00649nam0-22002411i-450-99000272037040332120091021123753.0000272037FED01000272037(Aleph)000272037FED0100027203720030910d1975----km-y0itay50------baengShare prices and mergers.by Michael Firt hs.l.Univ. of Stirling1975Firth,Michael111511ITUNINARICAUNIMARCBK9900027203704033216-292-TBT.B.ECAECAShare prices and mergers426890UNINA01153nam 2200397 450 991013157880332120240214110342.01-55441-327-3(CKB)3680000000168375(NjHacI)993680000000168375(EXLCZ)99368000000016837520240214d2002 uy 0freur|||||||||||txtrdacontentcrdamediacrrdacarrierLa nuptialité en France depuis la guerre /Maurice HalbwachsChicoutimi :J.-M. Tremblay,2002.1 online resourceClassiques des sciences socialesClassiques des sciences sociales.MarriageFranceFamiliesFranceFamily policyFranceMarriageFamiliesFamily policy306.850944Halbwachs Maurice1877-1945,119976NjHacINjHaclBOOK9910131578803321La nuptialité en France depuis la guerre3975550UNINA05878nam 22005295 450 991091859510332120241220115509.09783031722479303172247710.1007/978-3-031-72247-9(MiAaPQ)EBC31855100(Au-PeEL)EBL31855100(CKB)37058962800041(DE-He213)978-3-031-72247-9(EXLCZ)993705896280004120241220d2024 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierApplication of Regularized Regressions to Identify Novel Predictors in Clinical Research /by Ton J. Cleophas, Aeilko H. Zwinderman1st ed. 2024.Cham :Springer Nature Switzerland :Imprint: Springer,2024.1 online resource (274 pages)9783031722462 3031722469 -- Basic Principles of Regression Analysis. -- Optimal Scaling, Discretization, and Regularization vs Traditional Linear Regression. -- Regularized Regression Analysis, Ridge, Lasso, Elastic Net Regression Coefficients. -- Effect of Predictors on Health Scores, 110 Patients, Traditional vs Regularized Regressions. -- Effect on Physical strength of Races, 60 Patients, Traditional Regression vs Regularized regressions. -- Effects of Genetic Polymorphisms on Clinical Outcomes, 250 Patients, Traditional vs Regularized Regressions. -- Effect of Old Treatment and Age on New Treatment, 35 Patients, Traditional vs Regularized Regressions. -- Effect on Paroxysmal Atrial Fibrillations of Four Predictors, 50 Patients, Traditional vs Regularized Regressions. -- Effect of Air Quality of Operating Rooms on Infections, 8 Operating Rooms, Traditional vs Regularized Regressions. -- Effect on Weightloss of Age, Calorieintake, Exercise, Interaction, 64 Patients, Traditional vs Regularized Regressions. -- Effect on Body Surface Measured of Gender, Age, Weight, Height, and Weight x Height Interaction, 90 Patients, Traditional vs Regularized Regressions. -- Effect on Paroxysmal Atrial Fibrillations of Gender, Treatment and Their Interaction, 40 Patients, Traditional vs Regularized Regressions. -- Effect on Hours of Sleep of Treatment Group, Age, Gender, Comorbidity, 20 Patients, Traditional vs Regularized Regressions. -- Effect of Betaagonist and Prednisone on Peak Expiratory Flow, 78 COPD Patients, Traditional vs Regularized Regressions. -- Effect on LDL Cholesterol Reduction of Five Predictors, 953 Patients, Traditional vs Regularized Regressions. -- Effect of Five Factors on Body Weight, 217 Patients, Traditional vs Regularized Regressions. -- Functional Data Analysis and Regularized Regressions.This textbook is an important novel menu for multiple variables regression entitled "regularized regression". It is a must have for identifying unidentified leading factors. Also, you get fitted parameters for your overfitted data. Finally, there is no more need for commonly misunderstood p-values. Instead, the regression coefficient, R-value, as reported from a regression line has been applied as the key predictive estimator of the regression study. With simple one by one variable regression it is no wider than -1 to +1. With multiple variables regression it can easily get > +1 or < -1. This means we have a seriously flawed regression model, mostly due to collinearity or non-linear data. Completing the analysis will lead to overfitting, and thus a meaningless significant study due to data spread wider than compatible with random. In order for the regression coefficients to remain in the right size, fortunately a shrinking procedure has been invented. In the past two decades regularized regression has become a major topic of research, particularly with high dimensional data. Yet, the method is pretty new and infrequently used in real-data analysis. Its performance as compared to traditional null hypothesis testing has to be confirmed by prospective comparisons. Most studies published to date are of a theoretical nature involving statistical modeling and simulation studies. The journals Nature and Science published 19 and 10 papers of this sort in the past 8 years. The current edition will for the first time systematically test regularized regression against traditional regression analysis in 20 clinical data examples. The edition is also a textbook and tutorial for medical and healthcare students as well as recollection bench and help desk for professionals. Each chapter can be studied as a standalone, and, using, real as well as hypothesized data, it tests the performance of the novel methodology against traditional regressions. Step by step analyses of 20 data files are included for self-assessment. The authors are well qualified in their field. Professor Zwinderman is past-president of the International Society of Biostatistics and Professor Cleophas is past-president of the American College of Angiology. The authors have been working together for 25 years and their research can be characterized as a continued effort to demonstrate that clinical data analysis is a discipline at the interface of biology and mathematics.Medical sciencesStatisticsHealth SciencesApplied StatisticsMedical sciences.Statistics.Health Sciences.Applied Statistics.610Cleophas Ton J472359Zwinderman Aeilko H721640MiAaPQMiAaPQMiAaPQBOOK9910918595103321Application of Regularized Regressions to Identify Novel Predictors in Clinical Research4305529UNINA01979nam 2200493 a 450 991095396320332120240516142352.097817804258561780425856(CKB)2670000000170790(Au-PeEL)EBL887047(CaPaEBR)ebr10622108(CaONFJC)MIL427082(OCoLC)784886892(MiAaPQ)EBC887047(Perlego)4524177(EXLCZ)99267000000017079020121124d2011 uy 0porurcn|||||||||txtrdacontentcrdamediacrrdacarrierAuguste Renoir /[Nathalia Brodskaya ; Tradução, Ana Maria Quadrado Pardal]1st ed.[New York] Parkstone International[2011]80 p. ill., portsPerfect Square9789728819330 9728819331 Includes bibliographical references.Intro -- Auguste Renoir -- LISTA DAS ILUSTRAÇOES -- NOTAS.Em cada período histórico revela-se uma nova definição da beleza feminina. As imagens da beleza feminina não só expressam o desejo íntimo dos pintores, mas revelam também os padrões de beleza contemporâneos. Um dos pintores que amou as mulheres, Renoir, reviu o corpo feminino do século XIX, conferindo-lhe um novo charme, principalmente através da utilização da côr. Nesta viagem pelas obras do artista, as mulheres de Renoir são apresentadas como criaturas de carne e osso, que sugerem ser capazes de dar e receber amor. PaintersFranceBiographyPaintersBrodskaya Nathalia1608524Pardal Ana Maria Quadrado1810593MiAaPQMiAaPQMiAaPQBOOK9910953963203321Auguste Renoir4361987UNINA