OBJECTIVES Comparison of classification methods using data of one clinical study. The tuning of hyperparameters is assessed as part of the methods by nested-loop cross-validation. METHODS We assess the ability of 18 statistical and machine learning classifiers to detect glaucoma. The training data set is one case-control study consisting of confocal scanning laser ophthalmoscopy measurement v...

To explore the feasibility of parsimony analysis for large data sets, we conducted heuristic parsimony searches and bootstrap analyses on separate and combined DNA data sets for 190 angiosperms and three outgroups. Separate data sets of 18S rDNA (1,855 bp), rbcL (1,428 bp), and atpB (1,450 bp) sequences were combined into a single matrix 4,733 bp in length. Analyses of the combined data set sho...

In environmental and agricultural studies, it is often of interest to compare spatial variables across different regions. Traditional statistical tools that assume independent samples are inadequate, because of potential spatial correlations. In this paper, such spatial dependence is accounted for by a random field model, and a non-parametric test is developed to compare the overall distributio...

In this paper we consider the problem of bootstrapping a class of spatial regression models when the sampling sites are generated by a (possibly nonuniform) stochastic design and are irregularly spaced. It is shown that the natural extension of the existing block bootstrap methods for grid spatial data does not work for irregularly spaced spatial data under nonuniform stochastic designs. A vari...

Inference for random processes depends on the estimation of the long-run variance of a statistic. Commonly used approaches in time series analysis for estimating the long-run variance include bootstrap approaches and the class of heteroskedasticity and autocorrelation consistent covariance estimators. These approaches have been generalized to deal with point processes and continuous spatial pro...

In this paper we construct a test for the difference parameter d in the fractionally integrated autoregressive moving-average (ARFIMA) model. Obtaining estimates by smoothed spectral regression estimation method, we use the moving blocks bootstrap method to construct the test for d. The results of Monte Carlo studies show that this test is generally valid for certain block sizes, and for these ...

The log periodogram regression is widely used in empirical applications because of its simplicity, since only a least squares regression is required to estimate the memory parameter, d, its good asymptotic properties and its robustness to misspecification of the short term behavior of the series. However, the asymptotic distribution is a poor approximation of the (unknown) finite sample distrib...