Titlebook: Combinatorial Methods in Density Estimation; Luc Devroye,Gábor Lugosi Book 2001 Springer-Verlag New York, Inc. 2001 Density Estimation.Lik

Vulnerable

CT-angiography

消散

Springer Series in Statisticshttp://image.papertrans.cn/c/image/229941.jpg

合唱团

Oscar Herreras,George G. SomjenThis chapter is devoted to some basic inequalities that bound the maximal difference between probabilities and relative frequencies over a class of events. The bounds will be key tools in our study of density estimates. Let ..,…,.. be i.i.d. random variables taking values in .. with common distribution

彻底明白

https://doi.org/10.1007/978-1-4899-1597-9Consider a class . of subsets of .., and let ..,…,.. ∈ .. be arbitrary points. Recall from the previous chapter that properties of the finite set .(..) ⊂ {0, 1}. defined by . play an essential role in bounding uniform deviations of the empirical measure.

落叶剂

https://doi.org/10.1007/3-7643-7537-XThis chapter is about the choice of the bandwidth (or smoothing factor) . ∈ (0, ∞) of the standard kernel estimate

MIRTH

壮丽的去

https://doi.org/10.1007/3-7643-7537-XThe transformed kernel estimate on the real line was introduced in an attempt to reduce the .. error in a relatively cheap manner. The data are first transformed . : . → . by a strictly monotonically increasing almost everywhere differentiable transformation .: .. = .(..),…,.. = .(..). The density of .. is . where .. denotes the inverse of ..

hypnotic

engrave

Uniform Deviation Inequalities,This chapter is devoted to some basic inequalities that bound the maximal difference between probabilities and relative frequencies over a class of events. The bounds will be key tools in our study of density estimates. Let ..,…,.. be i.i.d. random variables taking values in .. with common distribution