappa statistics. Both the approaches certified the validity of the models. The factor clustering shows that the positively influencing clusters like erosivity factors and erodibility factors are playing a dominant role with, respectively, 76 and 75% correlation with gully erosion susceptibility, whereas the resistant factors achieved only 32% correlation which denotes week significance due to containing sparse vegetation cover having low canopy density as well as poor crop management and conservation practice.

关键词： Gully erosion susceptibility Correlation matrix logistic regression model Factor clustering GIS modeling model validation Mayurakshi river basin

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Predicting COVID-19 trends in Canada: a tale of four models

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COGNITIVE COMPUTATION AND SYSTEMS 2020年第3期2卷 112-118页

作者： Zhang, Wandong Zhao, W. G. (Will) Wu, Dana Yang, Yimin Univ Windsor Dept Elect & Comp Engn 401 Sunset Ave Windsor ON Canada Lakehead Univ Fac Business Adm 955 Oliver Rd Thunder Bay ON Canada Univ Toronto Fac Arts & Sci 27 Kings Coll Circle Toronto ON Canada Univ Toronto Fac Arts & Sci 955 Oliver Rd Thunder Bay ON Canada

This study aims to offer multiple-model informed predictions of COVID-19 in Canada, specifically through the use of deep learning strategy and mathematical prediction models including long-short term memory network, logistic regression model, Gaussian model, and susceptible-infected-removed model. Using the published data as of the 10th of April 2020, the authors forecast that the daily increased number of infective cases in Canada has not reached the peak turning point and will continue to increase. Therefore, Canada's healthcare services need to be ready for the magnitude of this pandemic.

关键词： diseases logistics learning (artificial intelligence) regression analysis health care multiple-model informed predictions tale COVID-19 trends Canada infective cases daily increased number susceptible-infected-removed model Gaussian model logistic regression model long-short term memory network mathematical prediction models deep learning strategy

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Epigenome-wide association study reveals CpG sites related to COG of neuroblastoma

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BIOSCIENCE REPORTS 2020年第5期40卷 1-7页

作者： Zhao, Hao Zhou, Xiaojun Sun, Hu Zhao, Dongyun Liu, Hongfei Huang, Bin Li, Xingang Gu, Yinghao Shandong Univ Qilu Hosp Dept Neurosurg Jinan 250012 Shandong Peoples R China Shandong Univ Inst Brain & Brain Inspired Sci Jinan 250012 Shandong Peoples R China Shandong Univ Dept Neurosurg Zibo Cent Hosp Zibo 255036 Shandong Peoples R China Shandong Univ Zibo Cent Hosp Dept Paediat Neurol Zibo 255036 Shandong Peoples R China Shandong Univ Shandong Key Lab Brain Funct Remodeling Qilu Hosp Jinan 250012 Shandong Peoples R China

Background. Neuroblastoma (NB) is the most common extracranial solid tumor in infants and children. Its variable location and complex pathogenesis make NB hard for early diagnosis and risk classification. Methodology. We analyzed the methylation data of 236 samples from patients with NB in Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. Kaplan-Meier survival analysis was used for comparing overall survival of NB patients in different groups. Epigenome-wide association study (EWAS) was conducted to screen CpGs significantly associated with NB patients' Children's Oncology Group (COG). logistic regression method was used for constructing a model to predict NB patients' COG. Results. NB patients in low COG showed significantly superior prognosis than those in high COG. A total of seven CpG sites were found closely related to COG. logistic regression model based on those CpGs showed superior performance in separating NB patients in different COGs. Conclusions. The present study highlights the important role of DNA methylation in NB development, which might provide evidence for treatment decisions for children NB.

关键词： CpG EWAS logistic regression model Neuroblastoma Risk classification

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Quality of phase I clinical drug trials: Influence of organizational management factors

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JOURNAL OF CLINICAL PHARMACY AND THERAPEUTICS 2020年第5期45卷 968-975页

作者： Zhao, Yang Yang, Qiuxia Zhang, Xinping Huazhong Univ Sci & Technol Tongji Med Coll Sch Med & Hlth Management 13 Hangkong Rd Wuhan Hubei Peoples R China

What is known and Objective Focusing on the tolerance and pharmacokinetics of new drugs, phase I clinical drug trials are characterized by high risk, poor compliance and management difficulties. High-quality clinical drug trials ensure subjects' safety while extending new drug research and development. Many studies have examined micro-level concerns of trial design and implementation rather than macro-level factors. Accordingly, we evaluated the quality of phase I clinical drug trials (trial quality) and analysed the influence of organizational management factors from a macro-level perspective. Methods We surveyed staff at clinical trial institutions engaged in phase I clinical drug trials in China using convenience sampling. We employed a five-point Likert-scale questionnaire, comprising five items on phase I clinical drug trial quality and items on organizational management factors. Data from 604 questionnaires were analysed. We utilized a logistic regression model to estimate the influence of organizational management factors on trial quality, using individual demographic factors as controlling variables. Results and discussion The trial quality score was 3.81, which indicates that substantial improvement is required. Government regulation, industry management and medical institution management had a positive effect on trial quality:beta = 0.842, 0.691 and 0.579, respectively;P beta = -0.476;P = .013. Research team management had no effect on trial quality:beta = 0.325;P = .141. What is new and Conclusion This study is the first to model the influence of organizational management factors on the quality of phase I drug trials involving different organizations from a macro-perspective. Efforts are needed to help research teams take responsibility for trial quality and to correct the negative impact of contract research organizations on trial quality.

关键词： influencing factors logistic regression model organizational management phase I clinical drug trials quality

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Linking the Driver's Driving Performance and Their Driving Safety: A Bridge via Naturalistic Driving Study 20

Linking the Driver's Driving Performance and Their Driving S...

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20th COTA International Conference of Transportation Professionals (CICTP) - Advanced Transportation, Enhanced Connection

作者： Su, Zhizhuo Li, Penghui Chen, Long Zhang, Qiang China Automot Engn Res Inst Intelligent Vehicle Test & Evaluat Ctr Beijing Peoples R China China Automot Engn Res Inst Co Ltd State Key Lab Vehicle NVH & Safety Technol Chongqing Peoples R China

ISBN: (纸本)9780784483053

As traffic accident remains the leading causation of death in the world, this preventable tragedy could be prevented by understanding the driver's behavior further. Specifically, which driving performance measure (DPM) should the research employ to evaluate the driving behavior and how the results should be interpreted requires more investigation. This is a twofold analysis to understand the DPM better. According to the result, in the one-to-one relationship, all the DPMs didn't show a statistically significant relationship with the safety measure. In terms of the logistic models, five DPMs are selected as the predictor. Except for Standard Deviation of Only Deceleration, at the significance level of 0.05, the slope of the other four measures are significant in the regression formula. The model performs well in fitting the training data. This study extends our recognition about DPM and can be considered as the reference for DPMs.

关键词： Driving performance measure Driving safety Naturalistic driving study logistic regression model

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