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Journal articles on the topic 'Parametric regression models'

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Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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1

Liebscher, Eckhard. "Model checks for parametric regression models." TEST 21, no. 1 (March 2, 2011): 132–55. http://dx.doi.org/10.1007/s11749-011-0239-1.

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2

Nygård Johansen, Martin, Søren Lundbye‐Christensen, and Erik Thorlund Parner. "Regression models using parametric pseudo‐observations." Statistics in Medicine 39, no. 22 (June 10, 2020): 2949–61. http://dx.doi.org/10.1002/sim.8586.

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3

Neumeyer, Natalie, Leonie Selk, and Charles Tillier. "Semi-parametric transformation boundary regression models." Annals of the Institute of Statistical Mathematics 72, no. 6 (September 21, 2019): 1287–315. http://dx.doi.org/10.1007/s10463-019-00731-5.

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4

LaCour-Little, Michael, Michael Marschoun, and Clark Maxam. "Improving Parametric Mortgage Prepayment Models with Non-parametric Kernel Regression." Journal of Real Estate Research 24, no. 3 (January 1, 2002): 299–328. http://dx.doi.org/10.1080/10835547.2002.12091098.

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5

Bottai, Matteo, and Giovanna Cilluffo. "Nonlinear parametric quantile models." Statistical Methods in Medical Research 29, no. 12 (July 19, 2020): 3757–69. http://dx.doi.org/10.1177/0962280220941159.

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Quantile regression is widely used to estimate conditional quantiles of an outcome variable of interest given covariates. This method can estimate one quantile at a time without imposing any constraints on the quantile process other than the linear combination of covariates and parameters specified by the regression model. While this is a flexible modeling tool, it generally yields erratic estimates of conditional quantiles and regression coefficients. Recently, parametric models for the regression coefficients have been proposed that can help balance bias and sampling variability. So far, however, only models that are linear in the parameters and covariates have been explored. This paper presents the general case of nonlinear parametric quantile models. These can be nonlinear with respect to the parameters, the covariates, or both. Some important features and asymptotic properties of the proposed estimator are described, and its finite-sample behavior is assessed in a simulation study. Nonlinear parametric quantile models are applied to estimate extreme quantiles of longitudinal measures of respiratory mechanics in asthmatic children from an epidemiological study and to evaluate a dose–response relationship in a toxicological laboratory experiment.
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6

Mahmoud, Hamdy F. F. "Parametric Versus Semi and Nonparametric Regression Models." International Journal of Statistics and Probability 10, no. 2 (February 23, 2021): 90. http://dx.doi.org/10.5539/ijsp.v10n2p90.

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There are three common types of regression models: parametric, semiparametric and nonparametric regression. The model should be used to fit the real data depends on how much information is available about the form of the relationship between the response variable and explanatory variables, and the random error distribution that is assumed. Researchers need to be familiar with each modeling approach requirements. In this paper, differences between these models, common estimation methods, robust estimation, and applications are introduced. For parametric models, there are many known methods of estimation, such as least squares and maximum likelihood methods which are extensively studied but they require strong assumptions. On the other hand, nonparametric regression models are free of assumptions regarding the form of the response-explanatory variables relationships but estimation methods, such as kernel and spline smoothing are computationally expensive and smoothing parameters need to be obtained. For kernel smoothing there two common estimators: local constant and local linear smoothing methods. In terms of bias, especially at the boundaries of the data range, local linear is better than local constant estimator.  Robust estimation methods for linear models are well studied, however the robust estimation methods in nonparametric regression methods are limited. A robust estimation method for the semiparametric and nonparametric regression models is introduced.
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7

Mulayath Variyath, Asokan, and P. G. Sankaran. "Parametric Regression Models Using Reversed Hazard Rates." Journal of Probability and Statistics 2014 (2014): 1–5. http://dx.doi.org/10.1155/2014/645719.

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Proportional hazard regression models are widely used in survival analysis to understand and exploit the relationship between survival time and covariates. For left censored survival times, reversed hazard rate functions are more appropriate. In this paper, we develop a parametric proportional hazard rates model using an inverted Weibull distribution. The estimation and construction of confidence intervals for the parameters are discussed. We assess the performance of the proposed procedure based on a large number of Monte Carlo simulations. We illustrate the proposed method using a real case example.
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8

GarcÍa-Portugués, Eduardo, Ingrid Van Keilegom, Rosa M. Crujeiras and, and Wenceslao González-Manteiga. "Testing parametric models in linear-directional regression." Scandinavian Journal of Statistics 43, no. 4 (August 12, 2016): 1178–91. http://dx.doi.org/10.1111/sjos.12236.

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9

Doveh, E., A. Shapiro, and P. D. Feigin. "Testing of monotonicity in parametric regression models." Journal of Statistical Planning and Inference 107, no. 1-2 (September 2002): 289–306. http://dx.doi.org/10.1016/s0378-3758(02)00259-8.

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10

Gao, Jiti. "PARAMETRIC TEST IN PARTIAL LINEAR REGRESSION MODELS." Acta Mathematica Scientia 15 (1995): 1–10. http://dx.doi.org/10.1016/s0252-9602(17)30758-0.

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11

Abd El-Monsef, Mohamed, Elhoussainy Rady, and Ayat Sobhy. "WEIBULL SEMIPARAMETRIC REGRESSION MODELS UNDER RANDOM CENSORSHIP." JOURNAL OF ADVANCES IN MATHEMATICS 11, no. 8 (December 22, 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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12

Gallo, Mariano, Vittorio Marzano, and Fulvio Simonelli. "Empirical Comparison of Parametric and Nonparametric Trade Gravity Models." Transportation Research Record: Journal of the Transportation Research Board 2269, no. 1 (January 2012): 29–41. http://dx.doi.org/10.3141/2269-04.

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A systematic comparison is made of parametric (i.e., ordinary least-squares regressions and related generalizations) and nonparametric (i.e., kernel regressions and regression trees) log-linear gravity models for reproducing international trade. Experiments were conducted to estimate a log-linear gravity model reproducing import and export trade flows in quantity between Italy and 13 world economic zones, based on a panel estimation data set. The best parametric regression model was estimated to define a baseline reference model. Some specifications of nonparametric models, belonging to the categories of kernel regressions and regression trees, were also estimated. The performance of parametric and nonparametric models is contrasted through a comparison of goodness-of-fit measures (R2, mean absolute percentage error) both in estimation and in hold-out sample validation. To assess the differences in model elasticity and forecasts, both parametric and nonparametric models are applied to future scenarios and the corresponding results compared.
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13

Mahaboob, B., B. Venkateswarlu, C. Narayana, J. Ravi sankar, and P. Balasiddamuni. "A Monograph on Nonlinear Regression Models." International Journal of Engineering & Technology 7, no. 4.10 (October 2, 2018): 543. http://dx.doi.org/10.14419/ijet.v7i4.10.21277.

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This research article uses Matrix Calculus techniques to study least squares application of nonlinear regression model, sampling distributions of nonlinear least squares estimators of regression parametric vector and error variance and testing of general nonlinear hypothesis on parameters of nonlinear regression model. Arthipova Irina et.al [1], in this paper, discussed some examples of different nonlinear models and the application of OLS (Ordinary Least Squares). MA Tabati et.al (2), proposed a robust alternative technique to OLS nonlinear regression method which provide accurate parameter estimates when outliers and/or influential observations are present. Xu Zheng et.al [3] presented new parametric tests for heteroscedasticity in nonlinear and nonparametric models.
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14

Trzpiot, Grażyna. "Quantile Non‑parametric Additive Models." Acta Universitatis Lodziensis. Folia Oeconomica 6, no. 345 (December 30, 2019): 127–39. http://dx.doi.org/10.18778/0208-6018.345.07.

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Quantile regression allows us to assess different possible impacts of covariates on different quantiles of a response variable. Additive models for quantile functions provide an attractive framework for non‑parametric regression applications focused on functions of the response instead of its central tendency. Total variation smoothing penalties can be used to control the smoothness of additive components. We write down a general approach to estimation and inference for additive models of this type. Quantile regression as a risk measure has been applied in sector portfolio analysis for a data set from the Warsaw Stock Exchange.
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15

Dong, Alice X. D., Jennifer S. K. Chan, and Gareth W. Peters. "RISK MARGIN QUANTILE FUNCTION VIA PARAMETRIC AND NON-PARAMETRIC BAYESIAN APPROACHES." ASTIN Bulletin 45, no. 3 (July 9, 2015): 503–50. http://dx.doi.org/10.1017/asb.2015.8.

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AbstractWe develop quantile functions from regression models in order to derive risk margin and to evaluate capital in non-life insurance applications. By utilizing the entire range of conditional quantile functions, especially higher quantile levels, we detail how quantile regression is capable of providing an accurate estimation of risk margin and an overview of implied capital based on the historical volatility of a general insurers loss portfolio. Two modeling frameworks are considered based around parametric and non-parametric regression models which we develop specifically in this insurance setting. In the parametric framework, quantile functions are derived using several distributions including the flexible generalized beta (GB2) distribution family, asymmetric Laplace (AL) distribution and power-Pareto (PP) distribution. In these parametric model based quantile regressions, we detail two basic formulations. The first involves embedding the quantile regression loss function from the nonparameteric setting into the argument of the kernel of a parametric data likelihood model, this is well known to naturally lead to the AL parametric model case. The second formulation we utilize in the parametric setting adopts an alternative quantile regression formulation in which we assume a structural expression for the regression trend and volatility functions which act to modify a base quantile function in order to produce the conditional data quantile function. This second approach allows a range of flexible parametric models to be considered with different tail behaviors. We demonstrate how to perform estimation of the resulting parametric models under a Bayesian regression framework. To achieve this, we design Markov chain Monte Carlo (MCMC) sampling strategies for the resulting Bayesian posterior quantile regression models. In the non-parametric framework, we construct quantile functions by minimizing an asymmetrically weighted loss function and estimate the parameters under the AL proxy distribution to resemble the minimization process. This quantile regression model is contrasted to the parametric AL mean regression model and both are expressed as a scale mixture of uniform distributions to facilitate efficient implementation. The models are extended to adopt dynamic mean, variance and skewness and applied to analyze two real loss reserve data sets to perform inference and discuss interesting features of quantile regression for risk margin calculations.
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16

Green, Peter J. "Penalized Likelihood for General Semi-Parametric Regression Models." International Statistical Review / Revue Internationale de Statistique 55, no. 3 (December 1987): 245. http://dx.doi.org/10.2307/1403404.

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17

Djaballah-Djeddour, Khedidja, and Moussa Tazerouti. "Test for Linearity in Non-Parametric Regression Models." Austrian Journal of Statistics 51, no. 1 (January 24, 2022): 16–34. http://dx.doi.org/10.17713/ajs.v51i1.1047.

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The problem of checking the linearity of a regression relationship is addressed. The test uses nonparametric estimation techniques. The null hypothesis is that the regression function is linear; it is tested against the non-specic alternatives hypotheses. This test is based on a Hermite transform characterization of conditional expectations. A statistical test is derived, the distribution of this statisticunder the null hypothesis of linearity is determined. A power study using simulation shows the new statistic to be more sensitive to non-linearity.
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18

Fan, Jianqing, and Li-Shan Huang. "Goodness-of-Fit Tests for Parametric Regression Models." Journal of the American Statistical Association 96, no. 454 (June 2001): 640–52. http://dx.doi.org/10.1198/016214501753168316.

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19

Zhang, Yi, and Qingle Zheng. "Non parametric mixture of strictly monotone regression models." Communications in Statistics - Theory and Methods 47, no. 2 (September 8, 2017): 415–26. http://dx.doi.org/10.1080/03610926.2017.1303730.

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20

Zhou, Xiao-Hua, and Hua Liang. "Semi-parametric single-index two-part regression models." Computational Statistics & Data Analysis 50, no. 5 (March 2006): 1378–90. http://dx.doi.org/10.1016/j.csda.2004.12.001.

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21

Durio, Alessandra, and Ennio Davide Isaia. "Bivariate non-parametric regression models: simulations and applications." Applied Stochastic Models in Business and Industry 20, no. 3 (July 2004): 291–303. http://dx.doi.org/10.1002/asmb.527.

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22

Piegorsch, Walter W., and George Casella. "Empirical Bayes Estimation for Logistic Regression and Extended Parametric Regression Models." Journal of Agricultural, Biological, and Environmental Statistics 1, no. 2 (June 1996): 231. http://dx.doi.org/10.2307/1400367.

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23

Fernández, J. M. Vilar, and W. González Manteiga. "Resampling for checking linear regression models via non-parametric regression estimation." Computational Statistics & Data Analysis 35, no. 2 (December 2000): 211–31. http://dx.doi.org/10.1016/s0167-9473(99)00117-6.

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24

Brown, Scott, and Andrew Heathcote. "On the Use of Nonparametric Regression in Assessing Parametric Regression Models." Journal of Mathematical Psychology 46, no. 6 (December 2002): 716–30. http://dx.doi.org/10.1006/jmps.2002.1421.

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25

Kuželka, K., and R. Marušák. "Use of nonparametric regression methods for developing a local stem form model." Journal of Forest Science 60, No. 11 (November 14, 2014): 464–71. http://dx.doi.org/10.17221/56/2014-jfs.

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A local mean stem curve of spruce was represented using regression splines. Abilities of smoothing spline and P-spline to model the mean stem curve were evaluated using data of 85 carefully measured stems of Norway spruce. For both techniques the optimal amount of smoothing was investigated in dependence on the number of training stems using a cross-validation method. Representatives of main groups of parametric models – single models, segmented models and models with variable coefficient – were compared with spline models using five statistic criteria. Both regression splines performed comparably or better as all representatives of parametric models independently of the numbers of stems used as training data.  
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26

Akritas, Michael G. "On the Use of Nonparametric Regression Techniques for Fitting Parametric Regression Models." Biometrics 52, no. 4 (December 1996): 1342. http://dx.doi.org/10.2307/2532849.

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27

Ayub, Kanwal, Weixing Song, and Jianhong Shi. "Extrapolation estimation in parametric regression models with measurement error." Computational Statistics & Data Analysis 172 (August 2022): 107478. http://dx.doi.org/10.1016/j.csda.2022.107478.

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28

Su, Steve. "Fitting Flexible Parametric Regression Models with GLDreg in R." Journal of Modern Applied Statistical Methods 15, no. 2 (November 1, 2016): 768–87. http://dx.doi.org/10.22237/jmasm/1478004240.

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29

Ecochard, René, and David G. Clayton. "Multivariate Parametric Random Effect Regression Models for Fecundability Studies." Biometrics 56, no. 4 (December 2000): 1023–29. http://dx.doi.org/10.1111/j.0006-341x.2000.01023.x.

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30

CHEN, SONGNIAN. "Non-Parametric Identification and Estimation of Truncated Regression Models." Review of Economic Studies 77, no. 1 (May 15, 2009): 127–53. http://dx.doi.org/10.1111/j.1467-937x.2009.00572.x.

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31

GONZÁLEZ-MANTEIGA, WENCESLAO, JUAN CARLOS PARDO-FERNÁNDEZ, and INGRID VAN KEILEGOM. "ROC Curves in Non-Parametric Location-Scale Regression Models." Scandinavian Journal of Statistics 38, no. 1 (April 16, 2010): 169–84. http://dx.doi.org/10.1111/j.1467-9469.2010.00693.x.

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32

Hlávka, Zdeněk, Marie Hušková, and Simos G. Meintanis. "Tests for independence in non-parametric heteroscedastic regression models." Journal of Multivariate Analysis 102, no. 4 (April 2011): 816–27. http://dx.doi.org/10.1016/j.jmva.2011.01.002.

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33

Otte, Clemens. "Interpretable semi-parametric regression models with defined error bounds." Neurocomputing 143 (November 2014): 1–6. http://dx.doi.org/10.1016/j.neucom.2013.11.042.

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34

Czado, Claudia. "Bayesian inference of binary regression models with parametric link." Journal of Statistical Planning and Inference 41, no. 2 (September 1994): 121–40. http://dx.doi.org/10.1016/0378-3758(94)90158-9.

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35

Wang, Jing. "Bayesian quantile regression for parametric nonlinear mixed effects models." Statistical Methods & Applications 21, no. 3 (March 16, 2012): 279–95. http://dx.doi.org/10.1007/s10260-012-0190-7.

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36

Fernandez, Luis. "Non-parametric maximum likelihood estimation of censored regression models." Journal of Econometrics 32, no. 1 (June 1986): 35–57. http://dx.doi.org/10.1016/0304-4076(86)90011-4.

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37

Tian, Lili, Changxing Ma, and Albert Vexler. "A Parametric Bootstrap Test for Comparing Heteroscedastic Regression Models." Communications in Statistics - Simulation and Computation 38, no. 5 (February 25, 2009): 1026–36. http://dx.doi.org/10.1080/03610910902737077.

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38

Zheng, John Xu. "A CONSISTENT NONPARAMETRIC TEST OF PARAMETRIC REGRESSION MODELS UNDER CONDITIONAL QUANTILE RESTRICTIONS." Econometric Theory 14, no. 1 (February 1998): 123–38. http://dx.doi.org/10.1017/s0266466698141051.

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This paper proposes a nonparametric, kernel-based test of parametric quantile regression models. The test statistic has a limiting standard normal distribution if the parametric quantile model is correctly specified and diverges to infinity for any misspecification of the parametric model. Thus the test is consistent against any fixed alternative. The test also has asymptotic power 1 against local alternatives converging to the null at proper rates. A simulation study is provided to evaluate the finite-sample performance of the test.
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39

Hashemian, A. H., M. Garshasbi, M. A. Pourhoseingholi, and S. Eskandari. "A Comparative Study of Cox Regression vs. Log-Logistic Regression (with and without its frailty) in Estimating Survival Time of Patients with Colorectal Cancer." Journal of Medical and Biomedical Sciences 6, no. 1 (June 13, 2017): 35–43. http://dx.doi.org/10.4314/jmbs.v6i1.5.

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Colorectal cancer is common and lethal disease with different incidence rate in different parts of the world which is taken into account as the third cause of cancer-related deaths. In the present study, using non-parametric Cox model and parametric Log-logistic model, factors influencing survival of patients with colorectal cancer were evaluated and the models efficiency were compared to provide the best model. This study is conducted on medical records of 1,127 patients with colorectal cancer referred to Taleghani Medical and Training Center of Tehran between 2001 - 2007 and were definitely diagnosed with cancer, pathologically. Semi-parametric Cox model and parametric log-logistic model were fitted. Akaike’s criterion of Cox Snell graph was used to compare the models. To take into account non-measured individual characteristics, frailty was added to Cox and log-logistic models. All calculations were carried out using STATA software version 12 and SPSS version 20.0, at the 0.05 level of significance. From a total of 1,127 patients studied in this research, there were 690 men and 437 women. According to non-parametric Kaplan-Meier method, chances of surviving for 1, 3, 5 and 7 years were 91.16, 73.20, 61.00, and 54.94, respectively. Addition of frailty parameter did not change the model outcome. The results of fitting classified Cox and log-logistic models showed that body mass index (BMI), tumor grade, tumor size, and spread to lymph nodes, were the factors affecting survival time. Based on comparisons, and according to Cox Snell residuals, Cox and log-logistic models had almost identical results; however, because of the benefits of parametric models, in surveying survival time of patients with colorectal cancer, log-logistic can be replaced, as a parametric model, with Cox model.Journal of Medical and Biomedical Sciences (2017) 6(1), 35-43Keywords: Colorectal cancer, Cox regression, Log-logistic model, Cox Snell residual
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40

Spiegel, Elmar, Thomas Kneib, and Fabian Otto-Sobotka. "Spatio-temporal expectile regression models." Statistical Modelling 20, no. 4 (March 18, 2019): 386–409. http://dx.doi.org/10.1177/1471082x19829945.

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Spatio-temporal models are becoming increasingly popular in recent regression research. However, they usually rely on the assumption of a specific parametric distribution for the response and/or homoscedastic error terms. In this article, we propose to apply semiparametric expectile regression to model spatio-temporal effects beyond the mean. Besides the removal of the assumption of a specific distribution and homoscedasticity, with expectile regression the whole distribution of the response can be estimated. For the use of expectiles, we interpret them as weighted means and estimate them by established tools of (penalized) least squares regression. The spatio-temporal effect is set up as an interaction between time and space either based on trivariate tensor product P-splines or the tensor product of a Gaussian Markov random field and a univariate P-spline. Importantly, the model can easily be split up into main effects and interactions to facilitate interpretation. The method is presented along the analysis of spatio-temporal variation of temperatures in Germany from 1980 to 2014.
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41

Chen, Song Xi, and Ingrid Van Keilegom. "A goodness-of-fit test for parametric and semi-parametric models in multiresponse regression." Bernoulli 15, no. 4 (November 2009): 955–76. http://dx.doi.org/10.3150/09-bej208.

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42

Kh.Bahez, Zahraa, and Husam .A. Rasheed. "Comparing Some of Robust the Non-Parametric Methods for Semi-Parametric Regression Models Estimation." Journal of Economics and Administrative Sciences 28, no. 132 (June 30, 2022): 105–17. http://dx.doi.org/10.33095/jeas.v28i132.2275.

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In this research, some robust non-parametric methods were used to estimate the semi-parametric regression model, and then these methods were compared using the MSE comparison criterion, different sample sizes, levels of variance, pollution rates, and three different models were used. These methods are S-LLS S-Estimation -local smoothing, (M-LLS)M- Estimation -local smoothing, (S-NW) S-Estimation-NadaryaWatson Smoothing, and (M-NW) M-Estimation-Nadarya-Watson Smoothing. The results in the first model proved that the (S-LLS) method was the best in the case of large sample sizes, and small sample sizes showed that the (M-LLS) method was the best, while the second model showed in general that the S-LLS method was the best in addition to the method M-LLS was the best in some cases of sample sizes and at different levels of variance. As for the third model, it was shown through the results that in most cases the S-LLS method was the best in addition to the M-LLS method which was better in some cases of sample sizes and at different levels of variance.
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43

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 Bayesian analysis involve distributions with a small number of parameters, where the prior specification is relatively straight forward. In the nonparametric Bayesian case, the prior is placed on an infinite dimensional space of all distributions, which requires special methods. A popular approach to nonparametric Bayesian analysis that involves Polya tree prior distributions is described. We provide example code to illustrate how models that contain Polya tree priors can be fit using SAS software. The methods are used to evaluate the covariate-specific accuracy of the biomarker, soluble epidermal growth factor receptor, for discerning lung cancer cases from controls using a flexible ROC regression modeling framework. The application highlights the usefulness of flexible models over a standard parametric method for estimating ROC curves.
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44

Menezes, André F. B., Josmar Mazucheli, and Subrata Chakraborty. "A collection of parametric modal regression models for bounded data." Journal of Biopharmaceutical Statistics 31, no. 4 (May 29, 2021): 490–506. http://dx.doi.org/10.1080/10543406.2021.1918141.

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45

Lin, D. Y., and P. S. F. Yip. "Parametric regression models for continuous time removal and recapture studies." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 61, no. 2 (April 1999): 401–11. http://dx.doi.org/10.1111/1467-9868.00184.

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46

Sridhar, K. N., and Mun Chan. "Modeling link lifetime data with parametric regression models in MANETs." IEEE Communications Letters 13, no. 12 (December 2009): 983–85. http://dx.doi.org/10.1109/lcomm.2009.12.091513.

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47

Chen, Hua Yun. "Nonparametric and Semiparametric Models for Missing Covariates in Parametric Regression." Journal of the American Statistical Association 99, no. 468 (December 2004): 1176–89. http://dx.doi.org/10.1198/016214504000001727.

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48

Ibrahim, Joseph G., Ming-Hui Chen, and Stuart R. Lipsitz. "Monte Carlo EM for Missing Covariates in Parametric Regression Models." Biometrics 55, no. 2 (June 1999): 591–96. http://dx.doi.org/10.1111/j.0006-341x.1999.00591.x.

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49

Ormoz, Ehsan, and Farzad Eskandari. "Variable selection in finite mixture of semi-parametric regression models." Communications in Statistics - Theory and Methods 45, no. 3 (January 25, 2016): 695–711. http://dx.doi.org/10.1080/03610926.2013.835413.

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50

Vogt, Michael, and Oliver Linton. "Classification of non-parametric regression functions in longitudinal data models." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 79, no. 1 (February 17, 2016): 5–27. http://dx.doi.org/10.1111/rssb.12155.

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