Sentence examples for shrinking estimates from inspiring English sources

The lasso selects variables by shrinking estimates towards zero such that small coefficients will become exactly zero.

One way of confronting the MSE problem emerging in large -p with small -n regressions is by shrinking estimates toward a fixed point (e.g., 0); this may increase bias but reduces the variance of the estimator.

In this example we show how to fit models that induce variable selection and others that shrink estimates toward zero.

Consistent with this interpretation, both fastBayesB and our EM algorithm without accounting for the PEV shrink estimates of SNP effects more severely than emBayesR or BayesR.

The maximum likelihood estimator (mle) overestimates the prevalence; therefore, we use the Burrows estimator (a type of so-called "shrinking estimator") that shrinks the estimate slightly, to reduce the bias of our estimator.

Simulations definitely reflect this bias, with Bayesian estimates on average underestimating across the board for CNLOH regions and generally for single-gains shrinking the estimates toward.

While the overall children's clothing market in Japan has been shrinking, Yano estimates that the market for clothes for 10- to 15-year-olds, roughly the same as Narumiya International's target market, will grow to 401 billion yen in 2005 from 390 billion yen last year, in large part because of strong sales of the more expensive brands.

Bayesian Estimation For regions where the MLEs are approaching the boundary of the parameter space (estimates that are essentially 0 or 1) the Bayesian estimates, as expected, shrink the estimates away from the boundary and toward the prior mean.

The limma package uses an empirical Bayes approach to create a moderated t-statistic by shrinking the estimated sample variances towards a pooled estimate thereby allowing more stable inference than an ordinary t-statistic when the sample size is small.

By shrinking the estimate, although this introduces bias, the estimation error can be decreased.

By jointly minimizing the sum of squared errors and shrinking some estimated regression coefficients to zero, it seeks a model with accurate estimates in terms of mean squared error properties, especially when the exposure-response association is weak or multiple significant pollutants exist, as illustrated in the simulation studies and examples.