carbon sequestration into plants biomass, especially in fast growing trees is an easier and economically way for dropping off co2 from atmosphere. this study was carried out in order to investigate above-ground biomass of white poplar (populous alba, l.) plantations that was planted in fourdifferent plant spacing (0.5 × 0.5, 1×1, 2×2 and 4×4 m.) in chaharmahal and bakhtiari province in west of ...

abstract spatial prediction of soil organic carbon is a crucial proxy to manage and conserve natural resources, monitoring co2 and preventing soil erosion strategies within the landscape, regional, and global scale. the objectives of this study was to evaluate capability of artificial neural network and multivariate linear regression models in order to predict soil organic carbon using terrain ...

histamine h3 receptor subtype has been the target of several recent drug development programs. quantitative structure-activity relationship (qsar) methods are used to predict the pharmaceutically relevant properties of drug candidates whenever it is applicable. the aim of this study was to compare the predictive powers of three different qsar techniques, namely, multiple linear regression (mlr)...

Among the alternative Unobserved Components formulations within the stochastic state space setting, the Dynamic Harmonic Regression (DHR) model has proven to be particularly useful for adaptive seasonal adjustment, signal extraction, forecasting and back-casting of time series. First, it is shown how to obtain ARMA representations for the DHR components under a Generalized Random Walk setting f...

Clustered linear regression (CLR) is a new machine learning algorithm that improves the accuracy of classical linear regression by partitioning training space into subspaces. CLR makes some assumptions about the domain and the data set. Firstly, target value is assumed to be a function of feature values. Second assumption is that there are some linear approximations for this function in each su...

This dissertation studies the least squares estimator of a trend parameter in a simple linear regression model with multiple changepoints when the changepoint times are known. The error component in the model is allowed to be autocorrelated. The least squares estimator of the trend and the variance of the trend estimator are derived. Consistency and asymptotic normality of the trend estimator a...

PLS univariate regression is a model linking a dependent variable y to a set X= {x1; : : : ; xp} of (numerical or categorical) explanatory variables. It can be obtained as a series of simple and multiple regressions. By taking advantage from the statistical tests associated with linear regression, it is feasible to select the signi6cant explanatory variables to include in PLS regression and to ...

Designing machine learning algorithms that are robust to noise in training data has lately been a subject of intense research. A large body of work addresses stochastic noise [12, 7], while another one studies adversarial noise [11, 2] in which errors are introduced by an adversary with the explicit purpose of sabotaging the algorithm. This is often too pessimistic, and leads to negative result...

We present a linear regression method for predictions on a small data set making use of a second possibly biased data set that may be much larger. Our method fits linear regressions to the two data sets while penalizing the difference between predictions made by those two models. The resulting algorithm is a shrinkage method similar to those used in small area estimation. We find a Stein-type f...

In order to calculate confidence intervals and hypothesis tests, it is assumed that the errors are independent and normally distributed with mean zero and variance 2 σ . Given a sample of N observations on X and Y, the method of least squares estimates β0 and β1 as well as various other quantities that describe the precision of the estimates and the goodness-of-fit of the straight line to the d...