Predicting Glaucomatous Progression with Piecewise Regression Model from Heterogeneous Medical Data

2016 ◽  
Author(s):  
Kyosuke Tomoda ◽  
Kai Morino ◽  
Hiroshi Murata ◽  
Ryo Asaoka ◽  
Kenji Yamanishi
Keyword(s):  
Regression Model ◽  
Medical Data ◽  
Piecewise Regression

On a new piecewise regression model with cure rate: Diagnostics and application to medical data

2021 ◽  
Author(s):  
Yolanda M. Gómez ◽  
Diego I. Gallardo ◽  
Jeremias Leão ◽  
Vinicius F. Calsavara
Keyword(s):  
Regression Model ◽  
Medical Data ◽  
Cure Rate ◽  
Piecewise Regression

FUZZY MULTIPLE OBJECTIVE PROGRAMMING IN AN INTERVAL PIECEWISE REGRESSION MODEL

2009 ◽  
Vol 17 (03) ◽  
pp. 365-376 ◽  
Author(s):  
JING-RUNG YU ◽  
GWO-HSHIUNG TZENG
Keyword(s):  
Regression Model ◽  
Regression Models ◽  
Heuristic Approach ◽  
Multiple Objective Programming ◽  
Change Points ◽  
Multiple Objective ◽  
Compromise Solution ◽  
Numerical Examples ◽  
Piecewise Regression ◽  
Objective Programming

This study proposes fuzzy multiple objective programming to determine the measure of fitness and the number of change-points in an interval piecewise regression model. To increase the measure of fitness, Tanaka and Lee proposed a conceptual procedure, which is a heuristic approach and becomes complicated for determining the proper polynomial. Therefore, a multiple objective approach is adopted to obtain a compromise solution among three objectives — maximizing the measure of fitness, minimizing the number of change-points and minimizing the width to obtain the interval regression models. By using the proposed method, a better measure of fitness can be obtained. Two numerical examples are used as demonstrations to illustrate our approach in more detail.

INTERVAL PIECEWISE REGRESSION MODEL WITH AUTOMATIC CHANGE-POINT DETECTION BY QUADRATIC PROGRAMMING

2005 ◽  
Vol 13 (03) ◽  
pp. 347-361 ◽  
Author(s):  
JING-RUNG YU ◽  
GWO-HSHIUNG TZENG ◽  
HAN-LIN LI
Keyword(s):  
Linear Programming ◽  
Quadratic Programming ◽  
Regression Model ◽  
Change Point ◽  
Regression Models ◽  
Change Point Detection ◽  
Fuzzy Regression ◽  
Programming Approach ◽  
Piecewise Regression ◽  
Point Detection

To handle large variation data, an interval piecewise regression method with automatic change-point detection by quadratic programming is proposed as an alternative to Tanaka and Lee's method. Their unified quadratic programming approach can alleviate the phenomenon where some coefficients tend to become crisp in possibilistic regression by linear programming and also obtain the possibility and necessity models at one time. However, that method can not guarantee the existence of a necessity model if a proper regression model is not assumed especially with large variations in data. Using automatic change-point detection, the proposed method guarantees obtaining the necessity model with better measure of fitness by considering variability in data. Without piecewise terms in estimated model, the proposed method is the same as Tanaka and Lee's model. Therefore, the proposed method is an alternative method to handle data with the large variations, which not only reduces the number of crisp coefficients of the possibility model in linear programming, but also simultaneously obtains the fuzzy regression models, including possibility and necessity models with better fitness. Two examples are presented to demonstrate the proposed method.

scholarly journals Directly and Simultaneously Expressing Absolute and Relative Treatment Effects in Medical Data Models and Applications

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1517
Author(s):  
Hao Yang Teng ◽  
Zhengjun Zhang
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Regression Models ◽  
Logistic Regression Model ◽  
Treatment Effects ◽  
Real Data ◽  
Medical Data ◽  
Severe Problem ◽  
New Model ◽  
Logistic Regression Models

Logistic regression is widely used in the analysis of medical data with binary outcomes to study treatment effects through (absolute) treatment effect parameters in the models. However, the indicative parameters of relative treatment effects are not introduced in logistic regression models, which can be a severe problem in efficiently modeling treatment effects and lead to the wrong conclusions with regard to treatment effects. This paper introduces a new enhanced logistic regression model that offers a new way of studying treatment effects by measuring the relative changes in the treatment effects and also incorporates the way in which logistic regression models the treatment effects. The new model, called the Absolute and Relative Treatment Effects (AbRelaTEs) model, is viewed as a generalization of logistic regression and an enhanced model with increased flexibility, interpretability, and applicability in real data applications than the logistic regression. The AbRelaTEs model is capable of modeling significant treatment effects via an absolute or relative or both ways. The new model can be easily implemented using statistical software, with the logistic regression model being treated as a special case. As a result, the classical logistic regression models can be replaced by the AbRelaTEs model to gain greater applicability and have a new benchmark model for more efficiently studying treatment effects in clinical trials, economic developments, and many applied areas. Moreover, the estimators of the coefficients are consistent and asymptotically normal under regularity conditions. In both simulation and real data applications, the model provides both significant and more meaningful results.

Piecewise Regression Model for Solenoid Embedded Inductors Based on the Quasi-newton Method

2005 ◽  
Vol 6 (6) ◽  
pp. 256-261
Author(s):  
Young-Don Ko ◽  
Kil-Han Kim ◽  
Il-Gu Yun ◽  
Kyu-Bok Lee ◽  
Jong-Kyu Kim
Keyword(s):  
Regression Model ◽  
Newton Method ◽  
Piecewise Regression ◽  
Quasi Newton

Aging Stubble Quail, Coturnix pectoralis Gould, by using Measurements of Lengths and the Moulting Stages of the Primaries

1981 ◽  
Vol 8 (1) ◽  
pp. 163
Author(s):  
FHJ Crome ◽  
SM Carpenter ◽  
DK Rushton
Keyword(s):  
Regression Model ◽  
Logistic Curve ◽  
Age Classes ◽  
Local Conditions ◽  
Piecewise Regression ◽  
In Captivity

'Measurements of the primaries at the first prebasic moult of captive stubble quail were used to assess their usefulness as aging criteria. The data from juvenile primaries 1-7 were fitted by a logistic curve, and the data from juvenile primaries 8-10 and adult primaries 1-7 by a piecewise regression model. Birds could be aged using these models up until the end of the first prebasic moult (90 days approx.). Birds could arrest primary replacement at the second prebasic moult and this complicated the assigning of birds to age classes based on the presence or absence of retained juvenile primaries 8, 9 and 10 and juvenile primary coverts. A key and tables are provided to assign birds to age classes and to calculate hatching dates of very young birds. The tables should not be used without being checked against known-age birds bred in captivity under local conditions.

scholarly journals Effect of Corporate Governance on Institutional Investors’ Preferences: An Empirical Investigation in Taiwan

2019 ◽  
Vol 12 (1) ◽  
pp. 32 ◽  
Author(s):  
Su-Lien Lu ◽  
Ying-Hui Li
Keyword(s):  
Corporate Governance ◽  
Regression Model ◽  
Institutional Investors ◽  
Governance System ◽  
Piecewise Regression ◽  
Positive Effects ◽  
Panel Data Regression ◽  
Data Regression ◽  
Corporate Governance System ◽  
Foreign Institutional Investors

This study discusses the institutional investors’ shareholding base on corporate governance system in Taiwan. The sample was 4760 Taiwanese companies from 2005 to 2012. Then, this study established six hypotheses to investigate the effects of corporate governance on institutional investors’ shareholdings. The panel data regression model and piecewise regression model were adopted to determine whether six hypotheses are supported. For sensitive analysis, additional consideration was given on the basis of industrial category (electronics or nonelectronics), and the 2008–2010 global financial crises. This study discovered that a nonlinear relationship exists between the domestic institutional investors’ shareholdings. The managerial ownership ratio and blockholder ownership ratio have positive effects both on domestic and foreign institutional investors. However, domestic and foreign institutional investors have distinct opinions regarding independent director ratios. Finally, the corporate governance did not improve institutional investors’ shareholdings during financial crisis periods; instead, they paid more attention to firm profits or other characteristics.

scholarly journals Trend analysis by a piecewise linear regression model applied to surface air temperatures in Southeastern Spain (1973–2014)

2016 ◽  
Author(s):  
Pablo Campra ◽  
Maria Morales
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Piecewise Linear ◽  
Surface Air Temperature ◽  
Least Square ◽  
Piecewise Regression ◽  
Southeastern Spain ◽  
Air Temperatures ◽  
Linear Trends

Abstract. The magnitude of the trends of environmental and climatic changes is mostly derived from the slopes of the linear trends using ordinary least-square fitting. An alternative flexible fitting model, piecewise regression, has been applied here to surface air temperature records in southeastern Spain for the recent warming period (1973–2014) to gain accuracy in the description of the inner structure of change, dividing the time series into linear segments with different slopes. Breakpoint years, with confidence intervals (CIs), were estimated and separated periods of significant trend change were determined. First, simple linear trends for mean, maximum and minimum surface air temperatures and diurnal temperature range (DTR) from the four longest and most reliable historic records in SE Spain were estimated. All series in the region showed intense linear warming signs during the period 1973–2014. However, updated warming trends were lower than those previously cited for the region and Spain from the 1970s onwards. Piecewise regression model allowed us to detect breakpoints in the series, and the absence of significant trends in the most recent period of the segmented fits for two stations. In general, piecewise regression model showed better fit than simple linear regression model, and thus, showed a better description of temperature variability.

scholarly journals Normal QT interval in Ukrainian Riding Horses at rest and during exercise

2020 ◽  
Vol 76 (12) ◽  
pp. 6472-2020
Author(s):  
BOHDANA POPADIUK ◽  
SERGIY HOLOPURA
Keyword(s):  
Regression Model ◽  
Qt Interval ◽  
Piecewise Linear ◽  
Diagnostic Value ◽  
Peak Exercise ◽  
Autonomic Tone ◽  
Piecewise Regression ◽  
Qt Intervals ◽  
Life Threatening ◽  
Show Jumping

Measurement of the main parameter of cardiac repolarization, namely QT interval, has a very high diagnostic value in human medicine, since its irregularities may indicate severe life-threatening ventricular tachyarrhythmias. The QT interval may vary not only with heart rate, age, sex, and autonomic tone, but also with horse breeds. Therefore, the description of its reference values for a specific breed is of great importance. The Ukrainian Riding Horse was bred as a show jumping, dressage and three-day eventing breed on the basis of Hanoverian, Thoroughbred and Trakehner stallions and local mares, as well as Hungarian Furioso, Gidran Arab and Nonius mares. Twenty-three horses of the Ukrainian riding breed were included in the study: 8 geldings, 8 mares, and 7 stallions aged 3-11 years. The electrodes for ECG recording were placed according to an adapted base-apex system. The ECG was registered during rest, exercise, and recovery periods. QT intervals were measured from resting to peak exercise levels on the traces of the 2nd lead and plotted against RR intervals. The piecewise regression model was fitted to the data plot. The values of Slope1, Slope2, and RRbend were compared to those of other breeds. The QT/RR relationship was relatively described by the piecewise linear regression model for all sexes (0.95 < r2 < 0.97). The sex of horses of the Ukrainian riding breed had a significant effect on the model. In terms of Slope1, Slope2, and RRbend values, Ukrainian riding horses are closest to Warmbloods, Standardbreds, and Thoroughbreds. The QT interval in horses should be corrected for breed and sex.

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