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- nonlinear regression model and generalized least squares estimates for a multivariate nonlinear regression model ... both. Other options can be set to correct variance estimates for heteroskedasticity and/or serial correlation...
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- value estimation for DNA microarrays. Motivation: Gene expression microarray experiments can generate data sets with ... increase the range of data sets to which these algorithms can be applied. In this ... report, we investigate automated methods for estimating missing data. Results: We present a comparative study ... several methods for the estimation of missing values in gene microarray data. We implemented...
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- random variates. It also performs maximum likelihood estimation of stable parameters and some exploratory data ... data set. This work is described in the paper ”Maximum likelihood estimation of stable parameters...
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- reordering of the Schur factorizations and estimating condition numbers. Dense and banded matrices are handled ... exploit the Level 3 BLAS — a set of specifications for Fortran subprograms that do various...
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- FRSDE: Fast reduced set density estimator using minimal enclosing ball approximation. Reduced Set Density Estimator ... used to replace the classical Parzen Window estimator (PW) for saving the computational cost. Though ... model weighting coefficients on large data sets. In order ... overcome this shortcoming, a Fast Reduced Set Density Estimator algorithm (FRSDE) is proposed in this...
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- Smoothing spline ANOVA models Nonparametric function estimation with stochastic data, otherwise known as smoothing ... univariate smoothing, ones practical in multivariate settings number far less. Smoothing spline ANOVA models ... time data; (ii) density and conditional density estimation under a variety of sampling schemes...
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- set because it maintains an archive of nondominated solutions which it exploits to estimate accurately...
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- they are conservatively biased. Almost unbiased estimates provides leave-one-out testing. SVMlight exploits that ... examples, so that it orders a new set of objects as accurately as possible. Such...
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- saery: Small Area Estimation for Rao and Yu Model. A complete set of functions...
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- tridiagonal linear systems of equations, condition estimation and iterative refinement, for LU and Cholesky factorization ... computers, example programs, and the full set of LAPACK Working Notes...
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- imputation error estimate without the need of a test set or elaborate cross-validation...
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- high dimensional (p>n) regression estimator, the linear regression noise level and the genetic signal ... intervals, including a comparison with a Bayesian setting in which our interval is just ... apply EigenPrism to a genetic data set to estimate the genetic signal-to-noise ratio...