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Journal articles on the topic 'Flexible regression models'

Author: Grafiati

Published: 9 March 2023

Last updated: 31 July 2025

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1

Gurmu, Shiferaw, and John Elder. "Flexible Bivariate Count Data Regression Models." Journal of Business & Economic Statistics 30, no. 2 (2012): 265–74. http://dx.doi.org/10.1080/07350015.2011.638816.

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2

O'Donnell, David, Alastair Rushworth, Adrian W. Bowman, E. Marian Scott, and Mark Hallard. "Flexible regression models over river networks." Journal of the Royal Statistical Society: Series C (Applied Statistics) 63, no. 1 (2013): 47–63. http://dx.doi.org/10.1111/rssc.12024.

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3

Nikulin, M., and Hong-Dar Isaac Wu. "Flexible regression models for carcinogenesis studies." Journal of Mathematical Sciences 145, no. 2 (2007): 4880–93. http://dx.doi.org/10.1007/s10958-007-0322-z.

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4

Lee, Young K., Enno Mammen, and Byeong U. Park. "Flexible generalized varying coefficient regression models." Annals of Statistics 40, no. 3 (2012): 1906–33. http://dx.doi.org/10.1214/12-aos1026.

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5

Durrleman, Sylvain, and Richard Simon. "Flexible regression models with cubic splines." Statistics in Medicine 8, no. 5 (1989): 551–61. http://dx.doi.org/10.1002/sim.4780080504.

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6

Bonat, Wagner Hugo, and Célestin C. Kokonendji. "Flexible Tweedie regression models for continuous data." Journal of Statistical Computation and Simulation 87, no. 11 (2017): 2138–52. http://dx.doi.org/10.1080/00949655.2017.1318876.

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7

Dahl, Christian M., and Svend Hylleberg. "Flexible regression models and relative forecast performance." International Journal of Forecasting 20, no. 2 (2004): 201–17. http://dx.doi.org/10.1016/j.ijforecast.2003.09.002.

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8

Santías, Francisco Reyes, Carmen Cadarso-Suárez, and María Xosé Rodríguez-Álvarez. "Estimating hospital production functions through flexible regression models." Mathematical and Computer Modelling 54, no. 7-8 (2011): 1760–64. http://dx.doi.org/10.1016/j.mcm.2010.11.087.

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9

da Silva, Nívea B., Marcos O. Prates, and Flávio B. Gonçalves. "Bayesian linear regression models with flexible error distributions." Journal of Statistical Computation and Simulation 90, no. 14 (2020): 2571–91. http://dx.doi.org/10.1080/00949655.2020.1783261.

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10

Shaw, J. E. H. "Numerical Bayesian Analysis of Some Flexible Regression Models." Statistician 36, no. 2/3 (1987): 147. http://dx.doi.org/10.2307/2348507.

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Saldaña-Zepeda, Dayna P., Ciro Velasco-Cruz, and Víctor H. Torres-Preciado. "Variable Selection in Switching Dynamic Regression Models." Revista Colombiana de Estadística 45, no. 1 (2022): 231–63. http://dx.doi.org/10.15446/rce.v45n1.85385.

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Complex dynamic phenomena in which dynamics is related to events (modes) that cause structural changes over time, are well described by the switching linear dynamical system (SLDS). We extend the SLDS by allowing the measurement noise to be mode-specific, a flexible way to model non stationary data. Additionally, for models that are functions of explanatory variables, we adapt a variable selection method to identify which of them are significant in each mode. Our proposed model is a flexible Bayesian nonparametric model that allows to learn about the number of modes and their location, and wit

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Bonat, Wagner H., Ricardo R. Petterle, John Hinde, and Clarice GB Demétrio. "Flexible quasi-beta regression models for continuous bounded data." Statistical Modelling 19, no. 6 (2018): 617–33. http://dx.doi.org/10.1177/1471082x18790847.

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We propose a flexible class of regression models for continuous bounded data based on second-moment assumptions. The mean structure is modelled by means of a link function and a linear predictor, while the mean and variance relationship has the form [Formula: see text], where [Formula: see text], [Formula: see text] and [Formula: see text] are the mean, dispersion and power parameters respectively. The models are fitted by using an estimating function approach where the quasi-score and Pearson estimating functions are employed for the estimation of the regression and dispersion parameters resp

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13

Su, Steve. "Fitting Flexible Parametric Regression Models with GLDreg in R." Journal of Modern Applied Statistical Methods 15, no. 2 (2016): 768–87. http://dx.doi.org/10.22237/jmasm/1478004240.

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14

Abd El-Monsef, Mohamed, Elhoussainy Rady, and Ayat Sobhy. "WEIBULL SEMIPARAMETRIC REGRESSION MODELS UNDER RANDOM CENSORSHIP." JOURNAL OF ADVANCES IN MATHEMATICS 11, no. 8 (2015): 5577–82. http://dx.doi.org/10.24297/jam.v11i8.1209.

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Semiparametric regression is concerned with the flexible combination of non-linear functional relationships in regression analysis. The main advantage of the semiparametric regression models is that any application benefits from regression analysis can also benefit from the semiparametric regression. In this paper, we derived a consistent estimator of parametric portion and nonparametric portion in Weibull semi-parametric regression models under random censorship.

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Branscum, Adam J., Wesley O. Johnson, and Andre T. Baron. "Robust Medical Test Evaluation Using Flexible Bayesian Semiparametric Regression Models." Epidemiology Research International 2013 (December 11, 2013): 1–8. http://dx.doi.org/10.1155/2013/131232.

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The application of Bayesian methods is increasing in modern epidemiology. Although parametric Bayesian analysis has penetrated the population health sciences, flexible nonparametric Bayesian methods have received less attention. A goal in nonparametric Bayesian analysis is to estimate unknown functions (e.g., density or distribution functions) rather than scalar parameters (e.g., means or proportions). For instance, ROC curves are obtained from the distribution functions corresponding to continuous biomarker data taken from healthy and diseased populations. Standard parametric approaches to Ba

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16

Prasetyo, Rindang Bangun, Heri Kuswanto, Nur Iriawan, and Brodjol Sutijo Suprih Ulama. "Binomial Regression Models with a Flexible Generalized Logit Link Function." Symmetry 12, no. 2 (2020): 221. http://dx.doi.org/10.3390/sym12020221.

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In binomial regression, a link function is used to join the linear predictor variables and the expectation of the response variable. This paper proposes a flexible link function from a new class of generalized logistic distribution, namely a flexible generalized logit (glogit) link. This approach considers both symmetric and asymmetric models, including the cases of lighter and heavier tails, as compared to standard logistic. The glogit is created from the inverse cumulative distribution function of the exponentiated-exponential logistic (EEL) distribution. Using a Bayesian framework, we condu

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Gonçalves, Jussiane Nader, and Wagner Barreto-Souza. "Flexible regression models for counts with high-inflation of zeros." METRON 78, no. 1 (2020): 71–95. http://dx.doi.org/10.1007/s40300-020-00163-9.

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18

Greven, Sonja, and Fabian Scheipl. "A general framework for functional regression modelling." Statistical Modelling 17, no. 1-2 (2017): 1–35. http://dx.doi.org/10.1177/1471082x16681317.

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Researchers are increasingly interested in regression models for functional data. This article discusses a comprehensive framework for additive (mixed) models for functional responses and/or functional covariates based on the guiding principle of reframing functional regression in terms of corresponding models for scalar data, allowing the adaptation of a large body of existing methods for these novel tasks. The framework encompasses many existing as well as new models. It includes regression for ‘generalized’ functional data, mean regression, quantile regression as well as generalized additiv

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19

Hogg, David W., and Soledad Villar. "Fitting Very Flexible Models: Linear Regression With Large Numbers of Parameters." Publications of the Astronomical Society of the Pacific 133, no. 1027 (2021): 093001. http://dx.doi.org/10.1088/1538-3873/ac20ac.

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20

Jung, Yu Jin, and Yong Ik Yoon. "Study on abnormal behavior prediction models using flexible multi-level regression." Journal of the Korean Data and Information Science Society 27, no. 1 (2016): 1–8. http://dx.doi.org/10.7465/jkdi.2016.27.1.1.

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Freitas, João, Juvêncio Nobre, and Caio Azevedo. "Flexible quasi-beta prime regression models for dependent continuous positive data." Statistics and Its Interface 17, no. 4 (2024): 715–31. http://dx.doi.org/10.4310/22-sii762.

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Marschner, I. C., and A. C. Gillett. "Relative risk regression: reliable and flexible methods for log-binomial models." Biostatistics 13, no. 1 (2011): 179–92. http://dx.doi.org/10.1093/biostatistics/kxr030.

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23

Muñoz Barús, José Ignacio, Manuel Febrero-Bande, and Carmen Cadarso-Suárez. "Flexible regression models for estimating postmortem interval (PMI) in forensic medicine." Statistics in Medicine 27, no. 24 (2008): 5026–38. http://dx.doi.org/10.1002/sim.3319.

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24

Pitha, J., I. Podrapska, R. Poledne, and Z. Valenta. "Gaining Insight from Flexible Models." Methods of Information in Medicine 45, no. 02 (2006): 186–90. http://dx.doi.org/10.1055/s-0038-1634065.

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Summary Objectives: We present results from a secondary prevention trial of coronary heart disease (CHD) in the Czech male population from northern Bohemia with the history of myocardial infarction (MI) and high prevalence of metabolic syndrome. We compare several approaches to analyzing survival data from our study in terms of respective model assumptions. Methods: While both the Cox and Weibull survival regression models assume proportionality of the hazard functions over time, in many instances this assumption appears incompatible with the data at hand. Gray’s implementation of flexible mod

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M. Hashimoto, Elizabeth, Gauss M. Cordeiro, Edwin M. M. Ortega, and G. G. Hamedani. "New Flexible Regression Models Generated by Gamma Random Variables with Censored Data." International Journal of Statistics and Probability 5, no. 3 (2016): 9. http://dx.doi.org/10.5539/ijsp.v5n3p9.

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We propose and study a new log-gamma Weibull regression model. We obtain explicit expressions for the raw and incomplete moments, quantile and generating functions and mean deviations of the log-gamma Weibull distribution. We demonstrate that the new regression model can be applied to censored data since it represents a parametric family of models which includes as sub-models several widely-known regression models and therefore can be used more effectively in the analysis of survival data. We obtain the maximum likelihood estimates of the model parameters by considering censored data and evalu

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Marra, Giampiero, and Rosalba Radice. "A joint regression modeling framework for analyzing bivariate binary data in R." Dependence Modeling 5, no. 1 (2017): 268–94. http://dx.doi.org/10.1515/demo-2017-0016.

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Abstract We discuss some of the features of the R add-on package GJRM which implements a flexible joint modeling framework for fitting a number of multivariate response regression models under various sampling schemes. In particular,we focus on the case inwhich the user wishes to fit bivariate binary regression models in the presence of several forms of selection bias. The framework allows for Gaussian and non-Gaussian dependencies through the use of copulae, and for the association and mean parameters to depend on flexible functions of covariates. We describe some of the methodological detail

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27

Hubin, Aliaksandr, Geir Storvik, and Florian Frommlet. "Flexible Bayesian Nonlinear Model Configuration." Journal of Artificial Intelligence Research 72 (November 22, 2021): 901–42. http://dx.doi.org/10.1613/jair.1.13047.

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Regression models are used in a wide range of applications providing a powerful scientific tool for researchers from different fields. Linear, or simple parametric, models are often not sufficient to describe complex relationships between input variables and a response. Such relationships can be better described through flexible approaches such as neural networks, but this results in less interpretable models and potential overfitting. Alternatively, specific parametric nonlinear functions can be used, but the specification of such functions is in general complicated. In this paper, we introdu

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28

Lang, Moritz N., Georg J. Mayr, Reto Stauffer, and Achim Zeileis. "Bivariate Gaussian models for wind vectors in a distributional regression framework." Advances in Statistical Climatology, Meteorology and Oceanography 5, no. 2 (2019): 115–32. http://dx.doi.org/10.5194/ascmo-5-115-2019.

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Abstract. A new probabilistic post-processing method for wind vectors is presented in a distributional regression framework employing the bivariate Gaussian distribution. In contrast to previous studies, all parameters of the distribution are simultaneously modeled, namely the location and scale parameters for both wind components and also the correlation coefficient between them employing flexible regression splines. To capture a possible mismatch between the predicted and observed wind direction, ensemble forecasts of both wind components are included using flexible two-dimensional smooth fu

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29

Pei, Eddie, and Ernest Fokoué. "Integrating Graph Structures into Kernel Regression Models." Serdica Journal of Computing 18, no. 1 (2024): 1–25. http://dx.doi.org/10.55630/sjc.2024.18.1-25.

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Kernel methods are highly flexible and powerful tools for capturing complex, nonlinear relationships in data. In this paper, we propose a substantial extension of existing network-based regression models by integrating kernel methods with graph-theoretic constraints. Our approach builds upon the foundational work of Li et al. [1], who incorporated network cohesion into generalized linear models (GLMs). We extend their framework by introducing a kernelized regression model that allows for the modeling of nonlinear interactions while leveraging network data. This kernelized framework relaxes the

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30

Umlauf, Nikolaus, and Thomas Kneib. "A primer on Bayesian distributional regression." Statistical Modelling 18, no. 3-4 (2018): 219–47. http://dx.doi.org/10.1177/1471082x18759140.

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Abstract: Bayesian methods have become increasingly popular in the past two decades. With the constant rise of computational power, even very complex models can be estimated on virtually any modern computer. Moreover, interest has shifted from conditional mean models to probabilistic distributional models capturing location, scale, shape and other aspects of a response distribution, where covariate effects can have flexible forms, for example, linear, non-linear, spatial or random effects. This tutorial article discusses how to select models in the Bayesian distributional regression setting, h

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31

Koochemeshkian, Pantea, Nuha Zamzami, and Nizar Bouguila. "Flexible Distribution-Based Regression Models for Count Data: Application to Medical Diagnosis." Cybernetics and Systems 51, no. 4 (2020): 442–66. http://dx.doi.org/10.1080/01969722.2020.1758464.

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32

Alfò, Marco, and Irene Rocchetti. "A flexible approach to finite mixture regression models for multivariate mixed responses." Statistics & Probability Letters 83, no. 7 (2013): 1754–58. http://dx.doi.org/10.1016/j.spl.2013.04.004.

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33

Kim, Sung-Hee, and Nakseok Kim. "Development of performance prediction models in flexible pavement using regression analysis method." KSCE Journal of Civil Engineering 10, no. 2 (2006): 91–96. http://dx.doi.org/10.1007/bf02823926.

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34

Robinson, Andrew P., Stephen E. Lane, and Guillaume Thérien. "Fitting forestry models using generalized additive models: a taper model example." Canadian Journal of Forest Research 41, no. 10 (2011): 1909–16. http://dx.doi.org/10.1139/x11-095.

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Nonparametric and semiparametric modelling methods are commonly applied in many fields. However, such methods have not been widely adopted in forestry, other than the most similar neighbour and nearest neighbor methods. Generalized additive modelling is a flexible semiparametric regression method that is useful when model-based prediction is the main goal and the parametric form of the model is unknown and possibly complex. Routines to fit generalized additive models (GAMs) are now readily available in much statistical software, making them an attractive option for forest modelling. Here, the

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Abed, Muataz Safaa. "Development of Regression Models for Predicting Pavement Condition Index from the International Roughness Index." Journal of Engineering 26, no. 12 (2020): 81–94. http://dx.doi.org/10.31026/j.eng.2020.12.05.

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Flexible pavements are considered an essential element of transportation infrastructure. So, evaluations of flexible pavement performance are necessary for the proper management of transportation infrastructure. Pavement condition index (PCI) and international roughness index (IRI) are common indices applied to evaluate pavement surface conditions. However, the pavement condition surveys to calculate PCI are costly and time-consuming as compared to IRI. This article focuses on developing regression models that predict PCI from IRI. Eighty-three flexible pavement sections, with section length e

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Makendran, C., R. Murugasan, and S. Velmurugan. "Performance Prediction Modelling for Flexible Pavement on Low Volume Roads Using Multiple Linear Regression Analysis." Journal of Applied Mathematics 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/192485.

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Prediction models for low volume village roads in India are developed to evaluate the progression of different types of distress such as roughness, cracking, and potholes. Even though the Government of India is investing huge quantum of money on road construction every year, poor control over the quality of road construction and its subsequent maintenance is leading to the faster road deterioration. In this regard, it is essential that scientific maintenance procedures are to be evolved on the basis of performance of low volume flexible pavements. Considering the above, an attempt has been mad

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37

Morris, Darcy Steeg, and Kimberly F. Sellers. "A Flexible Mixed Model for Clustered Count Data." Stats 5, no. 1 (2022): 52–69. http://dx.doi.org/10.3390/stats5010004.

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Clustered count data are commonly modeled using Poisson regression with random effects to account for the correlation induced by clustering. The Poisson mixed model allows for overdispersion via the nature of the within-cluster correlation, however, departures from equi-dispersion may also exist due to the underlying count process mechanism. We study the cross-sectional COM-Poisson regression model—a generalized regression model for count data in light of data dispersion—together with random effects for analysis of clustered count data. We demonstrate model flexibility of the COM-Poisson rando

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38

Simon, Thorsten, Georg J. Mayr, Nikolaus Umlauf, and Achim Zeileis. "NWP-based lightning prediction using flexible count data regression." Advances in Statistical Climatology, Meteorology and Oceanography 5, no. 1 (2019): 1–16. http://dx.doi.org/10.5194/ascmo-5-1-2019.

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Abstract. A method to predict lightning by postprocessing numerical weather prediction (NWP) output is developed for the region of the European Eastern Alps. Cloud-to-ground (CG) flashes – detected by the ground-based Austrian Lightning Detection &amp; Information System (ALDIS) network – are counted on the 18×18 km2 grid of the 51-member NWP ensemble of the European Centre for Medium-Range Weather Forecasts (ECMWF). These counts serve as the target quantity in count data regression models for the occurrence of lightning events and flash counts of CG. The probability of lightning occurrenc

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39

Zhang, Jing, and Hong Xia Guo. "Statistical Inference and Application for Partially Linear Models." Applied Mechanics and Materials 733 (February 2015): 910–13. http://dx.doi.org/10.4028/www.scientific.net/amm.733.910.

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As partially linear regression model contains parameters part and the nonparametric part, it is better than the linear model. Partially linear regression model is more freedom, flexible, and can seize the characteristics of data. This passage first reduces the dimension of expenditure index data using principal component analysis. Then based on the dimension-reduced data, a partial linear model is established to forecast expenditure on army. The results show a great advantage over those by stepwise linear regression analysis.

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Li, Shuwei, Tao Hu, Tiejun Tong, and Jianguo Sun. "Semiparametric regression analysis of multivariate doubly censored data." Statistical Modelling 20, no. 5 (2019): 502–26. http://dx.doi.org/10.1177/1471082x19859949.

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This article discusses regression analysis of multivariate doubly censored data with a wide class of flexible semiparametric transformation frailty models. The proposed models include many commonly used regression models as special cases such as the proportional hazards and proportional odds frailty models. For inference, we propose a nonparametric maximum likelihood estimation method and develop a new expectation–maximization algorithm for its implementation. The proposed estimators of the finite-dimensional parameters are shown to be consistent, asymptotically normal and semiparametrically e

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41

Cremers, Jolien, Tim Mainhard, and Irene Klugkist. "Assessing a Bayesian Embedding Approach to Circular Regression Models." Methodology 14, no. 2 (2018): 69–81. http://dx.doi.org/10.1027/1614-2241/a000147.

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Abstract. Circular data is different from linear data and its analysis also requires methods different from conventional methods. In this study a Bayesian embedding approach to estimating circular regression models is investigated, by means of simulation studies, in terms of performance, efficiency, and flexibility. A new Markov chain Monte Carlo (MCMC) sampling method is proposed and contrasted to an existing method. An empirical example of a regression model predicting teachers’ scores on the interpersonal circumplex will be used throughout. Performance and efficiency are better for the newl

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42

Bermúdez, Lluís, and Dimitris Karlis. "Multivariate INAR(1) Regression Models Based on the Sarmanov Distribution." Mathematics 9, no. 5 (2021): 505. http://dx.doi.org/10.3390/math9050505.

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A multivariate INAR(1) regression model based on the Sarmanov distribution is proposed for modelling claim counts from an automobile insurance contract with different types of coverage. The correlation between claims from different coverage types is considered jointly with the serial correlation between the observations of the same policyholder observed over time. Several models based on the multivariate Sarmanov distribution are analyzed. The new models offer some advantages since they have all the advantages of the MINAR(1) regression model but allow for a more flexible dependence structure

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43

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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44

Abdelbaset, A.Sh Abdalla, Ibrahim Elmesmari Nasir, and M. Elnazzal Mohammed. "Bayesian inference approach for a mixture of normal regression models." World Journal of Advanced Research and Reviews 24, no. 3 (2024): 214–27. https://doi.org/10.5281/zenodo.15165842.

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Mixture distributions are widely used to model data with distinct groups, providing a flexible approach to estimating density. However, Bayesian approaches for mixture models pose challenges, such as label switching in the Gibbs sampler output due to the non-identifiability of component parameters. We review advanced methods for Bayesian analysis, including the Markov chain Monte Carlo (MCMC) reversible jump algorithm and model comparison based on joint measures of fit and complexity. We also present a Bayesian regression model based on a two-component mixture model, implemented using the Gibb

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45

Fradi, Anis, Tien-Tam Tran, and Chafik Samir. "Decomposed Gaussian Processes for Efficient Regression Models with Low Complexity." Entropy 27, no. 4 (2025): 393. https://doi.org/10.3390/e27040393.

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In this paper, we address the challenges of inferring and learning from a substantial number of observations (N≫1) with a Gaussian process regression model. First, we propose a flexible construction of well-adapted covariances originally derived from specific differential operators. Second, we prove its convergence and show its low computational cost scaling as O(Nm2) for inference and O(m3) for learning instead of O(N3) for a canonical Gaussian process where N≫m. Moreover, we develop an implementation that requires less memory O(m2) instead of O(N2). Finally, we demonstrate the effectiveness

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46

Roquim, Fernanda V., Thiago G. Ramires, Luiz R. Nakamura, Ana J. Righetto, Renato R. Lima, and Rayne A. Gomes. "Building flexible regression models: including the Birnbaum-Saunders distribution in the gamlss package." Semina: Ciências Exatas e Tecnológicas 42, no. 2 (2021): 163. http://dx.doi.org/10.5433/1679-0375.2021v42n2p163.

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Generalized additive models for location, scale and shape (GAMLSS) are a very flexible statistical modeling framework, being an important generalization of the well-known generalized linear models and generalized additive models. Their main advantage is that any probability distribution (that does not necessarily belong to the exponential family) can be considered to model the response variable and different regression structures can be fitted in each of its parameters. Currently, there are more than 100 distributions that are already implemented in the gamlss package in R software. Neverthele

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Rubio-Herrero, Javier, and Yuchen Wang. "A flexible rolling regression framework for the identification of time-varying SIRD models." Computers & Industrial Engineering 167 (May 2022): 108003. http://dx.doi.org/10.1016/j.cie.2022.108003.

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48

Lu, Yang. "Flexible (panel) regression models for bivariate count–continuous data with an insurance application." Journal of the Royal Statistical Society: Series A (Statistics in Society) 182, no. 4 (2019): 1503–21. http://dx.doi.org/10.1111/rssa.12470.

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49

Brockhaus, Sarah, Michael Melcher, Friedrich Leisch, and Sonja Greven. "Boosting flexible functional regression models with a high number of functional historical effects." Statistics and Computing 27, no. 4 (2016): 913–26. http://dx.doi.org/10.1007/s11222-016-9662-1.

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50

Espasandín‐Domínguez, J., C. Cadarso‐Suárez, T. Kneib, et al. "Assessing the relationship between markers of glycemic control through flexible copula regression models." Statistics in Medicine 38, no. 27 (2019): 5161–81. http://dx.doi.org/10.1002/sim.8358.

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