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- W2070792261 abstract "A methodology is developed to assign, from an observed sample, a joint-probability distribution to a set of continuous variables. The algorithm proposed performs this assignment by mapping the original variables onto a jointly-Gaussian set. The map is built iteratively, ascending the log-likelihood of the observations, through a series of steps that move the marginal distributions along a random set of orthogonal directions towards normality. AMS subject classifications. 34A50, 65C30, 65L20, 60H35. 1. Introduction and setting Extracting information from is a fundamental underlying many ap- plications. Medical doctors seek to diagnose a patient's health from clinical data, blood tests and genetic information. Pharmaceutical companies analyze the results of massive in vitro tests of different compounds to select the best candidate for new drug development. Insurance companies assess, based on financial data, the probabil- ity that a number of credit-lines go into default within the same time-window. Using commercial data, market analysts attempt to quantify the effect that advertising cam- paigns have on sales. Weather forecasters extract from present and past observations the likely state of the weather in the near future. Climate scientists estimate long-time trends from observations over the years of quantities such as sea-surface temperature and the atmospheric concentration of CO2. In many of these applications, the fundamental data problem consists of es- timating, from a sample of a set of interdependent variables, their joint probability distribution. Thus, the financial analyst dealing in credit derivatives seeks the proba- bility of joint default of many debts over a specified time window; the medical doctor, the likelihood that a patient's test results are associated with a certain disease; the weather forecaster, the likelihood that the pattern of today's measurements anticipate tomorrow's rain." @default.
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- W2070792261 date "2010-01-01" @default.
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- W2070792261 title "Density estimation by dual ascent of the log-likelihood" @default.
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- W2070792261 doi "https://doi.org/10.4310/cms.2010.v8.n1.a11" @default.
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