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With the increase in data processing and storage capacity, a large amount of data is available. Data without analysis does not have much value. Thus, the demand for data analysis is increasing daily, and the consequence is the appearance of a large number of jobs and published articles. Data science has emerged as a multidisciplinary field to support data-driven activities, integrating and developing ideas, methods, and processes to extract information from data. This includes methods built from different knowledge areas: Statistics, Computer Science, Mathematics, Physics, Information Science, and Engineering. This mixture of areas has given rise to what we call Data Science. New solutions to the new problems are reproducing rapidly to generate large volumes of data. Current and future challenges require greater care in creating new solutions that satisfy the rationality for each type of problem. Labels such as Big Data, Data Science, Machine Learning, Statistical Learning, and Artificial Intelligence are demanding more sophistication in the foundations and how they are being applied. This point highlights the importance of building the foundations of Data Science. This book is dedicated to solutions and discussions of measuring uncertainties in data analysis problems.
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This book discusses the problem of model choice when the statistical models are separate, also called nonnested. Chapter 1 provides an introduction, motivating examples and a general overview of the problem. Chapter 2 presents the classical or frequentist approach to the problem as well as several alternative procedures and their properties. Chapter 3 explores the Bayesian approach, the limitations of the classical Bayes factors and the proposed alternative Bayes factors to overcome these limitations. It also discusses a significance Bayesian procedure. Lastly, Chapter 4 examines the pure likelihood approach. Various real-data examples and computer simulations are provided throughout the text.
Statistics. --- Biostatistics. --- Econometrics. --- Statistical Theory and Methods. --- Statistics for Business/Economics/Mathematical Finance/Insurance. --- Statistics for Life Sciences, Medicine, Health Sciences. --- Statistics and Computing/Statistics Programs. --- Mathematical statistics. --- Mathematics --- Statistical inference --- Statistics, Mathematical --- Statistical methods --- Statistics --- Probabilities --- Sampling (Statistics) --- Statistical methods. --- Statistics for Business, Management, Economics, Finance, Insurance. --- Economics, Mathematical --- Statistical analysis --- Statistical data --- Statistical science --- Econometrics --- Statistics . --- Biological statistics --- Biology --- Biometrics (Biology) --- Biostatistics --- Biomathematics
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This book discusses the problem of model choice when the statistical models are separate, also called nonnested. Chapter 1 provides an introduction, motivating examples and a general overview of the problem. Chapter 2 presents the classical or frequentist approach to the problem as well as several alternative procedures and their properties. Chapter 3 explores the Bayesian approach, the limitations of the classical Bayes factors and the proposed alternative Bayes factors to overcome these limitations. It also discusses a significance Bayesian procedure. Lastly, Chapter 4 examines the pure likelihood approach. Various real-data examples and computer simulations are provided throughout the text.
Statistical science --- Quantitative methods (economics) --- Mathematical statistics --- Biomathematics. Biometry. Biostatistics --- Business economics --- Computer. Automation --- medische statistiek --- Bayesian statistics --- biostatistiek --- informatica --- statistiek --- biometrie --- econometrie --- statistisch onderzoek
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Abath, Ciro ; Ayesta, Ezequiel Padilla ; Barakat, Tayseer ; Blazeska, Violeta ; Boshoff, Willem ; Boxer, David Wayne ; Boyadjiev, Bojidar ; Branch, Winston ; Bruguera, Tania ; Burnside-Beadle-Burnside ; Caldas, Waltercio ; Canas, Carlos ; Capellan, Tony ; Carrilho ; Cavenago, Umberto ; Chichkan, Illya ; Claassen, Tom ; Cornet, Heleen ; Delgado, Leandro Silva ; Demur, Boris ; Duval-Carrié, Edouard ; Fink, Christoph ; García-Sevilla, Ferran ; Gardner, Joscelyn ; Grabuloski, Bogdan ; Hausswolff, Annika von ; Heske, Marianne ; Ho Siu-kee ; Hume, Gary ; Hydes, Bendel ; Jorarsaa ; Keskküla, Ando ; Kim, Tschoo-Su ; Klasmer, Gabriel ; Kobayashi, Masato ; Koller, Julius ; Latamie, Marc ; Lawaetz, Roy ; Lemmerz, Christian ; Leogane, Lucien ; LeWitt, Sol ; Lombardi, Inés ; Madi, Hussein ; Mezza, Gonzalo ; Moffatt, Tracey ; Mostafa, Ramzi ; Namizato, Eduardo Tokeshi ; Navalainen, Pekka ; Navridis, Nikos ; Ngui, Matthew ; O'Kelly, Alanna ; Otero, Néstor ; Palma, Luis Gonzalez ; Paraskos, Stass ; Patatitcs, Alexandru ; Penalva, João ; Pinto, Xavier Blum ; Restrepo, José Alejandro ; Robinot, Roseman ; Rodriguez, Juan Luis ; Rondinone, Ugo ; Sacco, Graciela ; Séchas, Alan ; Simoncic, Petra Varl ; Sirait, Marintan ; Soto, Jesus Rafael ; Spatuzza, Carlo ; Suter, Gerardo ; Tho, Tran ; Urbinati ; Veress, Zsolt ; Vidová-Zackova, Janka ; Wolff, Carl Emanuel ; Yingqi, Jiao ; Zelenka
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