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The AdaBoost algorithm was designed to combine many "weak" hypotheses that perform slightly better than random guessing into a "strong" hypothesis that has very low error.
This paper deals with empirical processes of the type Cn(B) =pn{μn(B)− P(Xn+1 2 B |X1, . . . ,Xn)}, where (Xn) is a sequence of random variables and μn = (1/n)Pn i=1 Xi the empirical measure...
We give tbe rate of convergence in the central limit theorem and the random central limit theorem for functions belonging to the class I of all real differentiable functions g such that gr E L(1).
On the rate of convergence to Brownian motion of the partial sums of infima of independent random variables。
In this paper we present uniform and nonuniform rates of convergence d sums of independent random variables to a stable law. The results obtained extend to the case of nonidentically distributed ra...
This paper focuses on pconvolutions, a class of generalized convolutions of random vectors. It establishes the rate of convergence in uniform (Kolmogorov) metric for normalized n-fold p-convolution ...
The rate of convergence of penalized maximum likelihood estimation will be developd based on HeIlinger entropy with bracketing which measures the size of underlying spaces.
Let XI, X,, . .. be i.i.d. random variables with a commondf. P.LetS,=X,+ ...+ X,,n>1,andMm=max,s,Xk,n21. In this paper for a large class of subexponential distributions we estimate the rate of conv...
We find upper bounds for the probability of underestimation and overestimation errors in penalized likelihood context tree estimation. The bounds are explicit and applies to processes of not necessa...
We consider estimation procedures which are recursive in the sense that each successive estimator is obtained from the previous one by a simple adjustment. We study rate of convergence of recursive ...

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