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Artykuły w czasopismach na temat "Cross-sectional Regression"
Robinson, Peter M. "Nonparametric trending regression with cross-sectional dependence". Journal of Econometrics 169, nr 1 (lipiec 2012): 4–14. http://dx.doi.org/10.1016/j.jeconom.2012.01.005.
Pełny tekst źródłaLiu, Jie, Zaixia Hu i Shaohua Tan. "Cross-sectional stock return analysis using support vector regression". Applied Economics Letters 17, nr 1 (2.04.2008): 71–74. http://dx.doi.org/10.1080/13504850701719777.
Pełny tekst źródłaVanhonacker, Wilfried R., i Diana Day. "Cross-Sectional Estimation in Marketing: Direct Versus Reverse Regression". Marketing Science 6, nr 3 (sierpień 1987): 254–67. http://dx.doi.org/10.1287/mksc.6.3.254.
Pełny tekst źródłaMARTUZZI, M., i P. ELLIOTT. "CROSS-SECTIONAL DATA ANALYSIS: A SIMPLE ALTERNATIVE TO LOGISTIC REGRESSION". Epidemiology 7, Supplement (lipiec 1996): S70. http://dx.doi.org/10.1097/00001648-199607001-00207.
Pełny tekst źródłaHodoshima, Jiro, Xavier Garza–Gómez i Michio Kunimura. "Cross-sectional regression analysis of return and beta in Japan". Journal of Economics and Business 52, nr 6 (listopad 2000): 515–33. http://dx.doi.org/10.1016/s0148-6195(00)00031-x.
Pełny tekst źródłaKirby, Chris. "Firm Characteristics, Cross-Sectional Regression Estimates, and Asset Pricing Tests". Review of Asset Pricing Studies 10, nr 2 (25.06.2019): 290–334. http://dx.doi.org/10.1093/rapstu/raz005.
Pełny tekst źródłaWang, Na. "Association between Arsenic and Generalized Anxiety Disorder: A Cross-Sectional Study". Women Health Care and Issues 4, nr 4 (27.05.2021): 01–05. http://dx.doi.org/10.31579/2642-9756/054.
Pełny tekst źródłaKAN, RAYMOND, CESARE ROBOTTI i JAY SHANKEN. "Pricing Model Performance and the Two-Pass Cross-Sectional Regression Methodology". Journal of Finance 68, nr 6 (12.11.2013): 2617–49. http://dx.doi.org/10.1111/jofi.12035.
Pełny tekst źródłaHur, Jungshik, Raman Kumar i Vivek Singh. "Cross-sectional regression of returns on betas and portfolio grouping procedures". International Journal of Business and Systems Research 8, nr 1 (2014): 1. http://dx.doi.org/10.1504/ijbsr.2014.058005.
Pełny tekst źródłaKarafiath, Imre. "Estimating cross-sectional regressions in event studies with conditional heteroskedasticity and regression designs that have leverage". International Journal of Managerial Finance 10, nr 4 (26.08.2014): 418–31. http://dx.doi.org/10.1108/ijmf-12-2012-0134.
Pełny tekst źródłaRozprawy doktorskie na temat "Cross-sectional Regression"
Hargarten, Paul. "A Cross-Sectional Analysis of Health Impacts of Inorganic Arsenic in Chemical Mixtures". VCU Scholars Compass, 2015. http://scholarscompass.vcu.edu/etd/3788.
Pełny tekst źródłaYu, Mingyu Carleton University Dissertation Mathematics. "Nested-error regression models and small area estimation combining cross-sectional and time series data". Ottawa, 1993.
Znajdź pełny tekst źródłaNOVAES, ANDRE LUIZ FARIAS. "ECONOMETRIC GENETIC PROGRAMMING: A NEW APPROACH FOR REGRESSION AND CLASSIFICATION PROBLEMS IN CROSS-SECTIONAL DATASETS". PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2015. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=25338@1.
Pełny tekst źródłaCONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
Esta dissertação propõe modelos parcimoniosos para tarefas de regressão e classificação em conjuntos de dados exclusivamente seccionais, mantendo-se a hipótese de amostragem aleatória. Os modelos de regressão são lineares, estimados por Mínimos Quadrados Ordinários resolvidos pela Decomposição QR, apresentando solução única sob posto cheio ou não da matriz de regressores. Os modelos de classificação são não lineares, estimados por Máxima Verossimilhança utilizando uma variante do Método de Newton, nem sempre apresentando solução única. A parcimônia dos modelos de regressão é fundamentada na prova matemática de que somente agregará acurácia ao modelo o regressor que apresentar módulo da estatística de teste, em um teste de hipótese bicaudal, superior à unidade. A parcimônia dos modelos de classificação é fundamentada em significância estatística e embasada intuitivamente no resultado teórico da existência de classificadores perfeitos. A Programação Genética (PG) realiza o processo de evolução de modelos, explorando o espaço de busca de possíveis modelos, constituídos de distintos regressores. Os resultados obtidos via Programação Genética Econométrica (PGE) – nome dado ao algoritmo gerador de modelos – foram comparados aos proporcionados por benchmarks em oito distintos conjuntos de dados, mostrando-se competitivos em termos de acurácia na maior parte dos casos. Tanto sob o domínio da PG quanto sob o domínio da econometria, a PGE mostrou benefícios, como o auxílio na identificação de introns, o combate ao bloat por significância estatística e a geração de modelos econométricos de elevada acurácia, entre outros.
This dissertation proposes parsimonious models for regression and classification tasks in cross-sectional datasets under random sample hypothesis. Regression models are linear in parameters, estimated by Ordinary Least Squares solved by QR Decomposition, presenting a unique solution under full rank of the regressor matrix or not. Classification models are nonlinear in parameters, estimated by Maximum Likelihood, not always presenting a unique solution. Parsimony in regression models is based on the mathematical proof that accuracy will be added to models only by the regressor that presents a test statistic module higher than a predefined value in a two-sided hypothesis test. Parsimony in classification models is based on statistical significance and, intuitively, on the theoretical result about the existence of perfect classifiers. Genetic Programming performs the evolution process of models, being responsible for exploring the search space of possible regressors and models. The results obtained with Econometric Genetic Programming – name of the algorithm in this dissertation – was compared with those from benchmarks in eight distinct cross-sectional datasets, showing competitive results in terms of accuracy in most cases. Both in the field of Genetic Programming and in that of econometrics, Econometric Genetic Programming has shown benefits such as help on introns identification, combat to bloat by statistical significance and generation of high level accuracy models, among others.
collet, CLAIRE, i Kimberlay Duquennoy. "Did the pattern of poverty in West Germany change because of the reunification? : A cross-sectional study of poverty in West Germany". Thesis, Linnéuniversitetet, Institutionen för nationalekonomi och statistik (NS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-85939.
Pełny tekst źródłaKlekamp, Benjamin Glenn. "Assessing the Relationship of Monocytes with Primary and Secondary Dengue Infection among Hospitalized Dengue Patients in Malaysia, 2010: A Cross-Sectional Study". Scholar Commons, 2011. http://scholarcommons.usf.edu/etd/3185.
Pełny tekst źródłaWang, Yue Nan, i wangyn14@hotmail com. "The diversification benefits and the risk and return relationships in the Chinese A-share market". RMIT University. Economics, Finance and Marketing, 2006. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20061205.103325.
Pełny tekst źródłaWang, Yuenan, i yangyn14@hotmail com. "The diversification benefits and the risk and return relationships in the Chinese A-share market". RMIT University. Economics, Finance and Marketing, 2006. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20080103.093949.
Pełny tekst źródłaGashi, Arben, i Florent Sinani. "Adolescents, Sleep Deprivation and Externalizing Behaviour - Is There a Connection?" Thesis, Örebro universitet, Institutionen för juridik, psykologi och socialt arbete, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-65584.
Pełny tekst źródłaRogers, Paul B. "An analysis of parameters of the U.S. pilot population from 1983-2005 from a scientific information system point of view : a foundation for computational epidemiological studies /". Oklahoma City : [s.n.], 2007.
Znajdź pełny tekst źródłaKeunecke, Johann Georg [Verfasser], Johannes [Akademischer Betreuer] Prottengeier, Johannes [Gutachter] Prottengeier i Torsten [Gutachter] Birkholz. "Workload and influencing factors in non-emergency medical transfers: a multiple linear regression analysis of a cross-sectional questionnaire study / Johann Georg Keunecke ; Gutachter: Johannes Prottengeier, Torsten Birkholz ; Betreuer: Johannes Prottengeier". Erlangen : Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 2020. http://d-nb.info/1220506036/34.
Pełny tekst źródłaKsiążki na temat "Cross-sectional Regression"
Uribe, Jorge M., i Montserrat Guillen. Quantile Regression for Cross-Sectional and Time Series Data. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44504-1.
Pełny tekst źródłaBulkley, George. Excessive stock price dispersion: A regression test of cross-sectional volatility. London: London School of Economics, Financial Markets Group, 1996.
Znajdź pełny tekst źródłaKan, Raymond. Pricing model performance and the two-pass cross-sectional regression methodology. Cambridge, MA: National Bureau of Economic Research, 2009.
Znajdź pełny tekst źródłaSwaine, Daniel G. What do cross-sectional growth regressions tell us about convergence? [Boston]: Federal Reserve Bank of Boston, 1998.
Znajdź pełny tekst źródłaSwaine, Daniel G. What do cross-sectional growth regressions tell us about convergence? [Boston]: Federal Reserve Bank of Boston, 1998.
Znajdź pełny tekst źródłaSwaine, Daniel G. What do cross-sectional growth regressions tell us about convergence? [Boston]: Federal Reserve Bank of Boston, 1998.
Znajdź pełny tekst źródłaUribe, Jorge M., i Montserrat Guillen. Quantile Regression for Cross-Sectional and Time Series Data: Applications in Energy Markets Using R. Springer, 2020.
Znajdź pełny tekst źródłaLee, Patricia, Donald Stewart i Stephen Clift. Group Singing and Quality of Life. Redaktorzy Brydie-Leigh Bartleet i Lee Higgins. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190219505.013.22.
Pełny tekst źródłaLi, Quan. Using R for Data Analysis in Social Sciences. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190656218.001.0001.
Pełny tekst źródłaEgger, Eva-Maria, Cecilia Poggi i Héctor Rufrancos. Welfare and the depth of informality: Evidence from five African countries. Wyd. 25. UNU-WIDER, 2021. http://dx.doi.org/10.35188/unu-wider/2021/963-1.
Pełny tekst źródłaCzęści książek na temat "Cross-sectional Regression"
Uribe, Jorge M., i Montserrat Guillen. "Cross-sectional Quantile Regression". W Quantile Regression for Cross-Sectional and Time Series Data, 21–32. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44504-1_4.
Pełny tekst źródłaUribe, Jorge M., i Montserrat Guillen. "Time Series Quantile Regression". W Quantile Regression for Cross-Sectional and Time Series Data, 33–44. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44504-1_5.
Pełny tekst źródłaUribe, Jorge M., i Montserrat Guillen. "Quantile Regression: A Methodological Overview". W Quantile Regression for Cross-Sectional and Time Series Data, 13–19. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44504-1_3.
Pełny tekst źródłaUribe, Jorge M., i Montserrat Guillen. "Novel Approaches in Quantile Regression". W Quantile Regression for Cross-Sectional and Time Series Data, 49–53. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44504-1_7.
Pełny tekst źródłaUribe, Jorge M., i Montserrat Guillen. "Goodness of Fit in Quantile Regression Models". W Quantile Regression for Cross-Sectional and Time Series Data, 45–48. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44504-1_6.
Pełny tekst źródłaUribe, Jorge M., i Montserrat Guillen. "Why and When Should Quantile Regression Be Used?" W Quantile Regression for Cross-Sectional and Time Series Data, 1–5. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44504-1_1.
Pełny tekst źródłaUribe, Jorge M., i Montserrat Guillen. "A Case Study: Modeling Energy Markets by the Means of Quantile Regression". W Quantile Regression for Cross-Sectional and Time Series Data, 7–11. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44504-1_2.
Pełny tekst źródłaUribe, Jorge M., i Montserrat Guillen. "What Have We Learned from Quantile Regression? Implications for Economics and Finance". W Quantile Regression for Cross-Sectional and Time Series Data, 55. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44504-1_8.
Pełny tekst źródłaSutradhar, Brajendra C. "Overview of Regression Models for Cross-Sectional Univariate Categorical Data". W Springer Series in Statistics, 7–87. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-2137-9_2.
Pełny tekst źródłaTian, Cunzhi, Qiuping Guo i Haijun Wang. "The Performance Persistence of Mutual Fund: The Empirical Research Based on Cross-Sectional Regression". W Informatics in Control, Automation and Robotics, 193–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25899-2_26.
Pełny tekst źródłaStreszczenia konferencji na temat "Cross-sectional Regression"
Jianbao, Chen, Cheng Tingting i Wang Dengling. "Notice of Retraction: Quantile Regression Analysis of Cross Sectional Price-Volume Relation in Chinese Stock Markets". W 2009 International Forum on Information Technology and Applications (IFITA). IEEE, 2009. http://dx.doi.org/10.1109/ifita.2009.315.
Pełny tekst źródłaAshour, Reem, Sana Elashie, Bayan Alkeilan i Mujahed Shraim. "Smartphone Addiction among Qatar University Students: A Cross-Sectional study". W Qatar University Annual Research Forum & Exhibition. Qatar University Press, 2020. http://dx.doi.org/10.29117/quarfe.2020.0203.
Pełny tekst źródłaHardiawan, Donny, Arief Anshory Yusuf i Bagdja Muljarijadi. "THE IMPACT OF EXPENDITURE INEQUALITY AND SOCIOECONOMIC ON CRIME RATES IN INDONESIA. CROSS SECTIONAL STUDY USING SPATIAL ECONOMETRICS AND GEOGRAPHICALLY WEIGHTED REGRESSION". W Proceedings of the Achieving and Sustaining SDGs 2018 Conference: Harnessing the Power of Frontier Technology to Achieve the Sustainable Development Goals (ASSDG 2018). Paris, France: Atlantis Press, 2019. http://dx.doi.org/10.2991/assdg-18.2019.14.
Pełny tekst źródłaD. Guillén-Gámez, Francisco. "A cross-sectional study on attitudes towards statistics in primary education students of Higher Education: prediction of variables through a multiple regression model". W 2nd International Conference on Modern Research in Education, Teaching and Learning. Acavent, 2020. http://dx.doi.org/10.33422/2nd.icmetl.2020.11.82.
Pełny tekst źródłaHallingberg, B., O. Maynard, L. Gray, A. MacKintosh, E. Lowthian i G. Moore. "OP59 #Have e-cigarettes re-normalized or displaced youth smoking?: a segmented regression analysis of repeated cross sectional survey data in england, scotland and wales". W Society for Social Medicine 62nd Annual Scientific Meeting, Hosted by the MRC/CSO Social and Public Health Sciences Unit, University of Glasgow, 5–7 September 2018. BMJ Publishing Group Ltd, 2018. http://dx.doi.org/10.1136/jech-2018-ssmabstracts.58.
Pełny tekst źródła‘Arub, Lathifah, Eti Poncorini Pamungkasari i Yulia Lanti Retno Dewi. "Multiple Logistic Regression on the Factors Affecting Exclusive Breastfeeding Practice in Karanganyar, Central Java". W The 7th International Conference on Public Health 2020. Masters Program in Public Health, Universitas Sebelas Maret, 2020. http://dx.doi.org/10.26911/the7thicph.03.89.
Pełny tekst źródłaZamzam, Maki, Didik Gunawan Tamtomo i Vitri Widyaningsih. "Biopsychosocial Determinants of Quality of Life in Post Stroke Patients: A Multiple Logistic Regression Analysis". W The 7th International Conference on Public Health 2020. Masters Program in Public Health, Universitas Sebelas Maret, 2020. http://dx.doi.org/10.26911/the7thicph.01.35.
Pełny tekst źródłaMartaningrum, Herlina Ika, Uki Retno Budihastuti i Bhisma Murti. "Factors Affecting the Use of Visual Inspection Acetic Acid Test in Magelang, Central Java". W The 7th International Conference on Public Health 2020. Masters Program in Public Health, Universitas Sebelas Maret, 2020. http://dx.doi.org/10.26911/the7thicph.03.90.
Pełny tekst źródłaPangesti, Tri Puji, Didik Gunawan Tamtomo i Bhisma Murti. "Multilevel Logistic Regression Analysis on the Effectiveness of Chronic Disease Management Program in Improving “Cerdik” Healthy Behavior for Hypertensive Patients". W The 7th International Conference on Public Health 2020. Masters Program in Public Health, Universitas Sebelas Maret, 2020. http://dx.doi.org/10.26911/the7thicph.04.44.
Pełny tekst źródłaHamdan, Abeer, i Manar Abdel-Rahman. "Child Disciplinary Practices in relation to Household Head Education and beliefs in Five Middle East and North African (MENA) countries: Cross Sectional study-Further analysis of Multiple Indicator Cluster survey data". W Qatar University Annual Research Forum & Exhibition. Qatar University Press, 2020. http://dx.doi.org/10.29117/quarfe.2020.0168.
Pełny tekst źródłaRaporty organizacyjne na temat "Cross-sectional Regression"
Robinson, Peter. Nonparametric trending regression with cross-sectional dependence. Institute for Fiscal Studies, luty 2011. http://dx.doi.org/10.1920/wp.cem.2011.1011.
Pełny tekst źródłaKan, Raymond, Cesare Robotti i Jay Shanken. Pricing Model Performance and the Two-Pass Cross-Sectional Regression Methodology. Cambridge, MA: National Bureau of Economic Research, czerwiec 2009. http://dx.doi.org/10.3386/w15047.
Pełny tekst źródłaVillamizar-Villegas, Mauricio, i Yasin Kursat Onder. Uncovering Time-Specific Heterogeneity in Regression Discontinuity Designs. Banco de la República de Colombia, listopad 2020. http://dx.doi.org/10.32468/be.1141.
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