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Journal articles on the topic 'Interval regression model'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 February 2022

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1

ISHIBUCHI, Hisao, Hideo TANAKA, and Kazunori NAGASAKA. "Interval Data Analysis by Revised Interval Regression Model." Transactions of the Society of Instrument and Control Engineers 25, no. 11 (1989): 1218–24. http://dx.doi.org/10.9746/sicetr1965.25.1218.

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2

Lee, Seung-Chun, and Byung Su Choi. "Bayesian Interval Estimation of Tobit Regression Model." Korean Journal of Applied Statistics 26, no. 5 (2013): 737–46. http://dx.doi.org/10.5351/kjas.2013.26.5.737.

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3

Zhang, Xinyu, and Chu-An Liu. "INFERENCE AFTER MODEL AVERAGING IN LINEAR REGRESSION MODELS." Econometric Theory 35, no. 4 (2018): 816–41. http://dx.doi.org/10.1017/s0266466618000269.

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This article considers the problem of inference for nested least squares averaging estimators. We study the asymptotic behavior of the Mallows model averaging estimator (MMA; Hansen, 2007) and the jackknife model averaging estimator (JMA; Hansen and Racine, 2012) under the standard asymptotics with fixed parameters setup. We find that both MMA and JMA estimators asymptotically assign zero weight to the under-fitted models, and MMA and JMA weights of just-fitted and over-fitted models are asymptotically random. Building on the asymptotic behavior of model weights, we derive the asymptotic distr

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4

Gu, Xiangdong, David Shapiro, Michael,D Hughes, and Raji Balasubramanian. "Stratified Weibull Regression Model for Interval-Censored Data." R Journal 6, no. 1 (2014): 31. http://dx.doi.org/10.32614/rj-2014-003.

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5

Namdari, Mahshid, and Seung Hoe Choi. "Interval Regression Model Using L(p,q) -Estimator." International Journal of Computer Science and Application 3, no. 1 (2014): 50. http://dx.doi.org/10.14355/ijcsa.2014.0301.12.

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6

Sigauke, Caston, Murendeni Nemukula, and Daniel Maposa. "Probabilistic Hourly Load Forecasting Using Additive Quantile Regression Models." Energies 11, no. 9 (2018): 2208. http://dx.doi.org/10.3390/en11092208.

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Short-term hourly load forecasting in South Africa using additive quantile regression (AQR) models is discussed in this study. The modelling approach allows for easy interpretability and accounting for residual autocorrelation in the joint modelling of hourly electricity data. A comparative analysis is done using generalised additive models (GAMs). In both modelling frameworks, variable selection is done using least absolute shrinkage and selection operator (Lasso) via hierarchical interactions. Four models considered are GAMs and AQR models with and without interactions, respectively. The AQR

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7

Komárek, Arnošt, and Emmanuel Lesaffre. "The regression analysis of correlated interval-censored data." Statistical Modelling 9, no. 4 (2009): 299–319. http://dx.doi.org/10.1177/1471082x0900900403.

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The accelerated failure time (AFT) model is a useful alternative to the proportional hazard model for modelling interval-censored survival times. We illustrate the usefulness of a class of flexible AFT models. Flexibility is achieved by assuming that the distributional parts consist of penalized Gaussian mixtures. The AFT models are introduced and exemplified via research questions originating from a longitudinal dental study conducted in Flanders (North of Belgium). Emphasis is put on the analyzes which are performed using routines written in the R-language. They show the practical usefulness

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8

Kabaila, Paul. "VALID CONFIDENCE INTERVALS IN REGRESSION AFTER VARIABLE SELECTION." Econometric Theory 14, no. 4 (1998): 463–82. http://dx.doi.org/10.1017/s0266466698144031.

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We consider a linear regression model with regression parameters (θ1,...,θp) and error variance parameter σ2. Our aim is to find a confidence interval with minimum coverage probability 1 − α for a parameter of interest θ1 in the presence of nuisance parameters (θ2,...,θp,σ2). We consider two confidence intervals, the first of which is the standard confidence interval for θ1 with coverage probability 1 − α. The second confidence interval for θ1 is obtained after a variable selection procedure has been applied to θp. This interval is chosen to be as short as possible subject to the constraint th

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9

Liu, Hao, and Yu Shen. "A Semiparametric Regression Cure Model for Interval-Censored Data." Journal of the American Statistical Association 104, no. 487 (2009): 1168–78. http://dx.doi.org/10.1198/jasa.2009.tm07494.

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10

Lima Neto, Eufrásio de A., and Ulisses U. dos Anjos. "Regression model for interval-valued variables based on copulas." Journal of Applied Statistics 42, no. 9 (2015): 2010–29. http://dx.doi.org/10.1080/02664763.2015.1015114.

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11

Peng, Defen, Gilbert MacKenzie, and Kevin Burke. "A multiparameter regression model for interval‐censored survival data." Statistics in Medicine 39, no. 14 (2020): 1903–18. http://dx.doi.org/10.1002/sim.8508.

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12

de Lima Taga, Marcel F., and Julio M. Singer. "Simple linear regression with interval censored dependent and independent variables." Statistical Methods in Medical Research 27, no. 1 (2016): 198–207. http://dx.doi.org/10.1177/0962280215626467.

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We consider a simple linear regression model that accommodates situations where both the dependent and the independent variables are interval censored. We obtain maximum likelihood estimators of its parameters and compare their performance with that of estimators derived under ordinary linear regression models. We also develop prediction intervals for the response and illustrate the results with data from an audiometric study designed to evaluate the possibility of prediction of behavioural thresholds from physiological thresholds.

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13

Zheng, Bowen, Kaiping Yu, Shuaishuai Liu, and Rui Zhao. "Interval model updating using universal grey mathematics and Gaussian process regression model." Mechanical Systems and Signal Processing 141 (July 2020): 106455. http://dx.doi.org/10.1016/j.ymssp.2019.106455.

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14

Chen, Yong Qi. "A Parameter Selection Method for Support Vector Interval Regression Model." Applied Mechanics and Materials 66-68 (July 2011): 626–30. http://dx.doi.org/10.4028/www.scientific.net/amm.66-68.626.

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The support vector interval regression model is an effective method to estimate imprecise data. Parameters of this model is very important in order to obtain the excellent regression result. The flexible polyhedron search algorithm is a fast optimization algorithm. Based on the flexible polyhedron search algorithm, this paper proposes an automatic parameters selection method for the support vector interval regression model. Experiments illustrate the validity and applicability of the support vector interval regression model based on the flexible polyhedron search algorithm.

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15

Ogawa, Shinichiro, and Masahiro Satoh. "Random Regression Analysis of Calving Interval of Japanese Black Cows." Animals 11, no. 1 (2021): 202. http://dx.doi.org/10.3390/ani11010202.

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We estimated genetic parameters for the calving interval of Japanese Black cows using a random regression model and a repeatability model. We analyzed 92,019 calving interval records of 36,178 cows. Pedigree data covered 390,263 individuals. Age of cow at previous calving for each record ranged from 18 to 120 months. We used up to the second-order Legendre polynomials based on age at previous calving as sub-models for random regression analysis, and assumed a constant error variance across ages. Estimated heritability was 0.12 to 0.20 with the random regression model and 0.17 with the repeatab

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16

Storm, Scott M., Raymond R. Hill, Joseph J. Pignatiello, Edward D. White, and G. Geoffrey Vining. "Point-wise model validation over experimental regions using regression confidence and tolerance intervals with Bayesian relaxations." SIMULATION 96, no. 1 (2019): 75–87. http://dx.doi.org/10.1177/0037549719844193.

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As systems grow more complex, so does our propensity to use computers to emulate complex real-world systems. Often these real-world systems possess dynamic response behavior over the operational domain of input parameter configurations. This domain is referred to as the design space or experimental region. It is critical we ensure that computer models which emulate such dynamic behavior be validated over the full design space. This paper presents a dual-interval validation methodology. Confidence intervals and tolerance intervals are developed based on a system response surface function. Model

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17

Blanco-Fernández, Angela, Ana Colubi, and Gil González-Rodríguez. "Confidence sets in a linear regression model for interval data." Journal of Statistical Planning and Inference 142, no. 6 (2012): 1320–29. http://dx.doi.org/10.1016/j.jspi.2011.09.017.

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18

YU, JING-RUNG, and GWO-HSHIUNG TZENG. "FUZZY MULTIPLE OBJECTIVE PROGRAMMING IN AN INTERVAL PIECEWISE REGRESSION MODEL." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 17, no. 03 (2009): 365–76. http://dx.doi.org/10.1142/s0218488509005929.

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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 us

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19

Bortniker, Ethan, Michael Tadros, and John Birk. "Surveillance Colonoscopy Interval: A Regression Model That Renders Pathology Unnecessary." American Journal of Gastroenterology 109 (October 2014): S597—S598. http://dx.doi.org/10.1038/ajg.2014.285.

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20

Wong, George Y. C., and Qiqing Yu. "Estimation Under the Lehmann Regression Model with Interval-Censored Data." Communications in Statistics - Simulation and Computation 41, no. 8 (2012): 1489–500. http://dx.doi.org/10.1080/03610918.2011.606949.

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21

Shen, Pao-sheng. "Median regression model with left truncated and interval-censored data." Journal of the Korean Statistical Society 42, no. 4 (2013): 469–79. http://dx.doi.org/10.1016/j.jkss.2013.02.002.

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22

Friedrich, Thomas, and Guido Knapp. "Generalised interval estimation in the random effects meta regression model." Computational Statistics & Data Analysis 64 (August 2013): 165–79. http://dx.doi.org/10.1016/j.csda.2013.03.011.

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23

Ospina, Raydonal, Francisco Cribari-Neto, and Klaus L. P. Vasconcellos. "Improved point and interval estimation for a beta regression model." Computational Statistics & Data Analysis 51, no. 2 (2006): 960–81. http://dx.doi.org/10.1016/j.csda.2005.10.002.

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24

Hashimoto, Elizabeth M., Edwin M. M. Ortega, Vicente G. Cancho, and Gauss M. Cordeiro. "The log-exponentiated Weibull regression model for interval-censored data." Computational Statistics & Data Analysis 54, no. 4 (2010): 1017–35. http://dx.doi.org/10.1016/j.csda.2009.10.014.

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25

Pacini, David. "The two‐sample linear regression model with interval‐censored covariates." Journal of Applied Econometrics 34, no. 1 (2018): 66–81. http://dx.doi.org/10.1002/jae.2654.

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26

YU, JING-RUNG, GWO-HSHIUNG TZENG, and HAN-LIN LI. "INTERVAL PIECEWISE REGRESSION MODEL WITH AUTOMATIC CHANGE-POINT DETECTION BY QUADRATIC PROGRAMMING." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 13, no. 03 (2005): 347–61. http://dx.doi.org/10.1142/s0218488505003503.

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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 automa

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27

Hidayati, Lilik, Nur Chamidah, and I. Nyoman Budiantara. "ESTIMASI SELANG KEPERCAYAAN NILAI UJIAN NASIONAL BERBASIS KOMPETENSI BERDASARKAN MODEL REGRESI SEMIPARAMETRIK MULTIRESPON TRUNCATED SPLINE." MEDIA STATISTIKA 13, no. 1 (2020): 92–103. http://dx.doi.org/10.14710/medstat.13.1.92-103.

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Confidence interval estimation is important in statistical inference for the parameters of the regression model, but the theory of confidence interval estimation for multi-response semiparametric regression model parameters based on the truncated spline estimator has not been examined. In this study, we estimate the confidence interval of the multi-response semiparametric regression model based on the truncated spline estimator by using pivotal quantity method with the central limit theorem approach. This confidence interval theory is applied to data of competency-based national exam (UNBK) sc

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28

Lio, Waichon, and Baoding Liu. "Residual and confidence interval for uncertain regression model with imprecise observations." Journal of Intelligent & Fuzzy Systems 35, no. 2 (2018): 2573–83. http://dx.doi.org/10.3233/jifs-18353.

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29

Gómez, Guadalupe, Anna Espinal, and Stephen W. Lagakos. "Inference for a linear regression model with an interval-censored covariate." Statistics in Medicine 22, no. 3 (2003): 409–25. http://dx.doi.org/10.1002/sim.1326.

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30

Wu, Jianrong, A. C. M. Wong, and Wei Wei. "Interval estimation of the mean response in a log-regression model." Statistics in Medicine 25, no. 12 (2006): 2125–35. http://dx.doi.org/10.1002/sim.2329.

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31

Lima Neto, Eufrásio de A., and Francisco de A. T. de Carvalho. "An exponential-type kernel robust regression model for interval-valued variables." Information Sciences 454-455 (July 2018): 419–42. http://dx.doi.org/10.1016/j.ins.2018.05.008.

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32

Salem, Hanaa Abd El Reheem. "Estimation of Beta Regression Model with Applied Study." JOURNAL OF ADVANCES IN MATHEMATICS 12, no. 11 (2016): 6773–77. http://dx.doi.org/10.24297/jam.v12i11.10.

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This paper proposes a regression model where the dependent variable is beta distributed. Therefore the observations of the dependent variable must fall within (0,1) interval. This beta regression model produces two regression coefficients: one for the model of the mean and one for the model of the dispersion. Parameter estimation is performed by maximum likelihood and Bayesian method. Finally, numerical study is presented.

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33

Yabuuchi, Yoshiyuki, Takayuki Kawaura, and Junzo Watada. "Fuzzy Autocorrelation Model with Fuzzy Confidence Intervals and its Evaluation." Journal of Advanced Computational Intelligence and Intelligent Informatics 20, no. 4 (2016): 512–20. http://dx.doi.org/10.20965/jaciii.2016.p0512.

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Interval models based on fuzzy regression and fuzzy time-series can illustrate the possibilities of a system using the intervals in the model. Thus, the aim is to minimize the vagueness of the model in order to describe the possible states of the system. In the present study, we consider on an interval fuzzy time-series model based on a Box–Jenkins model, a fuzzy autocorrelation model proposed by Yabuuchi, and a fuzzy regressive model proposed by Ozawa. We examine two models by analyzing the Japanese national consumer price index and demonstrate that our approach improves the accuracy of predi

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34

Noskov, S. I., I. P. Vrublevskiy, and V. O. Zayanchukovskaya. "APPLICATION OF INTERVAL REGRESSION ANALYSIS FOR MODELLING OF TRANSPORT OBJECTS." Herald of the Ural State University of Railway Transport, no. 3 (2020): 45–52. http://dx.doi.org/10.20291/2079-0392-2020-3-45-52.

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The article looks upon issues of application of regression analysis methods for modeling of complex objects, the process information for which is given with interval indefiniteness of different character caused by a number of reasons, in particular, malfunctioning of measuring equipment, mistakes of statistical service, ambiguity of existent methods of fixation of results of the objects under analysis. The problems of integrated comparison of the obtained results with conventional regression analogues are solved - least square and module methods, anti - crash assessment of regression model par

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35

Altun, Emrah, M. El-Morshedy, and M. S. Eliwa. "A new regression model for bounded response variable: An alternative to the beta and unit-Lindley regression models." PLOS ONE 16, no. 1 (2021): e0245627. http://dx.doi.org/10.1371/journal.pone.0245627.

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A new distribution defined on (0,1) interval is introduced. Its probability density and cumulative distribution functions have simple forms. Thanks to its simple forms, the moments, incomplete moments and quantile function of the proposed distribution are derived and obtained in explicit forms. Four parameter estimation methods are used to estimate the unknown parameter of the distribution. Besides, simulation study is implemented to compare the efficiencies of these parameter estimation methods. More importantly, owing to the proposed distribution, we provide an alternative regression model f

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36

Hu, Yi-Chung. "Multilayer Perceptron for Robust Nonlinear Interval Regression Analysis Using Genetic Algorithms." Scientific World Journal 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/970931.

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On the basis of fuzzy regression, computational models in intelligence such as neural networks have the capability to be applied to nonlinear interval regression analysis for dealing with uncertain and imprecise data. When training data are not contaminated by outliers, computational models perform well by including almost all given training data in the data interval. Nevertheless, since training data are often corrupted by outliers, robust learning algorithms employed to resist outliers for interval regression analysis have been an interesting area of research. Several approaches involving co

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37

Smith, Brian L., and Michael J. Demetsky. "Multiple-Interval Freeway Traffic Flow Forecasting." Transportation Research Record: Journal of the Transportation Research Board 1554, no. 1 (1996): 136–41. http://dx.doi.org/10.1177/0361198196155400117.

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Freeway traffic flow forecasting will play an important role in intelligent transportation systems. The TRB Committee on Freeway Operations has included freeway flow forecasting in its 1995 research program. Much of the past research in traffic flow forecasting has addressed short-term, single-interval predictions. Such limited forecasting models will not support the development of the longer-term operational strategies needed for such events as hazardous material incidents. A multiple-interval freeway traffic flow forecasting model has been developed that predicts traffic volumes in 15-min in

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38

Mehrez, Abraham, and B. L. Myers. "A Note on an Input Control Model of a Predictive Regression Interval." Journal of the Operational Research Society 45, no. 3 (1994): 354. http://dx.doi.org/10.2307/2584169.

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39

Finkelstein, Dianne M., and Robert A. Wolfe. "A Semiparametric Model for Regression Analysis of Interval-Censored Failure Time Data." Biometrics 41, no. 4 (1985): 933. http://dx.doi.org/10.2307/2530965.

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40

Blanco-Fernández, A., and G. González-Rodríguez. "Inferential studies for a flexible linear regression model for interval-valued variables." International Journal of Computer Mathematics 93, no. 4 (2014): 658–75. http://dx.doi.org/10.1080/00207160.2014.964998.

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41

Prawanti, Dasty Dewi, I. Nyoman Budiantara, and Jerry D. T. Purnomo. "Parameter Interval Estimation of Semiparametric Spline Truncated Regression Model for Longitudinal Data." IOP Conference Series: Materials Science and Engineering 546 (June 26, 2019): 052053. http://dx.doi.org/10.1088/1757-899x/546/5/052053.

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42

Mehrez, Abraham, and B. L. Myers. "A Note on an Input Control Model of a Predictive Regression Interval." Journal of the Operational Research Society 45, no. 3 (1994): 354–57. http://dx.doi.org/10.1057/jors.1994.48.

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43

Zhong, Yu, Zhongzhan Zhang, and Shoumei Li. "A Constrained Interval-Valued Linear Regression Model: A New Heteroscedasticity Estimation Method." Journal of Systems Science and Complexity 33, no. 6 (2020): 2048–66. http://dx.doi.org/10.1007/s11424-020-9075-2.

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44

Hao, Peng, and Junpeng Guo. "Constrained center and range joint model for interval-valued symbolic data regression." Computational Statistics & Data Analysis 116 (December 2017): 106–38. http://dx.doi.org/10.1016/j.csda.2017.06.005.

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45

Jiang, Peng, Yi-Chung Hu, Wenbao Wang, Hang Jiang, and Geng Wu. "Interval Grey Prediction Models with Forecast Combination for Energy Demand Forecasting." Mathematics 8, no. 6 (2020): 960. http://dx.doi.org/10.3390/math8060960.

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Time series data for decision problems such as energy demand forecasting are often derived from uncertain assessments, and do not meet any statistical assumptions. The interval grey number becomes an appropriate representation for an uncertain and imprecise observation. In order to obtain nonlinear interval grey numbers with better forecasting accuracy, this study proposes a combined model by fusing interval grey numbers estimated by neural networks (NNs) and the grey prediction models. The proposed model first uses interval regression analysis using NNs to estimate interval grey numbers for a

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46

Mwangi, Ronald Waweru, Hideyuki Imai, and Yoshiharu Sato. "A MINIMIZATION METHOD FOR COMPUTING PARAMETER BOUNDS IN AN INTERVAL VALUED LINEAR REGRESSION MODEL USING INTERVAL ANALYSIS." Journal of the Japanese Society of Computational Statistics 17, no. 1 (2004): 21–31. http://dx.doi.org/10.5183/jjscs1988.17.21.

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47

Gogo, Kevin Otieno, Lawrence Nderu, and Makau Mutua. "Variances in knowledge-based interval type 2 Gaussian fuzzy on linear regression models." Journal of Intelligent & Fuzzy Systems 41, no. 1 (2021): 1807–20. http://dx.doi.org/10.3233/jifs-210568.

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Fuzzy logic is a branch of artificial intelligence that has been used extensively in developing Fuzzy systems and models. These systems usually offer artificial intelligence based on the predictive mathematical models used; in this case linear regression mathematical model. Interval type 2 Gaussian fuzzy logic is a fuzzy logic that utilizes Gaussian upper membership function and the lower membership function, with a footprint of uncertainty in between the Gaussian membership functions. The artificial intelligence solutions predicted by these interval type 2 fuzzy systems depends on the trainin

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48

Desviona, Nayla, and Ferra Yanuar. "Simulation Study of Autocorrelated Error Using Bayesian Quantile Regression." Science and Technology Indonesia 5, no. 3 (2020): 70. http://dx.doi.org/10.26554/sti.2020.5.3.70-74.

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 The purpose of this study is to compare the ability of the Classical Quantile Regression method and the Bayesian Quantile Regression method in estimating models that contain autocorrelated error problems using simulation studies. In the quantile regression approach, the data response is divided into several pieces or quantiles conditions on indicator variables. Then, The parameter model is estimated for each selected quantiles. The parameters are estimated using conditional quantile functions obtained by minimizing absolute asymmetric errors. In the Bayesian quantile regression method,

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49

Yang, Zhaosheng, Xiujuan Tian, Wei Wang, Xiyang Zhou, and Hongmei Liang. "Research on Driver Behavior in Yellow Interval at Signalized Intersections." Mathematical Problems in Engineering 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/518782.

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Vehicles are often caught in dilemma zone when they approach signalized intersections in yellow interval. The existence of dilemma zone which is significantly influenced by driver behavior seriously affects the efficiency and safety of intersections. This paper proposes the driver behavior models in yellow interval by logistic regression and fuzzy decision tree modeling, respectively, based on camera image data. Vehicle’s speed and distance to stop line are considered in logistic regression model, which also brings in a dummy variable to describe installation of countdown timer display. Fuzzy

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50

DelSole, Timothy, and Xiaosong Yang. "Field Significance of Regression Patterns." Journal of Climate 24, no. 19 (2011): 5094–107. http://dx.doi.org/10.1175/2011jcli4105.1.

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Regression patterns often are used to diagnose the relation between a field and a climate index, but a significance test for the pattern “as a whole” that accounts for the multiplicity and interdependence of the tests has not been widely available. This paper argues that field significance can be framed as a test of the hypothesis that all regression coefficients vanish in a suitable multivariate regression model. A test for this hypothesis can be derived from the generalized likelihood ratio test. The resulting statistic depends on relevant covariance matrices and accounts for the multiplicit

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