Bivariate kernel density estimatesT.M. Davies
Bivariate kernel density estimates of seasonal thermal-bathymetric niches occupied by Atlantic cod ecotypes around Iceland.Timothy B. GrabowskiVilhjálmur ThorsteinssonBruce J. McAdamGuđrún Marteinsdóttir
ksdensityuses the reflection method. This method augments bounded data by adding reflected data near the boundaries, and estimates the pdf. Then,ksdensityreturns the estimated pdf corresponding to the original support with proper normalization, so that the estimated pdf's integral over the original ...
The sinc kernel has this feature, but other kernels are often adopted because they produce smoother estimates of the target density. An alternative to the sinc kernel is the de la Vall´ee Poussin kernel (VP kernel), defined in Table 1. This kernel is actually a probability density (i.e...
Two-sample test statistics for measuring discrepancies between two multivariate probability density functions using kernel-based density estimates. J Multivar Anal. 1994;50(1):41–54. doi: 10.1006/jmva.1994.1033 (Open in a new window)Web of Science ®(Open in a new window)Google Scholar ...
After the data are transformed to the unit square, the bivariate density is estimated using linear splines and their tensor products. The combined procedure yields an estimate of the bivariate density on the original scale, which may provide insight about the dependence structure. The procedure can...
eye. To obtain themaximum likelihood estimates(MLEs) of parameters, we develop a series ofminorization–maximization(MM) algorithms by constructing four surrogate functions with closed–form expressions at each iteration of the MM algorithms. Simulation studies are conducted, and two real datasets are...
assaying the statistical “confidence” in the results of either or both of the above activities, via tests of statistical significance, placing confidence intervals on parameter estimates, or other ways.In some cases of interest, however, we have no prior basis for distinguishing between criterion ...
The kernel type density estimates for the marginal densities are plotted in Fig. 9 (black dashed line, right panel). A nondecreasing trend can be observed in this case. With 20% and 50% censoring, the value of the test U0c and the confidence interval for its expectation are 0.180, (...
Fernández-Durán and Gregorio-Domínguez [12] developed an efficient algorithm based on optimization on manifolds to obtain the maximum likelihood estimates of the c parameters. This algorithm is included with other routines for the analysis of circular data based on NNTS models in the free R [42...