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Technology development has led to a growing availability of low-cost data ready-to-use, frequently derived from large scale observations (i.e. data from pervasive systems like GPS sensors, or remote sensing data from earth observation technologies). Oftentimes, these data can't directly answer specific questions posed by researchers and data users, or even if they can they are subject to measurement errors or self-selection bias. In both cases it is still necessary to rely, at least partially, on ad-hoc probabilistic surveys. On the other hand, the precision and quality of surveys estimates can be improved by using the data derived from these new sources as auxiliary information in the design phase and/or in the estimation phase. We present a sequential sampling strategy, suitable to investigate a spatially-related phenomenon, which exploits the auxiliary information at design level in order to obtain efficient estimates when the relation between the auxiliary and study variables it is not completely known and/or is not univocally defined for the whole population under study. Using this strategy the final sample is obtained after two (or more) steps: (i) in the first step we collect an initial sample of observations on the target variable, which is used also to investigate the relation between the auxiliary and study variables; (ii) then, this relation is exploited to target and tailor the subsequent sampling step; (iii) additional steps can be included by applying the procedure iteratively. The performance of the suggested strategy is investigated through Monte Carlo experiments by considering several scenarios, which differ in the distributions of the auxiliary and study variables and in their relation.

Social sciences --- Spatial data infrastructures. --- Probabilities. --- Societies, etc.

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Wonder in literature. --- Wonder (Philosophy)

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This book focuses on methods and models in classification and data analysis and presents real-world applications at the interface with data science. Numerous topics are covered, ranging from statistical inference and modelling to clustering and factorial methods, and from directional data analysis to time series analysis and small area estimation. The applications deal with new developments in a variety of fields, including medicine, finance, engineering, marketing, and cyber risk. The contents comprise selected and peer-reviewed contributions presented at the 13th Scientific Meeting of the Classification and Data Analysis Group of the Italian Statistical Society, CLADAG 2021, held (online) in Florence, Italy, on September 9–11, 2021. CLADAG promotes advanced methodological research in multivariate statistics with a special focus on data analysis and classification, and supports the exchange and dissemination of ideas, methodological concepts, numerical methods, algorithms, and computational and applied results at the interface between classification and data science.

Mathematical statistics—Data processing. --- Quantitative research. --- Machine learning. --- Statistics. --- Artificial intelligence—Data processing. --- Statistics and Computing. --- Data Analysis and Big Data. --- Statistical Learning. --- Statistical Theory and Methods. --- Applied Statistics. --- Data Science. --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- Learning, Machine --- Artificial intelligence --- Machine theory --- Data analysis (Quantitative research) --- Exploratory data analysis (Quantitative research) --- Quantitative analysis (Research) --- Quantitative methods (Research) --- Research --- Dades massives --- Estadística matemàtica

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This book focuses on methods and models in classification and data analysis and presents real-world applications at the interface with data science. Numerous topics are covered, ranging from statistical inference and modelling to clustering and factorial methods, and from directional data analysis to time series analysis and small area estimation. The applications deal with new developments in a variety of fields, including medicine, finance, engineering, marketing, and cyber risk. The contents comprise selected and peer-reviewed contributions presented at the 13th Scientific Meeting of the Classification and Data Analysis Group of the Italian Statistical Society, CLADAG 2021, held (online) in Florence, Italy, on September 9-11, 2021. CLADAG promotes advanced methodological research in multivariate statistics with a special focus on data analysis and classification, and supports the exchange and dissemination of ideas, methodological concepts, numerical methods, algorithms, and computational and applied results at the interface between classification and data science.

Statistical science --- Mathematical statistics --- Programming --- Artificial intelligence. Robotics. Simulation. Graphics --- Computer. Automation --- informatica --- statistiek --- programmeren (informatica) --- KI (kunstmatige intelligentie) --- statistisch onderzoek