Sentence examples for classes of regression from inspiring English sources

To get insight in the process we then modelled the experimental results with different classes of regression models; from modelling we could identify the special role played by some molecules and the relevance of their relative weight in the composition.

Traditional time series forecasting can be performed using several classes of regression models, as reviewed in De Gooijer and Hyndman (2006), while the two major classes are stochastic and machine learning.

In this work we present a new class of regression estimates called generalized PTS (GPTS).

We proposed a new class of regression models for location and scale based on the logarithm of the PCNB random variable.

Further details on this class of regression models can be found in Cox and Oakes (1984), Kalbfleisch and Prentice (2002) and Lawless (2003).

A class of regression models for location and scale is characterized by the fact that the random variable Y= log(Z) has a distribution with location parameter μ v), which depends only on the explanatory variable vector, and a scale parameter a.

A class of regression models for location and scale is characterized by the fact that the random variable Y= log(X) has a distribution with location parameter μ v) dependent only on the explanatory variable vector and a scale parameter σ.

One advantage of this process is that it can estimate a wider class of regression surfaces than parametric functions.

Adjusted model-based estimates of measures of association can be obtained resorting to a general class of regression models used in Survival Analysis called transformation models [ 31].

Partial least squares (PLS) is a class of regression methods for finding the fundamental relations between an input matrix (X) and a response matrix (Y).

Under the partial likelihood framework (2), we develop a class of regression models for zero-inflated and overdispersed count time series based on the conditional zero-inflated negative binomial (ZINB) distribution with probability mass function defined as follows: f f (y t ; k, μ t, π t | f t - 1 ) = π t I (y t = 0 ) + (1 - π t ) Γ (k + y t ) Γ (k ) y t !