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This book provides a self-contained, linear, and unified introduction to empirical processes and semiparametric inference. These powerful research techniques are surprisingly useful for developing methods of statistical inference for complex models and in understanding the properties of such methods. The targeted audience includes statisticians, biostatisticians, and other researchers with a background in mathematical statistics who have an interest in learning about and doing research in empirical processes and semiparametric inference but who would like to have a friendly and gradual introduction to the area. The book can be used either as a research reference or as a textbook. The level of the book is suitable for a second year graduate course in statistics or biostatistics, provided the students have had a year of graduate level mathematical statistics and a semester of probability. The book consists of three parts. The first part is a concise overview of all of the main concepts covered in the book with a minimum of technicalities. The second and third parts cover the two respective main topics of empirical processes and semiparametric inference in depth. The connections between these two topics is also demonstrated and emphasized throughout the text. Each part has a final chapter with several case studies that use concrete examples to illustrate the concepts developed so far. The last two parts also each include a chapter which covers the needed mathematical preliminaries. Each main idea is introduced with a non-technical motivation, and examples are given throughout to illustrate important concepts. Homework problems are also included at the end of each chapter to help the reader gain additional insights. Michael R. Kosorok is Professor and Chair, Department of Biostatistics, and Professor, Department of Statistics and Operations Research, at the University of North Carolina at Chapel Hill. His research has focused on the application of empirical processes and semiparametric inference to statistics and biostatistics. He is a Fellow of both the American Statistical Association and the Institute of Mathematical Statistics. He is an Associate Editor of the Annals of Statistics, Electronic Journal of Statistics, International Journal of Biostatistics, Statistics and Probability Letters, and Statistics Surveys.

statistisch onderzoek --- Statistical science --- Mathematical statistics --- Stochastic processes --- Sampling (Statistics) --- Probabilities --- Estimation theory --- Convergence --- Convergence. --- Estimation theory. --- Probabilities. --- Sampling (Statistics). --- Stochastic processes.

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Understanding ore textures is fundamental to unraveling the genesis of an ore deposit, which in turn allows exploration and mining geologists to build their conceptual models of the deposits they encounter and leads to more successful exploration and exploitation. This book is specifically designed for the field geologist working without the benefits of sophisticated chemical, mineralogical or petrological support. It covers the basic building blocks of textural recognition beginning with infill (direct precipitation from hydrothermal fluids into 'cavities'), alteration (the results of hydrothermal fluid reactions with wall rocks) and overprinting (the normal complexity caused by successive introduction of hydrothermal fluids usually accompanied or preceded by renewed fracturing) and ends with a detailed examination of textures associated with tectonic and intrusive breccias.

Earth Sciences. --- Mineralogy. --- Economic Geology. --- Characterization and Evaluation of Materials. --- Geography. --- Geology, economic. --- Surfaces (Physics). --- Géographie --- Minéralogie --- Surfaces (Physique) --- Ores --- Mineralogy, Determinative. --- Geology, Economic. --- Ore deposits. --- Sampling and estimation. --- Ores --Identification. --- Ores --Sampling and estimation. --- Mineralogy, Determinative --- Geology, Economic --- Ore deposits --- Geology --- Earth & Environmental Sciences --- Mineralogy --- Sampling and estimation --- Sampling of ores --- Deposits, Ore --- Chemical geology --- Determinative mineralogy --- Geology, Chemical --- Economic geology --- Analysis --- Earth sciences. --- Economic geology. --- Materials science. --- Assaying --- Mines and mineral resources --- Prospecting --- Minerals --- Metallurgy --- Geochemistry --- Metallurgical analysis --- Chemistry, Analytic --- Physical geology --- Physics --- Surface chemistry --- Surfaces (Technology) --- Crystallography --- Material science --- Physical sciences

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Mathematical statistics. --- Statistiek --- Mathematical statistics --- 519.2 --- biologie --- milieu --- statistiek --- statistieken --- wiskunde --- Mathematics --- Statistical inference --- Statistics, Mathematical --- Statistics --- Probabilities --- Sampling (Statistics) --- statistiek (wiskunde) --- Statistical methods --- Statistique mathématique --- Statistique mathématique --- Biométrie

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Increased attention is being paid to the need for statistically educated citizens: statistics is now included in the K-12 mathematics curriculum, increasing numbers of students are taking courses in high school, and introductory statistics courses are required in college. However, increasing the amount of instruction is not sufficient to prepare statistically literate citizens. A major change is needed in how statistics is taught. To bring about this change, three dimensions of teacher knowledge need to be addressed: their knowledge of statistical content, their pedagogical knowledge, and their statistical-pedagogical knowledge, i.e., their specific knowledge about how to teach statistics. This book is written for mathematics and statistics educators and researchers. It summarizes the research and highlights the important concepts for teachers to emphasize, and shows the interrelationships among concepts. It makes specific suggestions regarding how to build classroom activities, integrate technological tools, and assess students' learning. This is a unique book. While providing a wealth of examples through lessons and data sets, it is also the best attempt by members of our profession to integrate suggestions from research findings with statistics concepts and pedagogy. The book's message about the importance of listening to research is loud and clear, as is its message about alternative ways of teaching statistics. This book will impact instructors, giving them pause to consider: "Is what I'm doing now really the best thing for my students? What could I do better?" J. Michael Shaughnessy, Professor, Dept of Mathematical Sciences, Portland State University, USA This is a much-needed text for linking research and practice in teaching statistics. The authors have provided a comprehensive overview of the current state-of-the-art in statistics education research. The insights they have gleaned from the literature should be tremendously helpful for those involved in teaching and researching introductory courses. Randall E. Groth, Assistant Professor of Mathematics Education, Salisbury University, USA

Cognitive psychology --- Statistical science --- Didactics --- Didactics of mathematics --- Higher education --- HO (hoger onderwijs) --- didactiek --- cognitieve psychologie --- statistiek --- wiskunde --- Mathematical statistics --- Mathematics --- Statistical inference --- Statistics, Mathematical --- Statistics --- Probabilities --- Sampling (Statistics) --- Study and teaching --- Statistical methods

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Following the classical sampling theory, the survey statistician selects samples of people, businesses or others, in order to obtain the desired information. Drawing the samples is usually done by randomly selecting from a list representing the target population. In practice, this list is often not available. At best, the statistician only has access to a different list, indirectly related to the targeted population. The example of a survey of children where the statistician only has a list of adult persons is a typical case. In this case, the statistician first draws a sample of adults, and for each selected adult, the statistician then identifies his/her children. The survey is done from the latter. This is what is called indirect sampling. When indirect sampling is used jointly with the sampling of clusters of persons (families, for example), many complications arise for the survey statistician. One of the complications relates to the computation of the estimates from the survey. The production of estimates of simple totals or means can then become nightmares for the survey statistician. To solve this problem, the author proposes a simple solution, easy to implement, that is called the generalised weight share method. This book is the reference on indirect sampling and the generalised weight share method. It contains the different developments done by the author on these subjects. The theory surrounding them is presented, but also different possible applications that drive its interest. The reader will find in this book the answer to questions that come, inevitably, when working in a context of indirect sampling. Pierre Lavallée has been a survey statistician at Statistics Canada since 1985. He gas worked in social, business, and agricultural surveys. He has also worked for Eurostat in Luxembourg.

Mathematical statistics --- Statistics. --- Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law. --- Statistical Theory and Methods. --- Population Economics. --- Quality of Life Research. --- Demography. --- Methodology of the Social Sciences. --- Quality of Life. --- Mathematical statistics. --- Population. --- Social sciences --- Quality of Life --- Statistique --- Statistique mathématique --- Population --- Sciences sociales --- Démographie --- Methodology. --- Research. --- Méthodologie --- Sampling (Statistics) --- 519.2 --- Probability. Mathematical statistics --- Mathematics. --- Sampling (Statistics). --- Mathematical Statistics --- Mathematics --- Physical Sciences & Mathematics --- 519.2 Probability. Mathematical statistics --- Random sampling --- Statistics of sampling --- Medical research. --- Social sciences. --- Quality of life. --- Statistics for Social Science, Behavioral Science, Education, Public Policy, and Law. --- Historical demography --- Vital statistics --- Life, Quality of --- Economic history --- Human ecology --- Life --- Social history --- Basic needs --- Human comfort --- Social accounting --- Work-life balance --- Behavioral sciences --- Human sciences --- Sciences, Social --- Social science --- Social studies --- Civilization --- Human population --- Human populations --- Population growth --- Populations, Human --- Economics --- Sociology --- Demography --- Malthusianism --- Biomedical research --- Medical research --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Econometrics --- Statistics --- Statistics for Social Sciences, Humanities, Law. --- Statistical inference --- Statistics, Mathematical --- Probabilities --- Statistics .