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[null,"1.","Record Nr.",null,"UNISALENTO991000546689707536"]
[null,null,"Autore",null,"Ottonello, Giacomo"]
[null,null,"Titolo",null,"La scuola d'umanita di Paul Geheeb / Giacomo Ottonello"]
[null,null,"Pubbl/distr/stampa",null,"Firenze : La nuova Italia, 1966"]
[null,null,"Descrizione fisica",null,"271 p., [4] c. di tav. : ill. ; 20 cm"]
[null,null,"Collana",null,"Educatori antichi e moderni"]
[null,null,"Disciplina",null,"370.109"]
[null,null,"Soggetti",null,"Geheeb, Paul"]
[null,null,"Lingua di pubblicazione",null,"Italiano"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,"2.","Record Nr.",null,"UNINA9910918595103321"]
[null,null,"Autore",null,"Cleophas Ton J"]
[null,null,"Titolo",null,"Application of Regularized Regressions to Identify Novel Predictors in Clinical Research / / by Ton J. Cleophas, Aeilko H. Zwinderman"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024"]
[null,null,"ISBN",null,"9783031722479"]
[{"$nulls":4},"3031722477"]
[null,null,"Edizione",null,"[1st ed. 2024.]"]
[null,null,"Descrizione fisica",null,"1 online resource (274 pages)"]
[null,null,"Disciplina",null,"610"]
[null,null,"Soggetti",null,"Medical sciences"]
[{"$nulls":4},"Statistics"]
[{"$nulls":4},"Health Sciences"]
[{"$nulls":4},"Applied Statistics"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"-- 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 "]
[{"$nulls":4},"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."]
[null,null,null,"Sommario/riassunto","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."]