Asymptotic properties of robust regression estimators are well known. However, it is not always clear what the best strategy for confidence intervals and hypothesis testing when sample size very large, since distribution residuals coming from estimates has unknown small samples. In present work we propose an analysis various strategies estimating variance-covariance matrix S at variation n p, c...

Pharmacophore modelling-based virtual screening of compound is a ligand-based approach and is useful when the 3D structure of target is not available but a few known active compounds are known. Pharmacophore mapping studies were undertaken for a set of 50 N(3)-phenylpyrazinones possessing Corticotropin-releasing Factor 1 (CRF 1) antagonistic activity. Six point pharmacophores with two hydrogen ...

Unsupervised learning is one type of machine with an exceptionally high number applications in various fields. The most popular and best-known group unsupervised methods clustering methods. main goal to find hidden relationships between individual observations. There great interest different density estimation methods, especially when there are outliers the data. Density also can be applied dat...

We consider the problem of detecting (testing) Gaussian stochastic sequences (signals) with imprecisely known means and covariance matrices. An alternative is independent identically distributed zero-mean random variables unit variances. For a given false alarm (1st-kind error) probability, quality minimax detection by best miss probability (2nd-kind error probability) exponent over growing obs...

We study a simple learning algorithm for binary classification. Instead of predicting with the best hypothesis in the hypothesis class, this algorithm predicts with a weighted average of all hypotheses, weighted exponentially with respect to their training error. We show that the prediction of this algorithm is much more stable than the prediction of an algorithm that predicts with the best hyp...

Let πS(t) denote the argument of the Riemann zeta-function at the point 1/2 + it. Assuming the Riemann hypothesis, we sharpen the constant in the best currently known bounds for S(t) and for the change of S(t) in intervals. We then deduce estimates for the largest multiplicity of a zero of the zeta-function, and for the largest gap between the zeros.