On Approximating String Selection Problems with Outliers

Robust high-dimensional semiparametric regression using optimized differencing method applied to the vitamin B2 production data

Background and purpose: By evolving science, knowledge, and technology, we deal with high-dimensional data in which the number of predictors may considerably exceed the sample size. The main problems with high-dimensional data are the estimation of the coefficients and interpretation. For high-dimension problems, classical methods are not reliable because of a large number of predictor variable...

Approximating Initial-Value Problems with Two-Point Boundary-Value Problems: BBM-Equation

Outlier Detection for DNA Fragment Assembly

A major impediment in the development of efficient full genome sequencing is the large portion of erroneous reads produced by sequencing platforms. Error correction is the computational process that attempts to identify and correct these mistakes. Several classical stringology problems, including the Consensus String problem, are used to model error correction. However, a significant shortcomin...

Identification of outliers types in multivariate time series using genetic algorithm

Multivariate time series data, often, modeled using vector autoregressive moving average (VARMA) model. But presence of outliers can violates the stationary assumption and may lead to wrong modeling, biased estimation of parameters and inaccurate prediction. Thus, detection of these points and how to deal properly with them, especially in relation to modeling and parameter estimation of VARMA m...

Outlier Detection by Boosting Regression Trees

A procedure for detecting outliers in regression problems is proposed. It is based on information provided by boosting regression trees. The key idea is to select the most frequently resampled observation along the boosting iterations and reiterate after removing it. The selection criterion is based on Tchebychev’s inequality applied to the maximum over the boosting iterations of ...