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COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2019年第3期48卷 666-674页

作者： Yuzbasi, B. Arashi, M. Inonu Univ Dept Econometr Malatya Turkey Shahrood Univ Technol Dept Stat Shahrud Iran

The least absolute shrinkage and selection operator (LASSO) is a prominent estimator which selects significant (under some sense) features and kills insignificant ones. Indeed the LASSO shrinks features larger than a noise level to zero. In this article, we force LASSO to be shrunken more by proposing a Stein-type shrinkage estimator emanating from the LASSO, namely the Stein-type LASSO. The newly proposed estimator proposes good performance in risk sense numerically. Variants of this estimator have smaller relative MSE and prediction error, compared to the LASSO, in the analysis of prostate cancer dataset.

关键词： Double shrinking LASSO linear regression model MSE Prediction error Stein-type shrinkage estimator

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Whale-crow optimization (WCO)-based Optimal regression model for Software Cost Estimation

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SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES 2019年第4期44卷 1-15页

作者： Ahmad, Sumera W. Bamnote, G. R. Prof Ram Meghe Inst Technol & Res Dept Comp Sci & Engn Badnera 444701 Amravati India

Software Cost Estimation (SCE) is the emerging concern of the software companies during the development phase of the software, as it requires effort and cost factors for modelling the software. These factors are modelled using the Artificial Intelligence models, which seem to be less accurate and non-reliable by increasing the risk factor of the software projects. Thus, for estimating the software cost, meta-heuristics are employed. This paper proposes an algorithm, termed as whale-crow optimization (WCO) algorithm, which is the integration of the whale optimization algorithm (WOA) and the crow search algorithm (CSA). The main function of the WCO algorithm is to determine the Optimal regression coefficients for the regression models, such as the linear regression model and the Kernel Logistic regression model, to develop an Optimal regression model to estimate the software cost. The experimentation is carried out using four datasets taken from the Promise software engineering repository to perform effective performance analysis. Analysis is carried out regarding the mean magnitude of relative error (MMRE) that proves that the proposed method of SCE is effective, attaining the average MMRE at a rate of 0.2442 for the proposed linear regression model and 0.2692 for the proposed Kernel regression model.

关键词： Kernel Logistics regression model linear regression model WOA CSA Software Cost Estimation

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Design Method Using Statistical models for Miniature Left Ventricular Assist Device Hydraulics

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ANNALS OF BIOMEDICAL ENGINEERING 2019年第1期47卷 126-137页

作者： Smith, P. Alex Wang, Yaxin Bieritz, Shelby A. Sampaio, Luiz C. Cohn, William E. Metcalfe, Ralph W. Frazier, O. H. Texas Heart Inst Cardiovasc Surg Res Lab 6770 Bertner Ave Houston TX 77030 USA Univ Houston Dept Mech Engn Houston TX USA Rice Univ Dept Bioengn Houston TX USA Johnson & Johnson Ctr Device Innovat Houston TX USA

Left ventricular assist devices (LVADs) are increasingly used to treat heart failure patients. These devices' impeller blades and diffuser vanes must be designed for hydraulic performance and hemocompatibility. The traditional design method, applying mean-line theory, is not applicable to the design of small-scale pumps such as miniature LVADs. Furthermore, iterative experimental testing to determine how each geometric variable affects hydraulic performance is time and labor intensive. In this study, we tested a design method wherein empirical hydraulic results are used to establish a statistical model to predict pump hydraulic performance. This method was used to design an intra-atrial blood pump. Five geometric variables were chosen, and each was assigned two values to define the variable space. The experimental results were then analyzed with both correlation analysis and linear regression modeling. To validate the linear regression models, 2 test pumps were designed: mean value of each geometric variable within the boundaries, and random value of each geometric variable within the boundaries. The statistical model accurately predicted the hydraulic performance of both pump designs within the boundary space. This method could be expanded to include more geometric variables and broader boundary conditions, thus accelerating the design process for miniature LVADs.

关键词： Left ventricular assist device Blood pump Hydraulic performance Statistical modeling Hydraulic torque Mechanical circulatory support linear regression model Correlation analysis

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Multiple Use Confidence Intervals for a Univariate Statistical Calibration

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MEASUREMENT SCIENCE REVIEW 2019年第6期19卷 264-270页

作者： Chvostekova, Martina Slovak Acad Sci Inst Measurement Sci Dubravska Cesta 9 Bratislava 84104 Slovakia

The statistical calibration problem treated here consists of constructing the interval estimates for future unobserved values of a univariate explanatory variable corresponding to an unlimited number of future observations of a univariate response variable. An interval estimate is to be computed for a value x of an explanatory variable after observing a response Y-x by using the same calibration data from a single calibration experiment, and it is called the multiple use confidence interval. It is assumed that the normally distributed response variable Y-x is related to the explanatory variable x through a linear regression model, a polynomial regression is probably the most frequently used model in industrial applications. Construction of multiple use confidence intervals (MUCI's) by inverting the tolerance band for a linear regression has been considered by many authors, but the resultant MUCI's are conservative. A new method for determining MUCI's is suggested straightforward from their marginal property assuming a distribution of the explanatory variable. Using simulations, we show that the suggested MUCI's satisfy the coverage probability requirements of MUCI's quite well and they are narrower than previously published. The practical implementation of the proposed MUCI's is illustrated in detail on an example.

关键词： Statistical calibration linear regression model tolerance interval multiple use confidence interval

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Analysis of bioenergy by using linear regression

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SN APPLIED SCIENCES 2019年第10期1卷 1225页

作者： Iqbal, Muhammad Shahid Khan, Tamoor Kausar, Samina Bin, Luo Anhui Univ Sch Comp Sci & Technol Hefei Anhui Peoples R China Air Univ Dept Comp Sci Islamabad Pakistan Southwestern Univ Finance & Econ Sch Econ Informat Engn Chengdu Sichuan Peoples R China Shanghai Univ Sch Comp Engn & Sci Shanghai 200444 Peoples R China

Prediction of bioenergy production is challenging in the machine learning field. In this article, bioenergy production data from different countries (Russian Fed, Turkey, Armenia, Azerbaijan and total Eurasia) were analyzed to predict future bioenergy production in these countries using the linear regression. Data on output production of bioenergy in megawatt were obtained from the International Renewable Energy Agency, and (MW). We implemented the linear regression method redundant to predict the future production of bioenergy data and attained good accuracy. The aforementioned method is applicable to developing countries and can be used to predict bioenergy production and formulate new policies that are adapted to an increasing population in a particular country. Our results (average accuracy is 49.61%) revealed that bioenergy production levels in developing countries are not sufficient to cater for bioenergy needs of fast growing populations. Thus we recommend that governments should formulate adequate policies aimed at improving bioenergy production.

关键词： Bioenergy Energy production linear regression model Yearly production International renewable energy agency

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Granger Causality Analysis Based on Quantized Minimum Error Entropy Criterion

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IEEE SIGNAL PROCESSING LETTERS 2019年第2期26卷 347-351页

作者： Chen, Badong Ma, Rongjin Yu, Siyu Du, Shaoyi Qin, Jing Xi An Jiao Tong Univ Sch Elect & Informat Engn Inst Artificial Intelligence & Robot Xian 710049 Shaanxi Peoples R China Hong Kong Polytech Univ Sch Nursing Ctr Smart Hlth Hong Kong Peoples R China

linear regression model (LRM) based on mean square error (MSE) criterion is widely used in Granger causality analysis (GCA), which is the most commonly used method to detect the causality between a pair of time series. However, when signals are seriously contaminated by non-Gaussian noises, the LRM coefficients will be inaccurately identified. This may cause the GCA to detect a wrong causal relationship. Minimum error entropy (MEE) criterion can be used to replace the MSE criterion to deal with the non-Gaussian noises. But its calculation requires a double summation operation, which brings computational bottlenecks to GCA especially when sizes of the signals are large. To address the aforementioned problems, in this letter, we propose a new method called GCA based on the quantized MEE (QMEE) criterion (GCA-QMEE), in which the QMEE criterion is applied to identify the LRM coefficients and the quantized error entropy is used to calculate the causality indexes. Compared with the traditional GCA, the proposed GCA-QMEE not only makes the results more discriminative, but also more robust. Its computational complexity is also not high because of the quantization operation. Illustrative examples on synthetic and EEG datasets are provided to verify the desirable performance and the availability of the GCA-QMEE.

关键词： Granger causality analysis mean square error criterion minimum error entropy criterion quantized minimum error entropy criterion linear regression model

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Evaluation of four modelling approaches to estimate nitrous oxide emissions in China's cropland

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SCIENCE OF THE TOTAL ENVIRONMENT 2019年 652卷 1279-1289页

作者： Yue, Qian Cheng, Kun Ogle, Stephen Hillier, Jonathan Smith, Pete Abdalla, Mohamed Ledo, Alicia Sun, Jianfei Pan, Genxing Nanjing Agr Univ Inst Resource Ecosyst & Environm Agr 1 Weigang Nanjing 210095 Jiangsu Peoples R China Colorado State Univ Nat Resource Ecol Lab Ft Collins CO 80523 USA Colorado State Univ Dept Ecosyst Sci & Sustainabil Ft Collins CO 80523 USA Royal Dick Sch Vet Studies Global Acad Agr & Food Secur Easter Bush Campus Roslin EH25 9RG Midlothian Scotland Roslin Inst Easter Bush Campus Roslin EH25 9RG Midlothian Scotland Univ Aberdeen Inst Biol & Environm Sci Sch Biol Sci Aberdeen AB24 3UU Scotland

Process-basedmodels are useful tools to integrate the effects of detailed agricultural practices, soil characteristics, mass balance, and climate change on soil N2O emissions from soil - plant ecosystems, whereas static, seasonal or annual models often exist to estimate cumulative N2O emissions under data-limited conditions. A studywas carried out to compare the capability of four models to estimate seasonal cumulative N2O fluxes from 419 field measurements representing 65 studies across China's croplands. The models were 1) the DAYCENT model, 2) the DNDC model, 3) the linear regression model (YLRM) of Yue et al. (2018), and 4) IPCC Tier 1 emission factors. The DAYCENT and DNDC models estimated crop yields with R-2 values of 0.60 and 0.66 respectively, but both models showed significant underestimation for all measurements. The estimated seasonal N2O emissions with R-2 of 0.31, 0.30, 0.21 and 0.17 for DAYCENT, DNDC, YLRM, and IPCC, respectively. Based on RMSE, modelling efficiency and bias analysis, YLRM performed well on N2O emission prediction under no fertilization though bias still existed, while IPCC performed well for cotton and rapeseed and DNDC for soybean. The DAYCENT model accurately predicted the emissions with no bias across other crop and fertilization types whereas the DNDC model underestimated seasonal N2O emissions by 0.42 kg N2O N ha(-1) for all observed values. model evaluation indicated that the DAYCENT and DNDC models simulated temporal patterns of daily N2O emissions effectively, but both models had difficulty in simulating the timing of the N2O fluxes following some events such as fertilization and water regime. According to this evaluation, algorithms for crop production and N2O emission should be improved to increase the accuracy in the prediction of unfertilized fields both for DAYCENT and DNDC. The effects of crop types and management modes such as fertilizations should also be further refined for YLRM. (C) 2018 Elsevier B.V. All rights res

关键词： Nitrous oxide model simulation Cropland DAYCENT DNDC linear regression model

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Effect of harvesting altitude, fermentation time and roasting degree on the aroma released by coffee powder monitored by proton transfer reaction mass spectrometry

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EUROPEAN FOOD RESEARCH AND TECHNOLOGY 2019年第7期245卷 1499-1506页

作者： Bodner, Martina Morozova, Ksenia Kruathongsri, Prapaijit Thakeow, Prodpran Scampicchio, Matteo Free Univ Bolzano Fac Sci & Technol Piazza Univ 1 I-39100 Bolzano Italy Chiang Mai Univ Fac Agroind Dept Prod Dev Technol Chiang Mai 50100 Thailand

This study investigates the effect of harvesting altitude, fermentation time and roasting degree on the volatile profile released by coffee (Coffea arabica, variety Catimor) powder. The analysis of the volatile organic compounds was performed by proton transfer reaction mass spectrometer (PTR-MS). The experimental design consisted of three factors: altitude at two levels (900m and 1500m, milder and harsher climate, respectively), four different fermentation times (0, 24, 48 and 72h) and roasting at three levels (light, medium and dark). Analysis of the released volatile organic compounds (VOCs) revealed that the intensity of m/z 45 and 59 dramatically decreases at a higher harvesting altitude. Longer fermentation times induce a decrease of m/z 61 and 75, probably related to the activity of the isocitrate lyase. Finally, a prolonged roasting degree induces a rise of the m/z 61, 75, 81 and 97. The total count per second measured by PTR-MS was used as an index of overall coffee aroma intensity to build a linear regression model. The model was used to predict the total VOCs intensity on the basis of the altitude, fermentation time and roasting level. The validation of the model (adjusted R-2=0.91 and normalized relative standard deviation in prediction of 7%) reveals that, although the degree of roasting clearly affects the resulting aroma intensity, altitude and fermentation time also play a role in the formation of the volatile profile. Moreover, a significant interaction was observed between the altitude where the bean were grown and the roasting level. These results support the hypothesis that the type of process of the seeds has a strong impact on the resulting coffee's quality. Furthermore, PTR-MS coupled with linear regression models is a powerful tool for the quality control of coffee process.

关键词： PTR-MS Coffee powder Aroma VOCs linear regression model

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Statistical regression models for WCET estimation

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QUALITY TECHNOLOGY AND QUANTITATIVE MANAGEMENT 2019年第3期16卷 318-329页

作者： Zhang, Qiong Huangfu, Yijie Zhang, Wei Virginia Commonwealth Univ Dept Stat Sci & Operat Res Richmond VA 23284 USA Virginia Commonwealth Univ Dept Elect & Comp Engn Richmond VA 23284 USA

1. Problem Statement. Worst Case Execution Time (WCET) is the upper bound of the execution time of a real-time task running on a particular hardware platform. The estimation of WCET is of significant importance to the correctness of schedulability analysis and thus the reliability of real-time applications. 2. Approach. Traditionally, WCET is often estimated based on static analysis techniques. However, advanced architectural features make the static analysis of WCET extremely complicated. As a result, measurement-based approaches become popular in practice. Despite its popularity, this type of approach would require running a task with a large number of input settings to obtain safe and accurate WCET estimates. To solve this challenge, we develop statistical regression models on a set of benchmark tasks to connect task execution time with some selected features. Our models can be used to estimate the WCET of new tasks without running a large number of sample executions. 3. Results. Our fitted regression models show that the execution times are highly related to the selected feature factors 'Load' and 'Missing Rate'. Through a comparison study, we evaluate different measurement-based approaches from both safe and accurate perspectives. The comparison results are summarized to give general guidelines in searching good measurement-based WCET estimation approaches.

关键词： Worst case execution time linear regression model quantile regression model extreme value theory

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Peptidomic analysis characterising proteolysis in thaw-aging of beef short plate

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FOOD CHEMISTRY: MOLECULAR SCIENCES 2021年 3卷 100051页

作者： Kominami, Yuri Hayashi, Tatsuya Tokihiro, Tetsuji Ushio, Hideki Univ Tokyo Grad Sch Agr & Life Sci Dept Aquat Biosci Bunkyo Ku 1-1-1 Yayoi Tokyo 113865 Japan Hokkaido Univ Grad Sch Informat Sci & Technol Div Comp Sci & Informat Technol Kita Ku Kita 14Nishi 9 Sapporo Hokkaido 0600814 Japan Univ Tokyo Grad Sch Math Sci Dept Math Sci Meguro Ku 3-8-1 Komaba Tokyo 1538914 Japan

Recent studies have suggested that thaw-aging can improve sensory attributes of freeze-thawed meat. Acceleration of proteolysis is expected to promote tenderisation and improve taste;however, the details of protein degradation, including substrate proteins and cleavage sites, remain unclear. Here, we report a time course overview of the peptidome of beef short plates during thaw-aging. The accelerated degradation of key proteins for meat tenderisation, such as troponin T and desmin, was confirmed. Additionally, 11 cleavage sites in troponin T related to taste-active peptide generation were identified. Terminome analysis showed that the contribution of each protease varies depending on the substrate proteins and the thaw-aging period. Based on our results;proteases, not only calpains, but also others contributed to the degradation of myofibrillar proteins. The techniques employed indicate that meat proteolysis during thaw-aging is not constant but dynamic.

关键词： Thaw-aging Meat proteolysis Peptidomics Terminome analysis linear regression model

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