Linear mixed-effects models are commonly used to analyze clustered data structures. There numerous packages fit these in R and conduct likelihood-based inference. The implementation of resampling-based procedures for inference more limited. In this paper, we introduce the lmeresampler package bootstrapping nested linear via lme4 or nlme. Bootstrap estimation allows bias correction, adjusted sta...

For independent data, non-parametric bootstrap is realised by resampling the data with replacement. This approach fails for dependent data such as time series. If the data generating process is at least stationary and mixing, the blockwise bootstrap by drawing subsamples or blocks of the data saves the concept. For the blockwise bootstrap a blocklength has to be selected. We propose a method fo...

This paper contains a comparison of in-sample and out-of-sample performances between the resampled efficiency technique, patented by Richard Michaud and Robert Michaud (1999), and traditional Mean-Variance portfolio selection, presented by Harry Markowitz (1952). Based on the Monte Carlo simulation, data (samples) generation process determines the algorithms by using both, parametric and nonpar...

Various bootstrap methodologies are discussed for the selection of the bandwidth of a kernel density estimator. The smoothed bootstrap is seen to provide new and independent motivation of some previously proposed methods. A curious feature of bootstrapping in this context is that no simulated resampling is required, since the needed functionals of the distribution can be calculated explicitly.

Evolutionary trees sit at the core of all realistic models describing a set of related sequences, including alignment, homology search, ancestral protein reconstruction and 2D/3D structural change. It is important to assess the stochastic error when estimating a tree, including models using the most realistic likelihood-based optimizations, yet computation times may be many days or weeks. If so...

An accuracy measure (mean squared error, MSE) is necessary when small area estimators of linear parameters are provided. Even in the case when such estimators arise from the assumption of relatively simple models for the variable of interest, as linear mixed models, the analytic form of the MSE is not suitable to be calculated explicitly. Some good and widely used approximations are available f...