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[null,"1.","Record Nr.",null,"UNINA9910830227303321"]
[null,null,"Autore",null,"Lidow Alex"]
[null,null,"Titolo",null,"GaN transistors for efficient power conversion / / Alex Lidow, Michael de Rooij, Johan Strydom, David Reusch, John Glaser"]
[null,null,"Pubbl/distr/stampa",null,"Hoboken, NJ : , : John Wiley & Sons, Inc., , 2020"]
[null,null,"ISBN",null,"1-5231-2819-4"]
[{"$nulls":4},"1-119-59440-5"]
[{"$nulls":4},"1-119-59437-5"]
[{"$nulls":4},"1-119-59442-1"]
[null,null,"Edizione",null,"[Third edition.]"]
[null,null,"Disciplina",null,"621.3815284"]
[null,null,"Soggetti",null,"Field-effect transistors - Materials"]
[{"$nulls":4},"Power transistors - Materials"]
[{"$nulls":4},"Gallium nitride"]
[{"$nulls":4},"Transistors à effet de champ - Matériaux"]
[{"$nulls":4},"Transistors de puissance - Matériaux"]
[{"$nulls":4},"Nitrure de gallium"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","\"Written by leaders in the power semiconductor field and industry pioneers in GaN power transistor technology and applications. 35% new material including 3 new chapters on Thermal Management, Multi-Level Converters and LiDAR - Features practical guidance on formulating specific circuit designs when constructing power conversion systems using GaN transistors. A valuable learning resource for professional engineers and systems designers needing to fully understand new devices as well as electrical engineering students\"-- Provided by publisher."]
[null,"2.","Record Nr.",null,"UNINA9910810323603321"]
[null,null,"Autore",null,"Brown Helen <1962->"]
[null,null,"Titolo",null,"Applied mixed models in medicine / / Helen Brown, Robin Prescott"]
[null,null,"Pubbl/distr/stampa",null,"Chichester, England : , : Wiley, , 2015"]
[{"$nulls":4},"©2015"]
[null,null,"ISBN",null,"1-118-77824-3"]
[{"$nulls":4},"1-322-52060-7"]
[{"$nulls":4},"1-118-77823-5"]
[{"$nulls":4},"1-118-77821-9"]
[null,null,"Edizione",null,"[Third edition.]"]
[null,null,"Descrizione fisica",null,"1 online resource (539 p.)"]
[null,null,"Collana",null,"Statistics in Practice"]
[null,null,"Disciplina",null,"610.72/7"]
[null,null,"Soggetti",null,"Medicine"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Cover; Title Page; Copyright; Contents; Preface to third edition; Mixed models notation; About the Companion Website; Chapter 1 Introduction; 1.1 The use of mixed models; 1.2 Introductory example; 1.2.1 Simple model to assess the effects of treatment (Model A); 1.2.2 A model taking patient effects into account (Model B); 1.2.3 Random effects model (Model C); 1.2.4 Estimation (or prediction) of random effects; 1.3 A multi-centre hypertension trial; 1.3.1 Modelling the data; 1.3.2 Including a baseline covariate (Model B); 1.3.3 Modelling centre effects (Model C)"]
[{"$nulls":4},"1.3.4 Including centre-by-treatment interaction effects (Model D)1.3.5 Modelling centre and centre·treatment effects as random (Model E); 1.4 Repeated measures data; 1.4.1 Covariance pattern models; 1.4.2 Random coefficients models; 1.5 More about mixed models; 1.5.1 What is a mixed model?; 1.5.2 Why use mixed models?; 1.5.3 Communicating results; 1.5.4 Mixed models in medicine; 1.5.5 Mixed models in perspective; 1.6 Some useful definitions; 1.6.1 Containment; 1.6.2 Balance; 1.6.3 Error strata; Chapter 2 Normal mixed models; 2.1 Model definition; 2.1.1 The fixed effects model"]
[{"$nulls":4},"2.1.2 The mixed model2.1.3 The random effects model covariance structure; 2.1.4 The random coefficients model covariance structure; 2.1.5 The covariance pattern model covariance structure; 2.2 Model fitting methods; 2.2.1 The likelihood function and approaches to its maximisation; 2.2.2 Estimation of fixed effects; 2.2.3 Estimation (or "]
[{"$nulls":4},"prediction) of random effects and coefficients; 2.2.4 Estimation of variance parameters; 2.3 The Bayesian approach; 2.3.1 Introduction; 2.3.2 Determining the posterior density; 2.3.3 Parameter estimation, probability intervals and p-values"]
[{"$nulls":4},"2.3.4 Specifying non-informative prior distributions2.3.5 Evaluating the posterior distribution; 2.4 Practical application and interpretation; 2.4.1 Negative variance components; 2.4.2 Accuracy of variance parameters; 2.4.3 Bias in fixed and random effects standard errors; 2.4.4 Significance testing; 2.4.5 Confidence intervals; 2.4.6 Checking model assumptions; 2.4.7 Missing data; 2.4.8 Determining whether the simulated posterior distribution has converged; 2.5 Example; 2.5.1 Analysis models; 2.5.2 Results; 2.5.3 Discussion of points from Section 2.4; Chapter 3 Generalised linear mixed models"]
[{"$nulls":4},"3.1 Generalised linear models3.1.1 Introduction; 3.1.2 Distributions; 3.1.3 The general form for exponential distributions; 3.1.4 The GLM definition; 3.1.5 Fitting the GLM; 3.1.6 Expressing individual distributions in the general exponential form; 3.1.7 Conditional logistic regression; 3.2 Generalised linear mixed models; 3.2.1 The GLMM definition; 3.2.2 The likelihood and quasi-likelihood functions; 3.2.3 Fitting the GLMM; 3.3 Practical application and interpretation; 3.3.1 Specifying binary data; 3.3.2 Uniform effects categories; 3.3.3 Negative variance components"]
[{"$nulls":4},"3.3.4 Presentation of fixed and random effects estimates"]
[null,null,null,"Sommario/riassunto","A fully updated edition of this key text on mixed models, focusing on applications in medical research     The application of mixed models is an increasingly popular way of analysing medical data, particularly in the pharmaceutical industry. A mixed model allows the incorporation of both fixed and random variables within a statistical analysis, enabling efficient inferences and more information to be gained from the data. There have been many recent advances in mixed modelling, particularly regarding the software and applications. This third edition of Brown and Prescott's groundbreaking text"]