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Factors of the Development of Water Erosion in the Zone of Recreation Activity in the Ol'khon Region

EURASIAN SOIL SCIENCE 2018年第2期51卷 221-228页

作者： Znamenskaya, T. I. Vanteeva, J. V. Solodyankina, S. V. Russian Acad Sci Sochava Inst Geog Siberian Branch Ul Ulan Batorskaya 1 Irkutsk 664033 Russia

Specific features of water erosion of thin soils under conditions of nonpercolative water regime and intense recreational loads were studied in the Ol'khon region (Irkutsk oblast). An experiment on the transfer of terrigenous particles under the impact of rainfall simulation was performed. A thorough description of landscape characteristics affecting water erosion development was made. As a result, a multiple regression equation linking the transported matter with the slope steepness, projective cover of vegetation, the degree of vegetation degradation, and the fine sand content in the upper soil horizon was developed;the multiple correlation coefficient R reached 0.86. On this basis, the map of water erosion assessment for the study area was compiled with the use of landscape and topographic maps. The maximum intensity of water erosion is typical of the anthropogenically transformed landscapes on steep slopes with the low vegetative cover on the mountainous noncalcareous steppe soils and on thin loamy sandy surface-gravelly chestnut-like soils.

关键词： linear regression model experiments recreation load Lithic Leptosols

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Variational Bayesian inference of linear state space models

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JOURNAL OF ENGINEERING-JOE 2019年第23期2019卷 8531-8534页

作者： Pan, Chuanchao Wang, Jingzhuo Dong, Zijian China Univ Min & Technol Sch Informat & Control Engn Xuzhou Jiangsu Peoples R China Huaihai Inst Technol Sch Elect Engn Lianyungang Peoples R China

This article studies a variational Bayesian method to fix the linear regression (LR) model of which regressors are Gaussian distributed with non-zero prior means, and then apply the method to the linear state space (LSS) model. Here, we innovatively transform the LSS model into a special LR model: In each state, the value obtained from the predict step can be seen as the prior mean of the regressors, and the update step can be viewed as the iterative solving in LR model with non-zero prior means. We simulate the proposed algorithm with high-dimensional discrete LSS models where most states are prior zeros;simulation results show that the proposed algorithm and its applications in LSS are both effective and reliable.

关键词： regression analysis Bayes methods iterative methods Gaussian distribution inference mechanisms variational techniques linear state space model linear regression model high-dimensional discrete LSS models variational Bayesian inference method LR model Gaussian distribution iterative solution

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Explicit solution to the minimization problem of generalized cross-validation criterion for selecting ridge parameters in generalized ridge regression

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HIROSHIMA MATHEMATICAL JOURNAL 2018年第2期48卷 203-222页

作者： Yanagihara, Hirokazu Hiroshima Univ Grad Sch Sci Dept Math 1-3-1 Kagamiyama Higashihiroshima Hiroshima 7398626 Japan

This paper considers optimization of the ridge parameters in generalized ridge regression (GRR) by minimizing a model selection criterion. GRR has a major advantage over ridge regression (RR) in that a solution to the minimization problem for one model selection criterion, i.e., Mallows' C-p criterion, can be obtained explicitly with GRR, but such a solution for any model selection criteria, e.g., C-p criterion, cross-validation (CV) criterion, or generalized CV (GCV) criterion, cannot be obtained explicitly with RR. On the other hand, C-p criterion is at a disadvantage compared to CV and GCV criteria because a good estimate of the error variance is required in order for C-p criterion to work well. In this paper, we show that ridge parameters optimized by minimizing GCV criterion can also be obtained by closed forms in GRR. We can overcome one disadvantage of GRR by using GCV criterion for the optimization of ridge parameters. By using the result, we propose a principle component regression hybridized with the GRR that is a new method for a linear regression with high-dimensional explanatory variables.

关键词： Explicit optimal solution Generalized ridge regression Generalized cross-validation criterion linear regression model High-dimensional explanatory variables Multiple ridge parameters Principal component regression Selection of ridge parameters

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Positive Aspects of Caregiving and Caregiver Burden: A Study of Caregivers of Patients With Dementia

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JOURNAL OF GERIATRIC PSYCHIATRY AND NEUROLOGY 2018年第1期31卷 34-38页

作者： Abdollahpour, Ibrahim Nedjat, Saharnaz Salimi, Yahya Arak Univ Med Sci Sch Publ Hlth Dept Epidemiol Arak Iran Univ Tehran Med Sci Sch Publ Hlth Dept Epidemiol & Biostat Tehran Iran Kermanshah Univ Med Sci Sch Publ Hlth Dept Epidemiol Kermanshah Iran

Background: Now positive aspect of caregiving (PAC) is well-defined as caregiver gains, satisfaction, meaningful life, and enhanced family relationship. The adjusted association of PAC and caregiver burden is not well acknowledged. This study investigated the association of caregiver burden and PAC adjusting for potential confounders. Methods: This was a cross-sectional study that recruited 132 caregivers. A linear regression model with PAC was used to estimate the adjusted associations. Results: The caregiver burden was negatively associated with PAC (mean difference in PAC per a 1-unit increase in caregiver burden = -0.12, 95% confidence interval: -0.18 to -0.056;P < .001). This association remained after adjustment for caregivers' age and marital status as well as patients' dependency level. Conclusion: The negative significant association of caregiver burden with PAC reinforces the need for interventional and/or educational programs aiming at decreasing the overall imposed burden. This can play an important role in improving caregivers' general health and quality of life.

关键词： caregiver burden dementia linear regression model positive aspect of caregiving

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Predictive modeling for Advanced Virtual Metrology: A Tree-Based Approach 23

Predictive Modeling for Advanced Virtual Metrology: A Tree-B...

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23rd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Liu, Yang Li, Xin Duke Univ Dept Elect & Comp Engn Durham NC 27708 USA

ISBN: (纸本)9781538671085

The rapid development of industry 4.0 has promoted the extensive adoption of big data analytics for manufacturing industry. In this domain, virtual metrology is a critical technique that is able to reduce manufacturing cost over a large amount of practical applications. In this paper, we propose a novel tree-based approach for simultaneous feature selection and predictive modeling to facilitate efficient virtual metrology. The proposed method accurately identifies multiple feature sets and then chooses the best candidate to minimize modeling error. As demonstrated by the experimental results based on two industrial examples, the proposed method can achieve higher modeling accuracy and find a more complete feature set than the conventional approach implemented with orthogonal matching pursuit (OMP).

关键词： Predictive modeling Feature Selection Advanced Manufacturing Virtual Metrology linear regression model

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Google timeline accuracy assessment and error prediction

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Forensic Sciences Research 2018年第3期3卷 240-255页

作者： Andrea Macarulla Rodriguez Christian Tiberius Roel van Bree Zeno Geradts Netherlands Forensic Institute Den HaagThe Netherlands Delft University of Technology DelftThe Netherlands

Google Location Timeline,once activated,allows to track devices and save their *** feature might be useful in the future as available data for evidence in *** that,the court would be interested in the reliability of these *** position is presented in the form of a pair of coordinates and a radius,hence the estimated area for tracked device is enclosed by a *** research focuses on the assessment of the accuracy of the locations given by Google Location History Timeline,which variables affect this accuracy and the initial steps to develop a linear multivariate model that can potentially predict the actual error with respect to the true location considering environmental *** determination of the potential influential variables(configuration of mobile device connectivity,speed of movement and environment)was set through a series of experiments in which the true position of the device was recorded with a reference Global Positioning System(GPS)device with a superior order of *** accuracy was assessed measuring the distance between the Google provided position and the de facto one,later referred to as Google *** this Google error distance is less than the radius provided,we define it as a *** configuration that has the largest hit rate is when the mobile device has GPS available,with a 52%*** the use of 3G and 2G connection go with 38%and 33%*** Wi-Fi connection only has a hit rate of 7%.Regarding the means of transport,when the connection is 2G or 3G,the worst results are in Still with a hit rate of 9%and the best in Car with 57%.Regarding the prediction model,the distances and angles from the position of the device to the three nearest cell towers,and the categorical(nonnumerical)variables of Environment and means of transport were taking as input variables in this initial *** evaluate the usability of a model,a model hit is defined when the actual observation is within the 95%

关键词： Forensic science accuracy error Google Timeline linear regression model smartphone location

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Prior Day Effect in Forecasting Daily Natural Gas Flow from Monthly Data

Prior Day Effect in Forecasting Daily Natural Gas Flow from ...

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IEEE-Power-and-Energy-Society General Meeting (PESGM)

作者： Fakoor, Maral Corliss, George F. Brown, Ronald H. Marquette Univ GasDay Project Milwaukee WI 53233 USA

ISBN: (纸本)9781538677032

Many needs exist in the energy industry where measurement is monthly yet daily values are required. The process of disaggregation of low frequency measurement to higher frequency values has been presented in this literature. Also, a novel method that accounts for prior-day weather impacts in the disaggregation process is presented, even though prior-day impacts are not directly recoverable from monthly data. Having initial daily weather and gas flow data, the weather and flow data are aggregated to generate simulated monthly weather and consumption data. linear regression models can be powerful tools for parametrization of monthly/daily consumption models and will enable accurate disaggregation. Two-, three-, four-, and six-parameter linear regression models are built. RMSE and MAPE are used as means for assessing the performance of the proposed approach. Extensive comparisons between the monthly/daily gas consumption forecasts show higher accuracy of the results when the effect of prior-day weather inputs are considered.

关键词： linear regression model Disaggregation Time Series Prior Day Weather Effect Energy Consumption

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Large-area yield estimation method for rice using MODIS data production 5

Large-area yield estimation method for rice using MODIS data...

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5th International Workshop on Earth Observation and Remote Sensing Applications (EORSA)

作者： Jiang, YiXin Dong, Yang He, Binbin Univ Elect Sci & Technol China Sch Resources & Environm Chengdu Sichuan Peoples R China

ISBN: (纸本)9781538666425

This paper takes rice yield in Sichuan province as the research object. By using multi-temporal MODIS's Normalized Difference Vegetation Index (NDVI) data, and analyzing the correlation between these data and the rice yield in the past years, we search for high correlation characteristic parameters of remote sensing, then we use mathematical statistic regression to construct a correlation regression model between remote sensing characteristic parameters and rice yield. After joining these variables in this model, we can estimate the future rice yield information of Sichuan province.

关键词： remote sense NDVI yield estimate linear regression model Statistical Product and Service Solutions

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Empirical Analysis of Urban-rural Income Gap: A Case Tudy in Xinzhou, Shanxi Province

Empirical Analysis of Urban-rural Income Gap: A Case Tudy in...

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2019 3rd International Conference on Informatization in Education,Management and Business(IEMB 2019)

作者： Cuirong Shi School of management Shanghai University

With the rapid development of economy, people's living standards have been significantly improved, but the urban-rural income gap is becoming more and more serious. Xinzhou city, as a city mainly exploiting iron ore in Shanxi Province, has achieved rapid development in recent years, but it still has obvious urban-rural gap. This paper compares urban per capita disposable income and the rural per capita net income in Shanxi Province with that in Xinzhou area from 2005 to 2016. Through the principal component analysis and linear regression model of the factors influencing the urban-rural income gap are conducted. The purpose is to verify the impact of different factors on the urban-rural income gap and put forward relevant measures.

关键词： Urban-rural income gap Principal component analysis linear regression model

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Direction dependence analysis: A framework to test the direction of effects in linear models with an implementation in SPSS

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BEHAVIOR RESEARCH METHODS 2018年第4期50卷 1581-1601页

作者： Wiedermann, Wolfgang Li, Xintong Univ Missouri Stat Measurement & Evaluat Educ Dept Educ Sch & Counseling Psychol Coll Educ 13B Hill Hall Columbia MO 65211 USA

In nonexperimental data, at least three possible explanations exist for the association of two variables x and y: (1) x is the cause of y, (2) y is the cause of x, or (3) an unmeasured confounder is present. Statistical tests that identify which of the three explanatory models fits best would be a useful adjunct to the use of theory alone. The present article introduces one such statistical method, direction dependence analysis (DDA), which assesses the relative plausibility of the three explanatory models on the basis of higher-moment information about the variables (i.e., skewness and kurtosis). DDA involves the evaluation of three properties of the data: (1) the observed distributions of the variables, (2) the residual distributions of the competing models, and (3) the independence properties of the predictors and residuals of the competing models. When the observed variables are nonnormally distributed, we show that DDA components can be used to uniquely identify each explanatory model. Statistical inference methods for model selection are presented, and macros to implement DDA in SPSS are provided. An empirical example is given to illustrate the approach. Conceptual and empirical considerations are discussed for best-practice applications in psychological data, and sample size recommendations based on previous simulation studies are provided.

关键词： linear regression model Direction of effects Direction dependence Observational data Nonnormality

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