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Estimation of truncated regression models is usually done via parametric maximum likelihood method. More recently, various semi-parametric and non-parametric generalisation were proposed in the literature, e.g., based on the local least squares approach [5] or the local maximum likelihood approach, [6] which are kernel based methods.
The following outline is provided as an overview of and topical guide to regression analysis: Regression analysis – use of statistical techniques for learning about the relationship between one or more dependent variables ( Y ) and one or more independent variables ( X ).
Given an r-sample statistic, one can create an n-sample statistic by something similar to bootstrapping (taking the average of the statistic over all subsamples of size r). This procedure is known to have certain good properties and the result is a U-statistic. The sample mean and sample variance are of this form, for r = 1 and r = 2.
The taxonomic status of the common tropical western Atlantic venerid bivalve, Chione cancellata, was radically revised in 2000.What had previously been thought to be one species was discovered to be a "cryptic species pair" and as such it was divided into two separate species, on the basis of morphological, morphometric and phylogenetic analyses.
Megapitaria squalida, the chocolate clam, is a species of bivalve mollusc in the family Veneridae. It was first described to science by George Brettingham Sowerby , a British conchologist , in 1835.
Some pea clams (genus Pisidium) have an adult size of only 3 mm (0.12 in). In contrast, one of the largest species of freshwater bivalves is the swan mussel from the family Unionidae ; it can grow to a length of 20 cm (7.9 in), and usually lives in lakes or slow-flowing rivers.
Corbicula fluminea is commonly known in the west as the Asian clam, Asiatic clam, or Asian gold clam. In Southeast Asia, C. fluminea is known as the golden clam, prosperity clam, pygmy clam, or good luck clam. In New Zealand, it is commonly referred as the freshwater gold clam. [2] [3]
In statistics, least-angle regression (LARS) is an algorithm for fitting linear regression models to high-dimensional data, developed by Bradley Efron, Trevor Hastie, Iain Johnstone and Robert Tibshirani. [1] Suppose we expect a response variable to be determined by a linear combination of a subset of potential covariates.