ENCYCLOPEDIA OF STATISTICAL SCIENCES

ENCYCLOPEDIA OF STATISTICAL SCIENCES

ENCYCLOPEDIA OF STATISTICAL SCIENCES

ENCYCLOPEDIA OF STATISTICAL SCIENCES

ENCYCLOPEDIA OF STATISTICAL SCIENCES

"This defining work will be valuable to readers and researchers in social sciences and humanities at all academic levels. As a teaching resource it will be useful to instructors and students alike and will become a standard reference source. Essential for general and academic collections." --CHOICE This Encyclopedia provides readers with authoritative essays on virtually all social science methods topics, quantitative and qualitative, by an international collection of experts. Organized alphabetically, the Encyclopedia of Social Science Research Methods covers research terms ranging from different methodological approaches to epistemological issues and specific statistical techniques. Written to be accessible to general readers, the Encyclopedia entries do not require advanced knowledge of mathematics or statistics to understand the purposes or basic principles of any of the methods. To accomplish this goal, there are two major types of entries: definitions consisting of a paragraph or two to provide a quick explanation of a methodological term; and topical treatments or essays that discuss the nature, history, applications, and implications of using a certain method, including suggested readings and references. Readers are directed to related topics via cross-referenced terms that appear in small capital letters. By assembling entries of varied origins and serving different research purposes, readers will be able to benefit from this immense source of methodological expertise in advancing their understanding of research. With three volumes and more than 900 signed entries, the Encyclopedia of Social Science Research Methods will be a critical addition to any social science library.

Big data and machine learning are driving the Fourth Industrial Revolution. With the age of big data upon us, we risk drowning in a flood of digital data. Big data has now become a critical part of both the business world and daily life, as the synthesis and synergy of machine learning and big data has enormous potential. Big data and machine learning are projected to not only maximize citizen wealth, but also promote societal health. As big data continues to evolve and the demand for professionals in the field increases, access to the most current information about the concepts, issues, trends, and technologies in this interdisciplinary area is needed. The Encyclopedia of Data Science and Machine Learning examines current, state-of-the-art research in the areas of data science, machine learning, data mining, and more. It provides an international forum for experts within these fields to advance the knowledge and practice in all facets of big data and machine learning, emphasizing emerging theories, principals, models, processes, and applications to inspire and circulate innovative findings into research, business, and communities. Covering topics such as benefit management, recommendation system analysis, and global software development, this expansive reference provides a dynamic resource for data scientists, data analysts, computer scientists, technical managers, corporate executives, students and educators of higher education, government officials, researchers, and academicians.

I suppose Joseph Agassi's best and dearest self-description, his cher ished wish, is to practice what his 1988 book promises: The Gentle Art of Philosophical Polemics. But for me, and for so many who know him, our Agassi is tough-minded, not tender, not so gentle. True to his beloved critical thinking, he is ever the falsificationist, testing himself of course as much as everyone else. How, he asks himself, can he engage others in their own self-critical exploration? Irritate? Question their logic, their facts, their presuppositions, their rationales? Subvert their reasoning, uncover their motives? Help them to lose their balance, but always help them, make them do it to, and for, themselves. Out of their own mouths, and minds, and imagination. A unique teacher, in classroom and out; not for everyone. Agassi is not quite a tight textual Talmudist disputant, not quite the competitor in the marketplace of ideas offered for persuasive sale, not quite the clever cross-examining lawyer advocate, not quite a philosopher-scientist, not a sceptic more than necessary, not quite embat tled in the bloody world but not ever above the battle either . . . but a good deal of all of these, and steeped in intelligence and good will.

In 1945, very early in the history of the development of a rigorous analytical theory of probability, Feller (1945) wrote a paper called “The fundamental limit theorems in probability” in which he set out what he considered to be “the two most important limit theorems in the modern theory of probability: the central limit theorem and the recently discovered ... ‘Kolmogoroff’s cel ebrated law of the iterated logarithm’ ”. A little later in the article he added to these, via a charming description, the “little brother (of the central limit theo rem), the weak law of large numbers”, and also the strong law of large num bers, which he considers as a close relative of the law of the iterated logarithm. Feller might well have added to these also the beautiful and highly applicable results of renewal theory, which at the time he himself together with eminent colleagues were vigorously producing. Feller’s introductory remarks include the visionary: “The history of probability shows that our problems must be treated in their greatest generality: only in this way can we hope to discover the most natural tools and to open channels for new progress. This remark leads naturally to that characteristic of our theory which makes it attractive beyond its importance for various applications: a combination of an amazing generality with algebraic precision.