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Relevant bibliographies by topics / Interval regression model

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Academic literature on the topic 'Interval regression model'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 February 2022

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Journal articles on the topic "Interval regression model"

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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Interval regression model"

1

Chen, Cuixian. "Asymptotic properties of the Buckley-James estimator for a bivariate interval censorship regression model." Diss., Online access via UMI:, 2007.

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Tao, Jinxin. "Comparison Between Confidence Intervals of Multiple Linear Regression Model with or without Constraints." Digital WPI, 2017. https://digitalcommons.wpi.edu/etd-theses/404.

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Regression analysis is one of the most applied statistical techniques. The sta- tistical inference of a linear regression model with a monotone constraint had been discussed in early analysis. A natural question arises when it comes to the difference between the cases of with and without the constraint. Although the comparison be- tween confidence intervals of linear regression models with and without restriction for one predictor variable had been considered, this discussion for multiple regres- sion is required. In this thesis, I discuss the comparison of the confidence intervals between a

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Lin, Hui-Ling. "Jackknife Empirical Likelihood for the Variance in the Linear Regression Model." Digital Archive @ GSU, 2013. http://digitalarchive.gsu.edu/math_theses/129.

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The variance is the measure of spread from the center. Therefore, how to accurately estimate variance has always been an important topic in recent years. In this paper, we consider a linear regression model which is the most popular model in practice. We use jackknife empirical likelihood method to obtain the interval estimate of variance in the regression model. The proposed jackknife empirical likelihood ratio converges to the standard chi-squared distribution. The simulation study is carried out to compare the jackknife empirical likelihood method and standard method in terms of coverage pr

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Lu, Yinghua. "Empirical Likelihood Inference for the Accelerated Failure Time Model via Kendall Estimating Equation." Digital Archive @ GSU, 2010. http://digitalarchive.gsu.edu/math_theses/76.

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In this thesis, we study two methods for inference of parameters in the accelerated failure time model with right censoring data. One is the Wald-type method, which involves parameter estimation. The other one is empirical likelihood method, which is based on the asymptotic distribution of likelihood ratio. We employ a monotone censored data version of Kendall estimating equation, and construct confidence intervals from both methods. In the simulation studies, we compare the empirical likelihood (EL) and the Wald-type procedure in terms of coverage accuracy and average length of confidence int

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Huschens, Stefan. "Einführung in die Ökonometrie." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2017. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-222629.

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Die Kapitel 1 bis 6 im ersten Teil dieses Skriptes beruhen auf einer Vorlesung Ökonometrie I, die zuletzt im WS 2001/02 gehalten wurde, die Kapitel 7 bis 16 beruhen auf einer Vorlesung Ökonometrie II, die zuletzt im SS 2006 gehalten wurde. Das achte Kapitel enthält eine komprimierte Zusammenfassung der Ergebnisse aus dem Teil Ökonometrie I.

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Langohr, Klaus. "Regression models with an interval-censored covariate." Doctoral thesis, Universitat Politècnica de Catalunya, 2004. http://hdl.handle.net/10803/6508.

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El análisis de supervivencia trata de la evaluación estadística de variables que miden el tiempo transcurrido hasta un evento de interés. Una particularidad que ha de considerar el análisis de supervivencia son datos censurados. Éstos aparecen cuando el tiempo de interés no puede ser observado exactamente y la información al respecto es parcial. Se distinguen diferentes tipos de censura: un tiempo censurado por la derecha está presente si el tiempo de supervivencia es sabido mayor a un tiempo observado; censura por izquierda está dada si la supervivencia es menor que un tiempo observado. En el

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Huang, Jian. "Estimation in regression models with interval censoring /." Thesis, Connect to this title online; UW restricted, 1994. http://hdl.handle.net/1773/8950.

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Rada, Miroslav. "Algoritmy pro vybrané geometrické problémy nad zonotopy a jejich aplikace v optimalizaci a v analýze dat." Doctoral thesis, Vysoká škola ekonomická v Praze, 2009. http://www.nusl.cz/ntk/nusl-199386.

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The thesis unifies the most important author's results in the field of algorithms concerning zonotopes and their applications in optimization and statistics. The computational-geometric results consist of a new compact output-sensitive algorithm for enumerating vertices of a zonotope, which outperforms the rival algorithm with the same complexity-theoretic properties both theoretically and empirically, and a polynomial algorithm for arbitrarily precise approximation of a zonotope with the Löwner-John ellipsoid. In the application area, the thesis presents a result, which connects linear regres

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Zhang, Zhigang. "Nonproportional hazards regression models for survival analysis /." free to MU campus, to others for purchase, 2004. http://wwwlib.umi.com/cr/mo/fullcit?p3144473.

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Topp, Rebekka. "Regression and residual analysis in linear models with interval censored data." Doctoral thesis, Universitat Politècnica de Catalunya, 2002. http://hdl.handle.net/10803/6511.

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This work consists of two parts, both related with regression analysis for interval censored data. Interval censored data x have the property that their value cannot be observed exactly but only the respective interval [xL,xR] which contains the true value x with probability one.<br/><br/>In the first part of this work I develop an estimation theory for the regression parameters of the linear model where both dependent and independent variables are interval censored. In doing so I use a semi-parametric maximum likelihood approach which determines the parameter estimates via maximization of the

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Books on the topic "Interval regression model"

1

Confidence intervals in generalized regression models. CRC Press, 2008.

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Tapia-Aguilar, Alberto. Accurate confidence intervals for regression parameters in proportional hazards model. [s.n.], 1994.

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Liman, Mohamed. Simultaneous tolerance intervals in the random one-way model with convariates. Dept. of Statistics, Oregon State University, 1985.

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Fraser, D. A. S. Non-nested linear models: A conditional confidence approach. University of Toronto, Dept. of Statistics, 1985.

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W, Stringer Kenneth, Deloitte, Haskins & Sells., and Deloitte Touche Tohmatsu International, eds. Statistical techniques for analytical review in auditing. 2nd ed. John Wiley, 1996.

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Stringer, Kenneth W. Statistical techniques for analytical review in auditing. Wiley, 1986.

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7

Uusipaikka, Esa. Confidence Intervals in Generalized Regression Models. Chapman and Hall/CRC, 2008. http://dx.doi.org/10.1201/9781420060386.

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McCleary, Richard, David McDowall, and Bradley J. Bartos. Internal Validity. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190661557.003.0007.

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Chapter 7 begins with an outline and description of five threats to internal validity common to time series designs: history, maturation, instrumentation, regression, and selection. Given the fundamental role of prediction in the modern scientific method, scientific hypotheses are necessarily causal. After an outline of the evolving definition of “causality” in the social sciences, contemporary Rubin causality or counterfactual causality is introduced. Under the assumption that subjects were randomly assigned to the treatment and control groups, Rubin’s causal model allows one to estimate the

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Abdelhafez, Mohamed Elwy Mahran. Bootstrap prediction and tolerance intervals for the Weibull regression model with censored data. 1989.

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Cheng, Russell. Bootstrap Analysis. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198505044.003.0004.

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Parametric bootstrapping (BS) provides an attractive alternative, both theoretically and numerically, to asymptotic theory for estimating sampling distributions. This chapter summarizes its use not only for calculating confidence intervals for estimated parameters and functions of parameters, but also to obtain log-likelihood-based confidence regions from which confidence bands for cumulative distribution and regression functions can be obtained. All such BS calculations are very easy to implement. Details are also given for calculating critical values of EDF statistics used in goodness-of-fit

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Book chapters on the topic "Interval regression model"

1

Torsney, B., and J. López-Fidalgo. "Minimax Designs for Logistic Regression in a Compact Interval." In mODa 6 — Advances in Model-Oriented Design and Analysis. Physica-Verlag HD, 2001. http://dx.doi.org/10.1007/978-3-642-57576-1_27.

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Bárzana, Marta García, Ana Colubi, and Erricos John Kontoghiorghes. "Lasso Estimation of an Interval-Valued Multiple Regression Model." In Strengthening Links Between Data Analysis and Soft Computing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-10765-3_22.

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Maia, André Luis Santiago, and Francisco de A.T. de Carvalho. "Fitting a Least Absolute Deviation Regression Model on Interval-Valued Data." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-88190-2_26.

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Lin, Jianchang, Debajyoti Sinha, Stuart Lipsitz, and Adriano Polpo. "Semiparametric Analysis of Interval-Censored Survival Data with Median Regression Model." In Statistical Applications from Clinical Trials and Personalized Medicine to Finance and Business Analytics. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42568-9_13.

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de Carvalho, Francisco de A. T., Gilbert Saporta, and Danilo N. Queiroz. "A Clusterwise Center and Range Regression Model for Interval-Valued Data." In Proceedings of COMPSTAT'2010. Physica-Verlag HD, 2010. http://dx.doi.org/10.1007/978-3-7908-2604-3_45.

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García-Bárzana, Marta, Ana Colubi, and Erricos J. Kontoghiorghes. "On the Estimation of the Regression Model M for Interval Data." In Towards Advanced Data Analysis by Combining Soft Computing and Statistics. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-30278-7_4.

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Blanco-Fernández, Angela, Ana Colubi, Marta García-Bárzana, and Manuel Montenegro. "A Linear Regression Model for Interval-Valued Response Based on Set Arithmetic." In Synergies of Soft Computing and Statistics for Intelligent Data Analysis. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-33042-1_12.

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Pordeli, Pooneh, and Xuewen Lu. "A Proportional Odds Model for Regression Analysis of Case I Interval-Censored Data." In Advanced Statistical Methods in Data Science. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-2594-5_6.

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Blanco-Fernández, Angela, Norberto Corral, Gil González-Rodríguez, and Antonio Palacio. "On Some Confidence Regions to Estimate a Linear Regression Model for Interval Data." In Advances in Intelligent and Soft Computing. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14746-3_5.

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de Carvalho, Francisco de A. T., Eufrasio de A. Lima Neto, and Camilo P. Tenorio. "A New Method to Fit a Linear Regression Model for Interval-Valued Data." In KI 2004: Advances in Artificial Intelligence. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30221-6_23.

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Conference papers on the topic "Interval regression model"

1

Li, Feng, Shoumei Li, Nana Tang, and Thierry Denoux. "Constrained interval-valued linear regression model." In 2017 20th International Conference on Information Fusion (Fusion). IEEE, 2017. http://dx.doi.org/10.23919/icif.2017.8009676.

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Ishibuchi, H., and H. Tanaka. "Regression analysis with interval model by neural networks." In 1991 IEEE International Joint Conference on Neural Networks. IEEE, 1991. http://dx.doi.org/10.1109/ijcnn.1991.170638.

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Lima Neto, Eufrasio de Andrade, and Francisco de Assis Tenorio de Carvalho. "Nonlinear regression model to symbolic interval-valued variables." In 2008 IEEE International Conference on Systems, Man and Cybernetics (SMC). IEEE, 2008. http://dx.doi.org/10.1109/icsmc.2008.4811454.

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Cai, Lili, Degang Wang, Wenyan Song, and Hongxing Li. "Nonlinear interval regression analysis based on spline fuzzy model with interval coefficients." In 2016 Seventh International Conference on Intelligent Control and Information Processing (ICICIP). IEEE, 2016. http://dx.doi.org/10.1109/icicip.2016.7885903.

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Yabuuchi, Yoshiyuki. "Evaluation of an Interval-Type Model on Fuzzy Regression." In 2018 International Conference on Unconventional Modelling, Simulation and Optimization - Soft Computing and Meta Heuristics - UMSO. IEEE, 2018. http://dx.doi.org/10.1109/umso.2018.8637234.

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Inuiguchi, Masahiro, and Hiroki Inoue. "A Fuzzily Partitioned Interval Function Model for Ordinal Regression." In 2018 4th International Conference on Computer and Information Sciences (ICCOINS). IEEE, 2018. http://dx.doi.org/10.1109/iccoins.2018.8510614.

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de Souza, Renata M. C. R., Francisco Jose A. Cysneiros, Diego C. F. Queiroz, and Roberta A. de A. Fagundes. "A multi-class logistic regression model for interval data." In 2008 IEEE International Conference on Systems, Man and Cybernetics (SMC). IEEE, 2008. http://dx.doi.org/10.1109/icsmc.2008.4811455.

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Yabuuchi, Yoshiyuki. "Data Processing Method for an Interval-Type Fuzzy Regression Model." In 2018 4th International Conference on Computer and Information Sciences (ICCOINS). IEEE, 2018. http://dx.doi.org/10.1109/iccoins.2018.8510609.

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Mohammad Reza Rabiei, Naser Reza Arghami, S. Mahmoud Taheri, and Bahram Sadeghpour. "Fuzzy regression model with interval-valued fuzzy input-output data." In 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2013. http://dx.doi.org/10.1109/fuzz-ieee.2013.6622315.

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Zhu, Lin, Jiaxing Lu, and Yihong Chen. "HDI-Forest: Highest Density Interval Regression Forest." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/621.

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By seeking the narrowest prediction intervals (PIs) that satisfy the specified coverage probability requirements, the recently proposed quality-based PI learning principle can extract high-quality PIs that better summarize the predictive certainty in regression tasks, and has been widely applied to solve many practical problems. Currently, the state-of-the-art quality-based PI estimation methods are based on deep neural networks or linear models. In this paper, we propose Highest Density Interval Regression Forest (HDI-Forest), a novel quality-based PI estimation method that is instead based o

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Reports on the topic "Interval regression model"

1

Wong, George Y. Cox Regression Model for Interval-Censored Data in Breast Cancer Follow-up Studies. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada419260.

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Wegner, Michael D. Physician Provider Profiling in Brooke Army Medical Center's Internal Medicine Clinic: A Multiple Regression and Process Control Model. Defense Technical Information Center, 1999. http://dx.doi.org/10.21236/ada420371.

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Over, Thomas, Riki Saito, Andrea Veilleux, et al. Estimation of Peak Discharge Quantiles for Selected Annual Exceedance Probabilities in Northeastern Illinois. Illinois Center for Transportation, 2016. http://dx.doi.org/10.36501/0197-9191/16-014.

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This report provides two sets of equations for estimating peak discharge quantiles at annual exceedance probabilities (AEPs) of 0.50, 0.20, 0.10, 0.04, 0.02, 0.01, 0.005, and 0.002 (recurrence intervals of 2, 5, 10, 25, 50, 100, 200, and 500 years, respectively) for watersheds in Illinois based on annual maximum peak discharge data from 117 watersheds in and near northeastern Illinois. One set of equations was developed through a temporal analysis with a two-step least squares-quantile regression technique that measures the average effect of changes in the urbanization of the watersheds used i

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Pintar, Adam L., Emil Simiu, Franklin T. Lombardo, and Marc L. Levitan. Simple Guide for Evaluating and Expressing the Uncertainty of NIST MeasuremenMaps of Non-hurricane Non-tornadic Wind Speeds With Specified Mean Recurrence Intervals for the Contiguous United States Using a Two-Dimensional Poisson Process Extreme Value Model and Local Regressiont Results. National Institute of Standards and Technology, 2015. http://dx.doi.org/10.6028/nist.sp.500-301.

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McPhedran, R., K. Patel, B. Toombs, et al. Food allergen communication in businesses feasibility trial. Food Standards Agency, 2021. http://dx.doi.org/10.46756/sci.fsa.tpf160.

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Background: Clear allergen communication in food business operators (FBOs) has been shown to have a positive impact on customers’ perceptions of businesses (Barnett et al., 2013). However, the precise size and nature of this effect is not known: there is a paucity of quantitative evidence in this area, particularly in the form of randomised controlled trials (RCTs). The Food Standards Agency (FSA), in collaboration with Kantar’s Behavioural Practice, conducted a feasibility trial to investigate whether a randomised cluster trial – involving the proactive communication of allergen information a

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