Statistical habitat models give quantitative descriptions of species-habitat relationships by analysing records of environmental conditions and species incidence by means of logistic regression or other techniques. The most useful model in a given situation is usually unknown a priori. Several data-driven, computer-intensive methods exist to automatically fit a large number of candidate models ...

Cluster analysis requires fixing the number of clusters and often many hyper-parameters. In practice, one produces several partitions, a final is chosen based on validation or selection criteria. There exist an abundance methods that, implicitly explicitly, assume certain clustering notion. this paper, we focus groups that can be well separated by quadratic linear boundaries. The reference clus...

The statistical properties of sample estimation and bootstrap estimation of phylogenetic variability from a sample of nucleotide sequences are studied by using model trees of three taxa with an outgroup and by assuming a constant rate of nucleotide substitution. The maximum-parsimony method of tree reconstruction is used. An analytic formula is derived for estimating the sequence length that is...

In this note we consider several versions of the bootstrap and argue that it is helpful in explaining and thinking about such procedures to use an explicit representation of the random resampling process. To illustrate the point we give such explicit representations and use them to produce some results about bootstrapping linear models that are, apparently, not widely known. Among these are a d...

Bootstrap resampling is starting to be frequently applied to contingency tables analysis of Genome-Wide SNP data, to cope with the bias in genetic effect estimates, the large number of false positive associations and the instability of the lists of SNPs associated with a disease. The bootstrap procedure, however, increases the computational complexity by a factor B, where B is the number of boo...

We propose a new resampling procedure, the dependent wild bootstrap, for stationary time series. As a natural extension of the traditional wild bootstrap to time series setting, the dependent wild bootstrap offers a viable alternative to the existing block-based bootstrap methods, whose properties have been extensively studied over the last two decades. Unlike all of the block-based bootstrap m...

Artificially generated functional magnetic resonance imaging (fMRI) data drawn from a block-based visual stimulation paradigm were analyzed by the stochastic neuromorphic extended BS Infomax algorithm [1] implementing spatial independent component analysis (ICA) [2]. Variance estimate based on bootstrap resampling [3] was employed as model selection criterion and reliability assessment of ICA d...

This paper proposes a statistical methodology for comparing the performance of evolutionary computation algorithms. A two-fold sampling scheme for collecting performance data is introduced, and this data is assessed using a multiple hypothesis testing framework relying on a bootstrap resampling procedure. The proposed method offers a convenient, flexible, and reliable approach to comparing algo...

Prior to statistical analyses of daily milk yield data, outliers due to equipment malfunction or confirmed milk recording errors should be removed. However, those outliers that are caused by health status, body condition, stress, energy balance, or BST would be valid data. Hence, using the central limit theorem (CLT) to establish confidence intervals (CI) for yield measures could be misleading....

Principal Component Analysis (PCA) is a useful technique for reducing the dimensionality of datasets for compression or recognition purposes. Many different methods have been proposed for performing PCA. This study aims to compare these methods by analysing the solutions which these methods find. We have estimated the correlation between these solutions and produced the errors using bootstrap r...