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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 .

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Drawing --- France