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Parameterizing sea surface temperature cooling induced by tropical cyclones using a multivariate linear regression model

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Acta Oceanologica Sinica 2018年第1期37卷 1-10页

作者： WEI Jun LIU Xin JIANG Guoqing Laboratory for Climate and Ocean-Atmosphere Studies Department of Atmospheric and Oceanic Sciences Peking University State Key Laboratory of Severe Weather Chinese Academy of Meteorological Sciences

Combining a linear regression and a temperature budget formula, a multivariate regression model is proposed to parameterize and estimate sea surface temperature（SST） cooling induced by tropical cyclones（TCs）. Three major dynamic and thermodynamic processes governing the TC-induced SST cooling（SSTC）, vertical mixing, upwelling and heat flux, are parameterized empirically using a combination of multiple atmospheric and oceanic variables：sea surface height（SSH）, wind speed, wind curl, TC translation speed and surface net heat flux. The regression model fits reasonably well with 10-year statistical observations/reanalysis data obtained from 100 selected TCs in the northwestern Pacific during 2001–2010, with an averaged fitting error of 0.07 and a mean absolute error of 0.72°C between diagnostic and observed SST cooling. The results reveal that the vertical mixing is overall the pre dominant process producing ocean SST cooling, accounting for 55% of the total cooling. The upwelling accounts for 18% of the total cooling and its maximum occurs near the TC center, associated with TC-induced Ekman pumping. The surface heat flux accounts for 26% of the total cooling, and its contribution increases towards the tropics and the continental shelf. The ocean thermal structures, represented by the SSH in the regression model,plays an important role in modulating the SST cooling pattern. The concept of the regression model can be applicable in TC weather prediction models to improve SST parameterization schemes.

关键词： tropical cyclones SST cooling regression model parameterization

EMPIRICAL BAYES TEST OF regression COEFFICIENT IN A MULTIPLE LINEAR regression model

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Acta Mathematicae Applicatae Sinica 1990年第3期6卷 251-262页

作者：韦来生 University of Science & Technology of Chinaecently R. S. Singh has studied the empirical Bayes (EB) estimation in a multiple linearregression model. In this paper we consider the EB test of regression coefficient β for this model. Wework out the EB test decision rule by using kernel estimation of multivariate density function and itsfirst order partial derivatives. We obtain its asymptotically optimal (a. o.) property under thecondition E||β||_1<∞. It is shown that tbe convergence rates of this EB test decision rule areO(n~(-(r-1)λ/p+r)) under the condition E||β||~(pr/2-λ)<∞. where an integer r> 0

Recently R. S. Singh has studied the empirical Bayes (EB) estimation in a multiple linearregression model. In this paper we consider the EB test of regression coefficient β for this model. Wework out the EB test decision rule by using kernel estimation of multivariate density function and itsfirst order partial derivatives. We obtain its asymptotically optimal (a. o.) property under thecondition E||β||_1 0

关键词： Linear regression regression model Density Function regression Coefficient Partial Derivative

DETECTION OF THE JUMP POINTS OF A HETEROSCEDASTIC regression model BY WAVELETS

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Acta Mathematicae Applicatae Sinica 2000年第4期16卷 420-429页

作者：赵延孟李元 Department of Mathematical Education Shenzhen University Shenzhen China Department of Mathematics Guangzhou University Guangzhou China

Wavelets are applied to detect the jumps in a heteroscedastic regression model. By the empirical wavelet coefficients of the conditional mean and the conditional variance of the time series under consideration, it is shown that the wavelet coefficients exhibit high peaks near the jump points, based on which a procedure is developed to identify and then to locate the jumps. All estimators are proved to be consistent.

关键词： Heteroscedasticity regression model jumps wavel0

Step-by-Step Building of a Four Dimensional Fatigue Compatible regression model including Frequencies

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Open Journal of Statistics 2021年第6期11卷 1072-1096页

作者： Enrique Castillo Alfonso Fernández-Canteli Sergio Blasón Golta Khatibi Bernhard Czerny Mohammad Zareghomsheh Spanish Royal Academy of Engineering Madrid Spain Spanish Royal Academy of Sciences Madrid Spain Department of Construction and Engineering Manufacturing Polytechnic School of Gijón University of Oviedo Campus de Viesques Asturias Spain Bundesanstalt für Materialforschung und Prüfung (BAM) Berlin Germany Department 9-Component Safety Division 9.4 Weld Mechanics Berlin Germany Christian Doppler Laboratory for Lifetime and Reliability of Interfaces in Complex Multi-Material Electronics Chemical Technologies and Analytics Vienna Austria

The purpose of this research is to develop a model, with emphasis on compatibility conditions and model building, valid for high cycle fatigue design components such as wind turbines, automobiles, high speed railways and aeronautical material. In this work, we have added the frequency as one more variable to an existing fatigue model that already includes maximum stress, stress ratio and lifetime. As a result, a model and estimation method has been proposed and a random variable V has been identified, which, allows the accumulated damage and the probability of failure to be assessed for any load history in terms of stress levels, stress ranges and frequencies. Finally, the model is validated using a large set of real experimental data.

关键词： Compatibility Conditions Cumulative Damage Frequency Effect Functional Equations regression model

Effects of Multicollinearity on Type I Error of Some Methods of Detecting Heteroscedasticity in Linear regression model

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Open Journal of Statistics 2020年第4期10卷 664-677页

作者： Olusegun Olatayo Alabi Kayode Ayinde Omowumi Esther Babalola Hamidu Abimbola Bello Edward Charles Okon Department of Statistics Federal University of Technology Akure Nigeria

Heteroscedasticity and multicollinearity are serious problems when they exist in econometrics data. These problems exist as a result of violating the assumptions of equal variance between the error terms and that of independence between the explanatory variables of the model. With these assumption violations, Ordinary Least Square Estimator (OLS) will not give best linear unbiased, efficient and consistent estimator. In practice, there are several structures of heteroscedasticity and several methods of heteroscedasticity detection. For better estimation result, best heteroscedasticity detection methods must be determined for any structure of heteroscedasticity in the presence of multicollinearity between the explanatory variables of the model. In this paper we examine the effects of multicollinearity on type I error rates of some methods of heteroscedasticity detection in linear regression model in other to determine the best method of heteroscedasticity detection to use when both problems exist in the model. Nine heteroscedasticity detection methods were considered with seven heteroscedasticity structures. Simulation study was done via a Monte Carlo experiment on a multiple linear regression model with 3 explanatory variables. This experiment was conducted 1000 times with linear model parameters of β0 = 4 , β1 = 0.4 , β2= 1.5 and <

关键词： regression model Heteroscedasticity Methods Heteroscedasticity Structures Multicollinearity Monte Carlo Study Significance Levels Type I Error Rates

Estimation of the accumulation rates and health risks of heavy metals in residential soils of three metropolitan cities in China

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Journal of Environmental Sciences 2022年第5期34卷 149-161页

作者： Chi Peng Kai Zhang Meie Wang Xinxing Wan Weiping Chen Institute of Environmental Engineering School of Metallurgy and EnvironmentCentral South UniversityChangsha 410083China State key laboratory of urban and regional ecology Research center for eco-environmental sciencesChinese academy of sciencesBeijing 100085China Third Xiangya Hospital Central South UniversityChangsha 410013China

Heavy metal concentrations in urban soils are likely to increase over time because of contin-uous urbanization and heavy metal emissions.To estimate the accumulation rates of heavy metals in urban soils,we collected soil samples from residential areas with different build-ing ages in the metropolitan cities of Shanghai,Shenzhen,and Beijing,China.Heavy metal concentrations in the soils varied among the cities and were primarily affected by soil parent material and the intensity of anthropogenic sources.regression analyses revealed that the accumulation rates of Cd and Cu in the soils ranged from 0.0034 to 0.0039 mg/(kg·year)and 0.343 to 0.391 mg/(kg·year),respectively,and were similar across the three cities,while ac-cumulation rates of Zn and Pb in Shanghai were higher than those in Shenzhen and Beijing.The higher accumulation rates of Zn and Pb in Shanghai can be explained by differences in city history and industrial structures among the cities.Residential soils with high health risks posed by the heavy metals were mostly collected from old towns of Shanghai because of high Pb content in the areas.Although recent urbanization resulted in elevated concen-trations of Cd,Cu,Zn,and Pb in the residential soils,the effect on the total health risks of residents exposed to the soils was negligible.

关键词： Urbanization Urban soil Health risk assessment Building age regression model Source analysis

Reinforcement Learning Method Based on Semi-parametric regression model

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Reinforcement Learning Method Based on Semi-parametric Regre...

Photovoltaic power regression model based on Gauss Boltzmann machine

2010 Chinese Control and Decision Conference

作者： Yuhu Cheng,Xuesong Wang,Xilan Tian School of Information and Electrical Engineering,China University of Mining and Technology,Xuzhou,Jiangsu 221116 China

＜正＞In order to make full use of the advantages of both parametric and non-parametric models simultaneously,a kind of semi-parametric support vector machine（SVM） was proposed by combining a non-parametric SVM model and a parametric linear basis function model.The semi-parametric SVM was used to estimate the Q values of continuous-state-discontinuous-action pairs in an on-line manner so as to generalize a standard Q learning method to continuous state spaces.Simulation results concerning the balancing control problem of an inverted pendulum show that the proposed Q learning method has good adaptability for changes of system parameters and initial states,which provides a new approach to solve the generalization problem of continuous space of reinforcement learning.

关键词： Semi-parametric regression model Reinforcement learning Support vector machine

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Photovoltaic power regression model based on Gauss Boltzmann...

The Odd Log-Logistic Weibull-G Family of Distributions with regression and Financial Risk models

第三十九届中国控制会议

作者： Lu Zhiying Wang Zehan Ren Yimo School of Electrical and Information Engineering Tianjin University

Improving the short-term power forecast of photovoltaic panels is a key issue for solar photovoltaic power generation to effectively merge from distributed power sources into the current large-scale power grid, and has great significance for improving the utilization of solar photovoltaic power generation. The restricted Boltzmann machine is an autoencoder that can be used to build deep learning models, which can reconstruct the input data. In this paper, a restricted Boltzmann machine is introduced on the basis of a linear regression model, and a predictive regression model for photovoltaic power generation is constructed. The model first reconstructs the original data using a restricted Boltzmann machine, then builds a linear regression model on the reconstructed data, and finally applies it to the photovoltaic panel output power data provided by GEFCom2014. Experiments show that the restricted Boltzmann machine can greatly enhance the regression prediction ability of general regression models.

关键词： photovoltaic power prediction Distributed grid Restricted Boltzmann Machine deep learning automatic encoder regression model

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Journal of the Operations Research Society of China 2022年第1期10卷 133-158页

作者： Mahdi Rasekhi Emrah Altun Morad Alizadeh Haitham M.Yousof Department of Statistics Malayer UniversityMalayerIran Department of Mathematics Bartin UniversityBartinTurkey Department of Statistics Persian Gulf UniversityBushehrIran Department of Statistics Mathematics and InsuranceBenha UniversityBenhaEgypt

A new generalization of the Weibull-G family is proposed with two extra shape parameters.The mathematical properties are derived in great detail.Using the Weibull and normal distributions as baseline distributions,two models are introduced.The first model is a location-scale regression model based on a new extension of the Weibull distribution.The second model is a new two-step financial risk model to forecast the daily value at risk.The flexibility and applicability of the proposed models are investigated by means of five real data sets on the lifetime and financial returns.Empirical findings of the study show that proposed models work well and produce better results than other well-known models for financial risk modeling and censored lifetime data analysis.

关键词： Odd log-logistic-G family Weibull-G family regression model Value at risk Simulation Maximum likelihood Financial risk modeling