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The analysis method of the impact of missing audit information on the securities investment decisions based on logistic regression models

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APPLIED MATHEMATICS AND NONLINEAR SCIENCES 2023年第2期8卷 1179-1190页

作者： Ren, Wenjie China Railway First Survey & Design Inst Grp Co Lt Xian 710000 Shaanxi Peoples R China Univ New South Wales Armidale 4385 Australia

To identify the influence of the lack of audit information on the decision-making of securities investment, a method to analyse the impact of missing audit information on the decision-making of securities investment based on two logistic regression models was proposed in this paper. Assumptions about the impact of missing audit information on decision-making of investment were developed. Two logistic regression models were established to analyse the impact by using logistic regression functions. Variables in analysis models were designed to solve the logistic regression models with an ADMM algorithm, so as to analyse the impact of missing audit information on the decision-making of securities investment. The analysis of the impact of missing audit information on the securities investment decisions based on two selected models yielded the following results: (1) The lack of audit information was positively correlated with other decisions except the standard effective securities investment decisions;(2) To improve the problem of audit information missing, as the time went on, there was a strong correlation between the lack of audit information and the effectiveness of securities investment decisions;and (3) The lack of audit information was negatively correlated with the effectiveness of securities investment decisions.

关键词： logistic regression model audit information missing securities investment decision effectiveness impact

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Estimating the Risk of Lower Extremity Complications in Adults Newly Diagnosed With Diabetic Polyneuropathy: Retrospective Cohort Study

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JMIR Diabetes 2025年 10卷

作者： Alyce S Adams Catherine Lee Gabriel Escobar Elizabeth A Bayliss Brian Callaghan Michael Horberg Julie A Schmittdiel Connie Trinacty Lisa K Gilliam Eileen Kim Nima S Hejazi Lin Ma Romain Neugebauer Departments of Health Policy and of Epidemiology and Population Health School of Medicine Stanford University Encina Commons 615 Crothers Way Stanford CA United States Division of Research Kaiser Permanente Northern California Pleasanton CA United States Department of Family Medicine University of Colorado School of Medicine Institute for Health Research Kaiser Permanente Colorado Aurora CO United States Department of Neurology School of Medicine University of Michigan Ann Arbor MI United States Mid-Atlantic Permanente Research Institute Washington DC MD United States The Queen's Medical Center Honolulu HI United States Kaiser Permanente South San Francisco South San Francisco CA United States Kaiser Permanente Oakland Medical Center Oakland CA United States Department of Biostatistics Harvard T.H. Chan School of Public Health Boston MA United States

Background Diabetes-related lower extremity complications, such as foot ulceration and amputation, are on the rise, currently affecting nearly 131 million people worldwide. Methods for early detection of individuals at high risk remain elusive. While data-driven diabetic polyneuropathy algorithms exist, high-performing, clinically useful tools to assess risk are needed to improve clinical care. Objective This study aimed to develop an electronic medical record–based machine learning algorithm that would predict lower extremity complications. Methods We conducted a retrospective longitudinal cohort study to predict the risk of lower extremity complications within 24 months of an initial diagnosis of diabetic polyneuropathy. From an initial cohort of 468,162 individuals with at least 1 diagnosis of diabetic polyneuropathy at one of 2 multispecialty health care systems (based in northern California and Colorado) between April 2012 and December 2016, we created an analytic cohort of 48,209 adults with continuous enrollment, who were newly diagnosed with no evidence of end-of-life care. The outcome was any lower extremity complication, including foot ulceration, osteomyelitis, gangrene, or lower extremity amputation. We randomly split the data into training (38,569/48209; 80%) and testing (9,640/48209; 20%) datasets. In the training dataset, we used super Learner (SL), an ensemble learning method that employs cross-validation and combines multiple candidate risk predictors, into a single risk predictor. We evaluated the performance of the SL risk predictor in the testing dataset using the receiver operating characteristic curve and a calibration plot. Results Of the 48,209 individuals in the cohort, 2327 developed a lower extremity complication during follow-up. The SL risk estimator exhibited good discrimination (AUC=0.845, 95% CI 0.826-0.863) and calibration. A modified version of our SL algorithm, simplified to facilitate real-world adoption, had only slightly reduced

关键词： diabetes neuropathy machine learning risk prediction lower extremity complication diabetic AI artificial intelligence diabetic polyneuropathy California Colorado USA foot ulcer logistic regression model regression model lower extremity

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Analysis and prediction of older adult sports participation in South Korea using artificial neural networks and logistic regression models

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BMC GERIATRICS 2023年第1期23卷 676-676页

作者： Byun, Hyun Jeon, Sangwan Yi, Eun Surk Gachon Univ Dept Exercise Rehabil 191 Hambakmoe ro Incheon 21936 South Korea

Background Korea's aging population and the lack of older adult participation in sports are increasing medical expenses. Aims This study aimed to segment older adult sports participants based on their demographic characteristics and exercise practice behavior and applied artificial neural network and logistic regression models to these segments to best predict the effect of medical cost reduction. It presents strategies for older adult sports participation. Methods A sample comprising data on 1,770 older adults aged 50 years and above was drawn from the 2019 National Sports Survey. The data were analyzed through frequency analysis, hierarchical and K-means clustering, artificial neural network, logistic regression, cross-tabulation analyses, and one-way ANOVA using SPSS 23 and modeler 14.2. Results The participants were divided into five clusters. The artificial neural network and logistic analysis models showed that the cluster comprising married women in their 60s who participated in active exercise had the highest possibility of reducing medical expenses. Discussion Targeting women in their 60s who actively participate in sports, the government should expand the supply of local gymnasiums, community centers, and sports programs. If local gymnasiums and community centers run sports programs and appoint appropriate sports instructors, the most effective medical cost reduction effect can be obtained. Conclusions This study contributes to the field by providing insights into the specific demographic segments to focus on for measures to reduce medical costs through sports participation.

关键词： Neural networks model logistic regression model Older adult participants Medical costs

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The alarming problems of confounding equivalence using logistic regression models in the perspective of causal diagrams

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BMC MEDICAL RESEARCH METHODOLOGY 2017年第1期17卷 177-177页

作者： Yu, Yuanyuan Li, Hongkai Sun, Xiaoru Su, Ping Wang, Tingting Liu, Yi Yuan, Zhongshang Liu, Yanxun Xue, Fuzhong Shandong Univ Sch Publ Hlth Dept Biostat Jinan Shandong Peoples R China Peking Univ Sch Math Sci Beijing Peoples R China Shandong Univ Cheeloo Res Ctr Biomed Big Data Jinan Shandong Peoples R China

Background: Confounders can produce spurious associations between exposure and outcome in observational studies. For majority of epidemiologists, adjusting for confounders using logistic regression model is their habitual method, though it has some problems in accuracy and precision. It is, therefore, important to highlight the problems of logistic regression and search the alternative method. Methods: Four causal diagram models were defined to summarize confounding equivalence. Both theoretical proofs and simulation studies were performed to verify whether conditioning on different confounding equivalence sets had the same bias-reducing potential and then to select the optimum adjusting strategy, in which logistic regression model and inverse probability weighting based marginal structural model (IPW-based-MSM) were compared. The "do-calculus" was used to calculate the true causal effect of exposure on outcome, then the bias and standard error were used to evaluate the performances of different strategies. Results: Adjusting for different sets of confounding equivalence, as judged by identical Markov boundaries, produced different bias-reducing potential in the logistic regression model. For the sets satisfied G-admissibility, adjusting for the set including all the confounders reduced the equivalent bias to the one containing the parent nodes of the outcome, while the bias after adjusting for the parent nodes of exposure was not equivalent to them. In addition, all causal effect estimations through logistic regression were biased, although the estimation after adjusting for the parent nodes of exposure was nearest to the true causal effect. However, conditioning on different confounding equivalence sets had the same bias-reducing potential under IPW-based-MSM. Compared with logistic regression, the IPW-based-MSM could obtain unbiased causal effect estimation when the adjusted confounders satisfied G-admissibility and the optimal strategy was to adjust for the pare

关键词： Confounding equivalence logistic regression model Inverse probability weighting based marginal structural model Simulation study Causal diagrams

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An improved window opening behavior model involving the division of the dummy variable’s interval level:Case study of an office building in Xi’an during summer

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Building Simulation 2023年第11期16卷 2123-2144页

作者： Yaxiu Gu Tingting Wang Qingqing Dong Zhuangzhuang Ma Tong Cui Changgui Hu Kun Liu Song Pan Qian Qi Minyan Xie Department of Building Environment and Energy Engineering School of Civil EngineeringChang’an UniversityXi’an710061China State Key Laboratory of Green Building in Western China Xi’an University of Architecture&TechnologyXi’an710055China Hunan Architectural Design Institute Changsha410011China China United Engineering Corporation Limited Hangzhou310052China Beijing Key Laboratory of Green Built Environment and Energy Efficient Technology Beijing University of TechnologyBeijing100124China Shaanxi Environmental Investigation and Assessment Center Xi’an710054China

Window opening behavior significantly impacts indoor air quality,thermal comfort,and energy consumption.A field measurement was carried out in three typical rooms(a standard office,a meeting room and a smoking office)within an office *** window state and the physical environment were continuously recorded during the measured *** typical window opening behaviors were found in the measured samples,namely,active,moderate,and *** common logistic regression coefficient indicated that solar radiation exhibited the greatest effect on window opening behavior in the smoking office and standard ***,window opening behavior in the meeting room was the most strongly correlated with time of the day,mainly because of the meeting schedule for occupants in the meeting *** study discussed the dividing principles involved in setting the dummy variable interval level(discretizing continuous variables and dividing them into different intervals),and proposed a method to determine the optimal interval level of each *** improved model led to the increase in the prediction accuracy rate of the window being opened by 2.0%and 3.3%according to the comparison with the original model based on dummy variables and the common model based on continuous variables,*** study can provide a reference value for simulating energy consumption in office buildings in the future.

关键词： office building window opening behavior influencing factors logistic regression model dummy variables optimal interval level

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Depositional characteristics of debris flows in a rectangular channel with an abrupt change in slope

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JOURNAL OF HYDRO-ENVIRONMENT RESEARCH 2015年第3期9卷 420-428页

作者： Kim, Youngil Paik, Joongcheol Safety & Publ Adm Bur Gangneung City 210703 Gangwon South Korea Gangneung Wonju Natl Univ Dept Civil Engn Kangnung 210702 Gangwon South Korea

The degradation in the discharge capacity of culvert installed in mountain region is typically attributable to debris flow deposition, which may result in property damage and serious casualties. Understanding and accurately reproducing the behavior and the deposition features of debris flows with different material properties under various geometrical configurations are prerequisites for developing advanced design criteria for drainage culverts to prevent such damages. This study employed a series of laboratory experiments of debris flows in a rectangular channel with a constant width and with the change of slope to investigate the behavior of debris flows under a variety of conditions regarding approaching and culvert-like downstream channel slopes, representative grain sizes and sediment volume concentrations. The experimental results show that the thickness of debris flow can substantially increase (up to more than four times) compared to the thickness of clean water flow at the same flow rate. Sediment deposition of fine-material debris flow begins further downstream with an approximately 3 degrees higher surface slope compared to the case of coarse-material debris flow. Thus, the deposition area of coarse-material debris flow reaches further upstream in the approaching channel than that of fine-material debris flow. The total volume of coarse-material debris flow deposition in the channel appears to be larger than that of fine-material debris flow deposition. Based on experimental measurements and a series of complementary numerical simulations, we have developed a logistic regression model with the goal of determining optimal design criteria to prevent debris flow from depositing in the culvert-like channel. The resulting estimated logistic regression coefficients and derived deposition criteria curve indicate that the deposition of debris flow in the culvert-like channel is controlled predominantly by the bottom slope as well as the sediment volume concentrat

关键词： Debris flow Deposition Culvert Laboratory experiment logistic regression model

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Simulation-based Inference in a Zero-inflated Bernoulli regression model

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COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2016年第10期45卷 3597-3614页

作者： Diop, Aba Diop, Aliou Dupuy, Jean-Francois Univ La Rochelle Lab Math Image & Applicat La Rochelle France Univ Gaston Berger LERSTAD St Louis Senegal IRMAR Inst Natl Sci Appl Rennes Rennes France

The logistic regression model has become a standard tool to investigate the relationship between a binary outcome and a set of potential predictors. When analyzing binary data, it often arises that the observed proportion of zeros is greater than expected under the postulated logistic model. Zero-inflated binomial (ZIB) models have been developed to fit binary data that contain too many zeros. Maximum likelihood estimators in these models have been proposed and their asymptotic properties established. Several aspects of ZIB models still deserve attention however, such as the estimation of odds-ratios and event probabilities. In this article, we propose estimators of these quantities and we investigate their properties both theoretically and via simulations. Based on these results, we provide recommendations about the range of conditions (minimum sample size, maximum proportion of zeros in excess) under which a reliable statistical inference on the odds-ratios and event probabilities can be obtained in a ZIB regression model. A real-data example illustrates the proposed estimators.

关键词： Convergence logistic regression model Maximum likelihood estimation Mixture model Simulations 62 62J12

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Transfer learning with stacked reconstruction independent component analysis

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KNOWLEDGE-BASED SYSTEMS 2018年 152卷 100-106页

作者： Zhu, Yi Hu, Xuegang Zhang, Yuhong Li, Peipei Hefei Univ Technol Hefei 230009 Anhui Peoples R China

Significant improvements to transfer learning have emerged in recent years, because deep learning has been proposed to learn more higher level and robust features. However, most of existing deep learning approaches are based on the framework of auto-encoder or sparse auto-encoder, which pose challenges for independent component analysis and fail to measure similarities between data spaces. Therefore, in this paper, we propose a new strategy to achieve a better feature representation performance for transfer learning. There are several advantages in our method as follows: 1) The model of Stacked Reconstruction Independent Component Analysis (SRICA) is used to pursuit an optimal feature representation;2) The label information is used by logistic regression model to optimize representation features and the distance of distributions between domains is minimized by the method of KL-Divergence. Extensive experiments conducted on several image datasets demonstrate the superiority of our proposed method compared with all competing state-of-the-art methods. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Stacked RICA Transfer learning logistic regression model KL-Divergence

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Reliability analysis of TBM disc cutters under different conditions

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Underground Space 2021年第2期6卷 142-152页

作者： Bolong Liu Haiqing Yang Shivakumar Karekal School of Civil Engineering Chongqing UniversityChongqing 400045China School of Civil Mining and Environmental EngineeringUniversity of WollongongNSW 2522Australia

The reliability of disc cutters has a significant influence on the safety and working efficiency of tunnel boring machines(TBMs).To investigate the reliability of disc cutters under different geological and operational conditions,we conducted a series of novel rolling cutting tests on intact and jointed sandstone blocks using different dip angles and *** normal forces and rotational speeds of the cutterhead were also applied during the experiment.A novel reliability estimation method,based on a logistic regression model,was then proposed,and the influence of dip angle,strata,normal force,and rotational speed on the reliability of the disc cutter were *** reliability estimation method consisted of data acquisition regarding the normal force and cutter wear,feature extraction using wavelet packet transform and correlation analysis,and the estimation of the logistic regression *** obtain the spectrum and normalized wavelet energy for each frequency band,we decomposed the time domain of the normal force by the wavelet packet transform.A correlation analysis was employed to determine the feature frequency bands that were sensitive to wear *** the basis of salient feature parameters and wear loss,the logistic regression model was established to evaluate the reliability of disc *** analytical results indicated that the optimal dip angle for rock cutting was 30°.In the presence of mixed-face and single ground,the reliability of disc cutters was primarily affected by the difficulty of TBM excavation and wear loss,*** increase in normal force and rotational speed aggravated wear on the cutter,thus reducing ***,compared with Rabinowicz’s formula,the proposed method considers various geological and operational conditions,making the proposed method more applicable to estimate the reliability of disc cutters.

关键词： Tunnel boring machine Sandstone Disc cutter reliability logistic regression model

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Credit Scoring Based on the Set-Valued Identification Method

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Journal of Systems Science & Complexity 2020年第5期33卷 1297-1309页

作者： WANG Ximei HU Min ZHAO Yanlong DJEHICHE Boualem Department of Mathematics KTH Royal Institute of TechnologyStockholm 10044Sweden Key Laboratory of Systems and Control Institute of Systems ScienceAcademy of Mathematics and SystemsScienceChinese Academy of SciencesBeijing 100190China School of Mathernatical Sciences University ofChinese Academy of SciencesBeijing 100190China Department of Mathematics KTH Rogal Institute of TechnologyStockholm 10044Sweden

Credit scoring is one of the key problems in financial risk *** paper studies the credit scoring problem based on the set-valued identification method,which is used to explain the relation between the individual attribute vectors and classification for the credit worthy and credit worthless *** particular,system parameters are estimated by the set-valued identification algorithm based on a given recognition *** order to illustrate the efficiency of the proposed method,practical experiments are conducted for credit card applicants of Australia and credit card holders from Taiwan,*** empirical results show that the set-valued model has a higher prediction accuracy on both small and large numbers of data set compared with logistic regression ***,parameters estimated by the set-valued identification method are more stable,which provide a meaningful and logical explanation for extracting factors that influence the borrowers’credit scorings.

关键词： Credit scoring logistic regression model prediction accuracy set-valued model

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