Academic literature on the topic 'Parametric regression models'
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Journal articles on the topic "Parametric regression models"
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.
Full textNygård 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.
Full textNeumeyer, 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.
Full textLaCour-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.
Full textBottai, 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.
Full textMahmoud, 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.
Full textMulayath 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.
Full textGarcÍa-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.
Full textDoveh, 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.
Full textGao, 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.
Full textDissertations / Theses on the topic "Parametric regression models"
Li, Lingzhu. "Model checking for general parametric regression models." HKBU Institutional Repository, 2019. https://repository.hkbu.edu.hk/etd_oa/654.
Full textChen, Chunxia. "Semi-parametric estimation in Tobit regression models." Kansas State University, 2013. http://hdl.handle.net/2097/15300.
Full textDepartment of Statistics
Weixing Song
In the classical Tobit regression model, the regression error term is often assumed to have a zero mean normal distribution with unknown variance, and the regression function is assumed to be linear. If the normality assumption is violated, then the commonly used maximum likelihood estimate becomes inconsistent. Moreover, the likelihood function will be very complicated if the regression function is nonlinear even the error density is normal, which makes the maximum likelihood estimation procedure hard to implement. In the full nonparametric setup when both the regression function and the distribution of the error term [epsilon] are unknown, some nonparametric estimators for the regression function has been proposed. Although the assumption of knowing the distribution is strict, it is a widely adopted assumption in Tobit regression literature, and is also confirmed by many empirical studies conducted in the econometric research. In fact, a majority of the relevant research assumes that [epsilon] possesses a normal distribution with mean 0 and unknown standard deviation. In this report, we will try to develop a semi-parametric estimation procedure for the regression function by assuming that the error term follows a distribution from a class of 0-mean symmetric location and scale family. A minimum distance estimation procedure for estimating the parameters in the regression function when it has a specified parametric form is also constructed. Compare with the existing semiparametric and nonparametric methods in the literature, our method would be more efficient in that more information, in particular the knowledge of the distribution of [epsilon], is used. Moreover, the computation is relative inexpensive. Given lots of application does assume that [epsilon] has normal or other known distribution, the current work no doubt provides some more practical tools for statistical inference in Tobit regression model.
Delgado, Carlos Alberto Cardozo. "Semi-parametric generalized log-gamma regression models." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-15032018-185352/.
Full textO objetivo central do trabalho é proporcionar ferramentas estatísticas para modelos de regressão semiparamétricos quando os erros seguem distribução log-gamma generalizada na presença de observações censuradas ou não censuradas. A estimação paramétrica e não paramétrica são realizadas através dos procedimentos Newton - Raphson, escore de Fisher e Backfitting (Gauss - Seidel). As propriedades assintóticas dos estimadores de máxima verossimilhança penalizada são estudadas em forma analítica, bem como através de simulações. Alguns procedimentos de diagnóstico são desenvolvidos, tais como resíduos tipo componente do desvio e resíduo quantílico, bem como medidas de influ\\^encia local sob alguns esquemas usuais de perturbação. Todos procedimentos do presente trabalho são implementados no ambiente computacional R, o pacote sglg é desenvolvido, assim como algumas aplicações a dados reais são apresentadas.
Peluso, Alina. "Novel regression models for discrete response." Thesis, Brunel University, 2017. http://bura.brunel.ac.uk/handle/2438/15581.
Full textShadat, Wasel Bin. "Specification testing of Garch regression models." Thesis, University of Manchester, 2011. https://www.research.manchester.ac.uk/portal/en/theses/specification-testing-of-garch-regression-models(56c218db-9b91-4d8c-bf26-8377ab185c71).html.
Full textEspigolan, Rafael [UNESP]. "Parametric and semi-parametric models for predicting genomic breeding values of complex traits in Nelore cattle." Universidade Estadual Paulista (UNESP), 2017. http://hdl.handle.net/11449/149846.
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
O melhoramento genético animal visa melhorar a produtividade econômica das futuras gerações de espécies domésticas por meio da seleção. A maioria das características de interesse econômico na pecuária é de expressão quantitativa e complexa, isto é, são influenciadas por vários genes e afetadas por fatores ambientais. As análises estatísticas de informações de fenótipo e pedigree permite estimar os valores genéticos dos candidatos à seleção com base no modelo infinitesimal. Uma grande quantidade de dados genômicos está atualmente disponível para a identificação e seleção de indivíduos geneticamente superiores com o potencial de aumentar a acurácia de predição dos valores genéticos e, portanto, a eficiência dos programas de melhoramento genético animal. Vários estudos têm sido conduzidos com o objetivo de identificar metodologias apropriadas para raças e características específicas, o que resultará em estimativas de valores genéticos genômicos (GEBVs) mais acurados. Portanto, o objetivo deste estudo foi verificar a possibilidade de aplicação de modelos semiparamétricos para a seleção genômica e comparar a habilidade de predição com os modelos paramétricos para dados reais (características de carcaça, qualidade da carne, crescimento e reprodutiva) e simulados. As informações fenotípicas e de pedigree utilizadas foram fornecidas por onze fazendas pertencentes a quatro programas de melhoramento genético animal. Para as características de carcaça e qualidade da carne, o banco de dados continha 3.643 registros para área de olho de lombo (REA), 3.619 registros para espessura de gordura (BFT), 3.670 registros para maciez da carne (TEN) e 3.378 observações para peso de carcaça quente (HCW). Um total de 825.364 registros para peso ao sobreano (YW) e 166.398 para idade ao primeiro parto (AFC) foi utilizado para as características de crescimento e reprodutiva. Genótipos de 2.710, 2.656, 2.749, 2.495, 4.455 e 1.760 animais para REA, BFT, TEN, HCW, YW e AFC foram disponibilizados, respectivamente. Após o controle de qualidade, restaram dados de, aproximadamente, 450.000 polimorfismos de base única (SNP). Os modelos de análise utilizados foram BLUP genômico (GBLUP), single-step GBLUP (ssGBLUP), Bayesian LASSO (BL) e as abordagens semiparamétricas Reproducing Kernel Hilbert Spaces (RKHS) e Kernel Averaging (KA). Para cada característica foi realizada uma validação cruzada composta por cinco “folds” e replicada aleatoriamente trinta vezes. Os modelos estatísticos foram comparados em termos do erro do quadrado médio (MSE) e acurácia de predição (ACC). Os valores de ACC variaram de 0,39 a 0,40 (REA), 0,38 a 0,41 (BFT), 0,23 a 0,28 (TEN), 0,33 a 0,35 (HCW), 0,36 a 0,51 (YW) e 0,49 a 0,56 (AFC). Para todas as características, os modelos GBLUP e BL apresentaram acurácias de predição similares. Para REA, BFT e HCW, todos os modelos apresentaram ACC similares, entretanto a regressão RKHS obteve o melhor ajuste comparado ao KA. Para características com maior quantidade de registros fenotípicos comparada ao número de animais genotipados (YW e AFC) o modelo ssGBLUP é indicado. Considerando o desempenho geral, para todas as características estudadas, a regressão RKHS é, particularmente, uma alternativa interessante para a aplicação na seleção genômica, especialmente para características de baixa herdabilidade. No estudo de simulação, genótipos, pedigree e fenótipos para quatro características (A, B, C e D) foram simulados utilizando valores de herdabilidade baseados nos obtidos com os dados reais (0,09, 0,12, 0,36 e 0,39 para cada característica, respectivamente). O genoma simulado consistiu de 735.293 marcadores e 1.000 QTLs distribuídos aleatoriamente por 29 pares de autossomos, com comprimento variando de 40 a 146 centimorgans (cM), totalizando 2.333 cM. Assumiu-se que os QTLs explicavam 100% da variação genética. Considerando as frequências do alelo menor maiores ou iguais a 0,01, um total de 430.000 marcadores foram selecionados aleatoriamente. Os fenótipos foram obtidos pela soma dos resíduos (aleatoriamente amostrados de uma distribuição normal com média igual a zero) aos valores genéticos verdadeiros, e todo o processo de simulação foi replicado 10 vezes. A ACC foi calculada por meio da correlação entre o valor genético genômico estimado e o valor genético verdadeiro, simulados da 12a a 15a geração. A média do desequilíbrio de ligação, medido entre os pares de marcadores adjacentes para todas as características simuladas foi de 0,21 para as gerações recentes (12a, 13a e 14a), e 0,22 para a 15a geração. A ACC para as características simuladas A, B, C e D variou de 0,43 a 0,44, 0,47 a 0,48, 0,80 a 0,82 e 0,72 a 0,73, respectivamente. Diferentes metodologias de seleção genômica implementadas neste estudo mostraram valores similares de acurácia de predição, e o método mais adequado é dependente da característica explorada. Em geral, as regressões RKHS obtiveram melhor desempenho em termos de ACC com menor valor de MSE em comparação com os outros modelos.
Animal breeding aims to improve economic productivity of future generations of domestic species through selection. Most of the traits of economic interest in livestock have a complex and quantitative expression i.e. are influenced by a large number of genes and affected by environmental factors. Statistical analysis of phenotypes and pedigree information allows estimating the breeding values of the selection candidates based on infinitesimal model. A large amount of genomic data is now available for the identification and selection of genetically superior individuals with the potential to increase the accuracy of prediction of genetic values and thus, the efficiency of animal breeding programs. Numerous studies have been conducted in order to identify appropriate methodologies to specific breeds and traits, which will result in more accurate genomic estimated breeding values (GEBVs). Therefore, the objective of this study was to verify the possibility of applying semi-parametric models for genomic selection and to compare their ability of prediction with those of parametric models for real (carcass, meat quality, growth and reproductive traits) and simulated data. The phenotypic and pedigree information used were provided by farms belonging to four animal breeding programs which represent eleven farms. For carcass and meat quality traits, the data set contained 3,643 records for rib eye area (REA), 3,619 records for backfat thickness (BFT), 3,670 records for meat tenderness (TEN) and 3,378 observations for hot carcass weight (HCW). A total of 825,364 records for yearling weight (YW) and 166,398 for age at first calving (AFC) were used as growth and reproductive traits of Nelore cattle. Genotypes of 2,710, 2,656, 2,749, 2,495, 4,455 and 1,760 animals were available for REA, BFT, TEN, HCW, YW and AFC, respectively. After quality control, approximately 450,000 single nucleotide polymorphisms (SNP) remained. Methods of analysis were genomic BLUP (GBLUP), single-step GBLUP (ssGBLUP), Bayesian LASSO (BL) and the semi-parametric approaches Reproducing Kernel Hilbert Spaces (RKHS) regression and Kernel Averaging (KA). A five-fold cross-validation with thirty random replicates was carried out and models were compared in terms of their prediction mean squared error (MSE) and accuracy of prediction (ACC). The ACC ranged from 0.39 to 0.40 (REA), 0.38 to 0.41 (BFT), 0.23 to 0.28 (TEN), 0.33 to 0.35 (HCW), 0.36 to 0.51 (YW) and 0.49 to 0.56 (AFC). For all traits, the GBLUP and BL models showed very similar prediction accuracies. For REA, BFT and HCW, models provided similar prediction accuracies, however RKHS regression had the best fit across traits considering multiple-step models and compared to KA. For traits which have a higher number of animals with phenotypes compared to the number of those with genotypes (YW and AFC), the ssGBLUP is indicated. Judged by overall performance, across all traits, the RKHS regression is particularly appealing for application in genomic selection, especially for low heritability traits. Simulated genotypes, pedigree, and phenotypes for four traits A, B, C and D were obtained using heritabilities based on real data (0.09, 0.12, 0.36 and 0.39 for each trait, respectively). The simulated genome consisted of 735,293 markers and 1,000 QTLs randomly distributed over 29 pairs of autosomes, with length varying from 40 to 146 centimorgans (cM), totaling 2,333 cM. It was assumed that QTLs explained 100% of genetic variance. Considering Minor Allele Frequencies greater or equal to 0.01, a total of 430,000 markers were randomly selected. The phenotypes were generated by adding residuals, randomly drawn from a normal distribution with mean equal to zero, to the true breeding values and all simulation process was replicated 10 times. ACC was quantified using correlations between the predicted genomic breeding value and true breeding values simulated for the generations of 12 to 15. The average linkage disequilibrium, measured between pairs of adjacent markers for all simulated traits was 0.21 for recent generations (12, 13 and 14), and 0.22 for generation 15. The ACC for simulated traits A, B, C and D ranged from 0.43 to 0.44, 0.47 to 0.48, 0.80 to 0.82 and 0.72 to 0.73, respectively. Different genomic selection methodologies implemented in this study showed similar accuracies of prediction, and the optimal method was sometimes trait dependent. In general, RKHS regressions were preferable in terms of ACC and provided smallest MSE estimates compared to other models.
FAPESP: 2014/00779-0
FAPESP: 2015/13084-3
Wang, Sejong. "Three nonparametric specification tests for parametric regression models : the kernel estimation approach." Connect to resource, 1994. http://rave.ohiolink.edu/etdc/view.cgi?acc%5Fnum=osu1261492759.
Full textMostafa, Abdelelah M. "Regression approach to software reliability models." [Tampa, Fla] : University of South Florida, 2006. http://purl.fcla.edu/usf/dc/et/SFE0001648.
Full textLäuter, Henning. "Estimation in partly parametric additive Cox models." Universität Potsdam, 2003. http://opus.kobv.de/ubp/volltexte/2011/5150/.
Full textMasiulaitytė, Inga. "Regression and degradation models in reliability theory and survival analysis." Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2010. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2010~D_20100527_134956-15325.
Full textDaktaro disertacijos tyrimo objektai yra rezervuotos sistemos ir degradaciniai modeliai. Norint užtikrinti svarbių sistemos elementų aukštą patikimumą, naudojami jų rezerviniai elementai, kurie gali būti įjungiami sugedus šiems pagrindiniams elementams. Rezerviniai elementai gali funkcionuoti skirtinguose režimuose: „karštame“, „šaltame“ arba „šiltame“. Disertacijoje yra nagrinėjamos sistemos su „šiltai“ rezervuotais elementais. Darbe suformuluojama rezervinio elemento „sklandaus įjungimo“ hipotezė ir konstruojami statistiniai kriterijai šiai hipotezei tikrinti. Nagrinėjami neparametrinio ir parametrinio taškinio bei intervalinio vertinimo uždaviniai. Disertacijoje nagrinėjami pakankamai bendri degradacijos modeliai, kurie aprašo elementų gedimų intensyvumą kaip funkciją kiek naudojamų apkrovų, tiek ir degradacijos lygio, kuri savo ruožtu modeliuojama naudojant stochastinius procesus.
Books on the topic "Parametric regression models"
Cheng, Russell. Non-Standard Parametric Statistical Inference. Oxford, United Kingdom: Oxford University Press, 2017.
Find full textBerhane, Kiros. Non-parametric regression models for longitudinal data. 1994.
Find full textFerraty, Frédéric, and Philippe Vieu. A Unifying Classification for Functional Regression Modeling. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.1.
Full textCheng, Russell. Bootstrap Analysis. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198505044.003.0004.
Full textCheng, Russell. Embedded Model Problem. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198505044.003.0005.
Full textBook chapters on the topic "Parametric regression models"
Harrell, Frank E. "Parametric Survival Models." In Regression Modeling Strategies, 423–51. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19425-7_18.
Full textHarrell, Frank E. "Parametric Survival Models." In Regression Modeling Strategies, 413–42. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4757-3462-1_17.
Full textChen, Jie, and A. K. Gupta. "Regression Models." In Parametric Statistical Change Point Analysis, 111–25. Boston, MA: Birkhäuser Boston, 2000. http://dx.doi.org/10.1007/978-1-4757-3131-6_4.
Full textChen, Jie, and Arjun K. Gupta. "Regression Models." In Parametric Statistical Change Point Analysis, 139–54. Boston: Birkhäuser Boston, 2011. http://dx.doi.org/10.1007/978-0-8176-4801-5_4.
Full textKnopov, Pavel S., and Evgeniya J. Kasitskaya. "Parametric Regression Models." In Applied Optimization, 71–162. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4757-3567-3_3.
Full textKlein, John P., and Melvin L. Moeschberger. "Inference for Parametric Regression Models." In Statistics for Biology and Health, 373–403. New York, NY: Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4757-2728-9_12.
Full textKlein, John P., and Melvin L. Moeschberger. "Inference for Parametric Regression Models." In Statistics for Biology and Health, 393–423. New York, NY: Springer New York, 2003. http://dx.doi.org/10.1007/0-387-21645-6_12.
Full textde Vries, Harm, George Azzopardi, André Koelewijn, and Arno Knobbe. "Parametric Nonlinear Regression Models for Dike Monitoring Systems." In Advances in Intelligent Data Analysis XIII, 345–55. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12571-8_30.
Full textCaouder, Nathalie. "An Artificial Intelligence Approach for Modeling in Nonlinear Regression Parametric Models." In Computational Statistics, 373–78. Heidelberg: Physica-Verlag HD, 1992. http://dx.doi.org/10.1007/978-3-662-26811-7_52.
Full textGarre, M., M. A. Sicilia, J. J. Cuadrado, and M. Charro. "Regression Analisys of Segmented Parametric Software Cost Estimation Models Using Recursive Clustering Tool." In Intelligent Data Engineering and Automated Learning – IDEAL 2006, 849–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11875581_102.
Full textConference papers on the topic "Parametric regression models"
Seiler, Christof, Xavier Pennec, and Mauricio Reyes. "Parametric regression of 3D medical images through the exploration of non-parametric regression models." In 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. IEEE, 2010. http://dx.doi.org/10.1109/isbi.2010.5490313.
Full textDai, Denny C., and Kay C. Wiese. "Performance prediction for RNA design using parametric and non-parametric regression models." In 2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). IEEE, 2009. http://dx.doi.org/10.1109/cibcb.2009.4925702.
Full textShenoy, Saahil, and Dimitry Gorinevsky. "Stochastic optimization of power market forecast using non-parametric regression models." In 2015 IEEE Power & Energy Society General Meeting. IEEE, 2015. http://dx.doi.org/10.1109/pesgm.2015.7286589.
Full textLopez, Olivier, and Valentin Patilea. "Synthetic data based nonparametric testing of parametric mean-regression models with censored data." In Recent Advances in Stochastic Modeling and Data Analysis. WORLD SCIENTIFIC, 2007. http://dx.doi.org/10.1142/9789812709691_0032.
Full textBocklitz, Thomas. "Understanding of Non-linear Parametric Regression and Classification Models: A Taylor Series based Approach." In 8th International Conference on Pattern Recognition Applications and Methods. SCITEPRESS - Science and Technology Publications, 2019. http://dx.doi.org/10.5220/0007682008740880.
Full textAMER, AHMAD, and FOTIS KOPSAFTOPOULOS. "Probabilistic Damage Quantification via the Integration of Non- parametric Time-Series and Gaussian Process Regression Models." In Structural Health Monitoring 2019. Lancaster, PA: DEStech Publications, Inc., 2019. http://dx.doi.org/10.12783/shm2019/32379.
Full textSeungyeoun Lee, Jinseok Oh, Min-Seok Kwon, and Taesung Park. "Gene-gene interaction analysis for the survival phenotype based on the standardized residuals from parametric regression models." In 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW). IEEE, 2011. http://dx.doi.org/10.1109/bibmw.2011.6112460.
Full textSoto B., R., C. H. Wu, and A. M. Bubela. "Infill Drilling Recovery Models for Carbonate Reservoirs - A Multiple Statistical, Non-Parametric Regression, and Neural Network Approach." In SPE Eastern Regional Meeting. Society of Petroleum Engineers, 1999. http://dx.doi.org/10.2118/57458-ms.
Full textChar, Jir-Ming, and Mao-Yi Fan. "The Parametric Study of Ignition Process of a Fuel Droplet." In ASME 1991 International Gas Turbine and Aeroengine Congress and Exposition. American Society of Mechanical Engineers, 1991. http://dx.doi.org/10.1115/91-gt-108.
Full textChintala, Rohit H., and Bryan P. Rasmussen. "Automated Multi-Zone Linear Parametric Black Box Modeling Approach for Building HVAC Systems." In ASME 2015 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/dscc2015-9933.
Full textReports on the topic "Parametric regression models"
Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, July 1996. http://dx.doi.org/10.32747/1996.7613033.bard.
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