Minimax designs for the difference between two estimated responses in a trigonometric regression model

2013 ◽  
Vol 83 (3) ◽  
pp. 909-915 ◽  
Author(s):  
Fatemah Alqallaf ◽  
S. Huda
Keyword(s):  
Regression Model ◽  
The Difference ◽  
Trigonometric Regression

A comparative study of univariate models for container throughput forecasting of major ports in Asia

2021 ◽  
pp. 147509022110236
Author(s):  
Juan Huang ◽  
Ching-Wu Chu ◽  
Hsiu-Li Hsu
Keyword(s):  
Regression Model ◽  
Moving Average ◽  
Forecasting Model ◽  
Short Term ◽  
Autoregressive Integrated Moving Average ◽  
Port Authorities ◽  
Grey Forecasting ◽  
Grey Forecasting Model ◽  
Short Term Forecasting ◽  
Trigonometric Regression

This study aims to make comparisons on different univariate forecasting methods and provides a more accurate short-term forecasting model on the container throughput for rendering a reference to relevant authorities. We collected monthly data regarding container throughput volumes for three major ports in Asia, Shanghai, Singapore, and Busan Ports. Six different univariate methods, including the grey forecasting model, the hybrid grey forecasting model, the multiplicative decomposition model, the trigonometric regression model, the regression model with seasonal dummy variables, and the seasonal autoregressive integrated moving average (SARIMA) model, were used. We found that the hybrid grey forecasting model outperforms the other univariate models. This study’s findings can provide a more accurate short-term forecasting model for container throughput to create a reference for port authorities.

scholarly journals PM2.5 Concentration Estimation Based on Image Processing Schemes and Simple Linear Regression

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2423 ◽  
Author(s):  
Jiun-Jian Liaw ◽  
Yung-Fa Huang ◽  
Cheng-Hsiung Hsieh ◽  
Dung-Ching Lin ◽  
Chin-Hsiang Luo
Keyword(s):  
Image Processing ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Simple Linear Regression ◽  
Simple Linear Regression Model ◽  
Concentration Estimation ◽  
Single Feature ◽  
The Difference ◽  
Pm2.5 Concentration

Fine aerosols with a diameter of less than 2.5 microns (PM2.5) have a significant negative impact on human health. However, their measurement devices or instruments are usually expensive and complicated operations are required, so a simple and effective way for measuring the PM2.5 concentration is needed. To relieve this problem, this paper attempts to provide an easy alternative approach to PM2.5 concentration estimation. The proposed approach is based on image processing schemes and a simple linear regression model. It uses images with a high and low PM2.5 concentration to obtain the difference between these images. The difference is applied to find the region with the greatest impact. The approach is described in two stages. First, a series of image processing schemes are employed to automatically select the region of interest (RoI) for PM2.5 concentration estimation. Through the selected RoI, a single feature is obtained. Second, by employing the single feature, a simple linear regression model is used and applied to PM2.5 concentration estimation. The proposed approach is verified by the real-world open data released by Taiwan’s government. The proposed scheme is not expected to replace component analysis using physical or chemical techniques. We have tried to provide a cheaper and easier way to conduct PM2.5 estimation with an acceptable performance more efficiently. To achieve this, further work will be conducted and is summarized at the end of this paper.

scholarly journals The Relationship Between Tactical Positioning and the Race Outcome in 800-M Running at the 2016 Olympic Games and 2017 IAAF World Championship

2020 ◽  
Vol 71 (1) ◽  
pp. 299-305
Author(s):  
Fernando González-Mohíno ◽  
Jesús Santos del Cerro ◽  
Andrew Renfree ◽  
Inmaculada Yustres ◽  
José Mª González-Ravé
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Olympic Games ◽  
Area Under The Curve ◽  
Predictive Ability ◽  
Significance Test ◽  
Final Position ◽  
The Difference ◽  
The Relationship

AbstractThe purpose of this analysis was to quantify the probability of achieving a top-3 finishing position during 800-m races at a global championship, based on dispersion of the runners during the first and second laps and the difference in split times between laps. Overall race times, intermediate and finishing positions and 400 m split times were obtained for 43 races over 800 m (21 men’s and 22 women’s) comprising 334 individual performances, 128 of which resulted in higher positions (top-3) and 206 the remaining positions. Intermediate and final positions along with times, the dispersion of the runners during the intermediate and final splits (SS1 and SS2), as well as differences between the two split times (Dsplits) were calculated. A logistic regression model was created to determine the influence of these factors in achieving a top-3 position. The final position was most strongly associated with SS2, but also with SS1 and Dsplits. The Global Significance Test showed that the model was significant (p < 0.001) with a predictive ability of 91.08% and an area under the curve coefficient of 0.9598. The values of sensitivity and specificity were 96.8% and 82.5%, respectively. The model demonstrated that SS1, SS2 and Dplits explained the finishing position in the 800-m event in global championships.

scholarly journals PREDICTION OF THE MOISTURE CONTENT OF EASTERN CANADIAN CORN USING MEASUREMENTS OF CAPACITANCE AND TEST WEIGHT

1989 ◽  
Vol 69 (1) ◽  
pp. 109-119
Author(s):  
J. C. BABB ◽  
C. J. DEMPSTER ◽  
R. J. WALLIS
Keyword(s):  
Zea Mays ◽  
Moisture Content ◽  
Regression Model ◽  
Zea Mays L ◽  
Test Weight ◽  
Coefficient Of Determination ◽  
Statistical Regression ◽  
Conversion Table ◽  
Grain Moisture ◽  
The Difference

A statistical regression model for rapid prediction of moisture content based on measurements of dielectric capacitance and test weight was developed for eastern Canadian corn (Zea mays L.). For 336 samples of the 1986 crop, dielectric readings were determined with a Model 919 grain moisture meter, test weight values with an Ohaus half-litre measure and moisture content values by a single-stage air-oven procedure. The regression model, which incorporates linear terms for dielectric reading and test weight plus an interaction term which is a product of the two, is an excellent predictor of corn moisture as indicated by analysis of the residuals and by the high value of the coefficient of determination (R2 = 0.95) and low value of the standard error of estimate (SEE = 0.85). Although the relationship between moisture content and dielectric reading for Ontario samples differed from that for Quebec samples, the proposed regression model helped to compensate for the difference. This model was also effective in predicting moisture content for 365 samples of 1987-crop eastern Canadian corn. As well, it yielded a better fit to 1986–1987 crop data than did the dielectric-based regression model used in CGC Corn Moisture Conversion Table No. 9.Key words: Zea Mays L., predicting corn moisture, Model 919 meter, capacitance, test weight, dielectric

scholarly journals A multivariate analysis of prognostic factors in chronic myeloid leukemia

Blood ◽  
1982 ◽  
Vol 60 (6) ◽  
pp. 1298-1304 ◽  
Author(s):  
F Cervantes ◽  
C Rozman
Keyword(s):  
Multivariate Analysis ◽  
Chronic Myeloid Leukemia ◽  
Regression Model ◽  
Myeloid Leukemia ◽  
Poor Prognosis ◽  
Clinical Staging ◽  
Laboratory Findings ◽  
Two Factors ◽  
Cox's Regression ◽  
The Difference

Abstract The prognostic value of different clinical and laboratory findings at diagnosis of chronic myeloid leukemia (CML) was analyzed in a series of 121 cytogenetically studied patients. From the univariate and multivariate analysis of the whole series it was apparent that the minority of Ph1-negative patients (11.5%) could be considered as a poor prognosis group. The analysis was then restricted to the Ph1-positive patients. From a multivariate survival analysis (Cox's regression model) of the latter group the following poor prognosis factors emerged: splenomegaly, hepatomegaly, presence of erythroid precursors in peripheral blood, and bone marrow myeloblasts over 5%. From the contribution of each one of these factors to the regression model, a clinical staging of Ph1-positive CML was derived: stage I (low risk, 32% of patients), including patients with one or no factors; stage II (intermediate risk, 38%), including cases with two factors; and stage III (high risk, 30%), including patients with three or four factors. The difference in survival of the patients at different stages was highly significant (p less than 0.001).

scholarly journals A multivariate analysis of prognostic factors in chronic myeloid leukemia

Blood ◽  
1982 ◽  
Vol 60 (6) ◽  
pp. 1298-1304 ◽  
Author(s):  
F Cervantes ◽  
C Rozman
Keyword(s):  
Multivariate Analysis ◽  
Chronic Myeloid Leukemia ◽  
Regression Model ◽  
Myeloid Leukemia ◽  
Poor Prognosis ◽  
Clinical Staging ◽  
Laboratory Findings ◽  
Two Factors ◽  
Cox's Regression ◽  
The Difference

The prognostic value of different clinical and laboratory findings at diagnosis of chronic myeloid leukemia (CML) was analyzed in a series of 121 cytogenetically studied patients. From the univariate and multivariate analysis of the whole series it was apparent that the minority of Ph1-negative patients (11.5%) could be considered as a poor prognosis group. The analysis was then restricted to the Ph1-positive patients. From a multivariate survival analysis (Cox's regression model) of the latter group the following poor prognosis factors emerged: splenomegaly, hepatomegaly, presence of erythroid precursors in peripheral blood, and bone marrow myeloblasts over 5%. From the contribution of each one of these factors to the regression model, a clinical staging of Ph1-positive CML was derived: stage I (low risk, 32% of patients), including patients with one or no factors; stage II (intermediate risk, 38%), including cases with two factors; and stage III (high risk, 30%), including patients with three or four factors. The difference in survival of the patients at different stages was highly significant (p less than 0.001).

scholarly journals Prediction and evaluation of health state for power battery based on Ridge linear regression model

2021 ◽  
Vol 104 (4) ◽  
pp. 003685042110590
Author(s):  
Bixiong Huang ◽  
Haiyu Liao ◽  
Yiquan Wang ◽  
Xintian Liu ◽  
Xiao Yan
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Health State ◽  
Clustering Model ◽  
Power Battery ◽  
The Difference ◽  
Low Dimensional ◽  
Cycle Efficiency ◽  
Cell Data

The state of health (SOH) of power battery reflects the difference between the current performance of the battery and the time it left the factory. Accurate prediction of it is the key to improving battery cycle efficiency. This paper studies the application of data-driven algorithms in power battery health estimation. Firstly, Using the data of actual operating vehicles which are monitoring in the data platform as the research objects. The charging event segmentation algorithm is designed for the full amount of data, and the K-means clustering model is used to extract slow charging events. Secondly, feature engineering is performed on the data, including the use of Pearson and Spearman coefficients analysis for numerical features, the use of one-hot encoding for category features to determine the final input features of SOH model. Eventually, using the Ridge linear regression model to predict the health status of the power battery. The research shows that the MAE is less than 5%, which meets the needs of practical use. In addition, this paper comparing Ridge with three other models named Linear Regression, Lasso, and Elastic Net. The result showed that the linear regression model with L2 regularization is more applicable in low-dimensional feature application scenarios without cell data in prediction of SOH.

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