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We wrote this book to introduce graduate students and research workers in var­ ious scientific disciplines to the use of information-theoretic approaches in the analysis of empirical data. In its fully developed form, the information-theoretic approach allows inference based on more than one model (including estimates of unconditional precision); in its initial form, it is useful in selecting a "best" model and ranking the remaining models. We believe that often the critical issue in data analysis is the selection of a good approximating model that best represents the inference supported by the data (an estimated "best approximating model"). In­ formation theory includes the well-known Kullback-Leibler "distance" between two models (actually, probability distributions), and this represents a fundamental quantity in science. In 1973, Hirotugu Akaike derived an estimator of the (relative) Kullback-Leibler distance based on Fisher's maximized log-likelihood. His mea­ sure, now called Akaike 's information criterion (AIC), provided a new paradigm for model selection in the analysis of empirical data. His approach, with a funda­ mental link to information theory, is relatively simple and easy to use in practice, but little taught in statistics classes and far less understood in the applied sciences than should be the case. We do not accept the notion that there is a simple, "true model" in the biological sciences.

Mathematical statistics --- 519.226 --- 519.72 --- Inference and decision theory. Likelihood. Bayesian theory. Fiducial probability --- Information theory: mathematical aspects --- Biology --- Mathematical statistics. --- Mathematical models. --- 519.72 Information theory: mathematical aspects --- 519.226 Inference and decision theory. Likelihood. Bayesian theory. Fiducial probability --- Mathematics --- Statistical inference --- Statistics, Mathematical --- Statistics --- Probabilities --- Sampling (Statistics) --- Biological models --- Biomathematics --- Mathematical models --- Statistical methods --- Statistics . --- Statistical Theory and Methods. --- Statistical analysis --- Statistical data --- Statistical science --- Econometrics --- Biology. --- Life sciences --- Life (Biology) --- Natural history --- Biology - Mathematical models

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This advanced text focuses on the uses of distance sampling to estimate the density and abundance of biological populations. It addresses new methodologies, new technologies and recent developments in statistical theory and is the follow-up companion to Introduction to Distance Sampling (OUP, 2001). In this text, a general theoretical basis is established for methods of estimating animal abundance from sighting surveys, and a wide range of approaches to the design and analysis of distance sampling surveys is explored. These approaches include: modelling animal detectability as a function of covariates, where the effects of habitat, observer, weather, etc. on detectability can be assessed; estimating animal density as a function of location, allowing for example animal density to be related to habitat and other locational covariates; estimating change over time in population abundance, a necessary aspect of any monitoring programme; estimation when detection of animals on the line or at the point is uncertain, as often occurs for marine populations, or when the survey region has dense cover; automated generation of survey designs, using geographic information systems; adaptive distance sampling methods, which concentrate survey effort in areas of high animal density; passive distance sampling methods, which extend the application of distance sampling to species that cannot be readily detected in sightings surveys, but can be trapped; and testing of methods by simulation, so the performance of the approach in varying circumstances can be assessed. Authored by a leading team, this text is aimed at professionals in government and environment agencies, statisticians, biologists, wildlife managers, conservation biologists and ecologists, as well as graduate students, studying the density and abundance of biological populations.

Population biology --- Sampling (Statistics) --- Biology --- Health & Biological Sciences --- Biology - General --- Random sampling --- Statistics of sampling --- Statistics --- Mathematical statistics --- Statistical methods --- Statistical methods. --- Sampling (Statistics). --- Sampling (Statisticts)