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Convergence model of interest rates of CKLS type

Zuzana Zíková, Beáta Stehlíková (2012)

Kybernetika

This paper deals with convergence model of interest rates, which explains the evolution of interest rate in connection with the adoption of Euro currency. Its dynamics is described by two stochastic differential equations – the domestic and the European short rate. Bond prices are then solutions to partial differential equations. For the special case with constant volatilities closed form solutions for bond prices are known. Substituting its constant volatilities by instantaneous volatilities we...

Convergence of the tail probability for weighted sums of negatively orthant dependent random variables

Haiwu Huang, Linyan Li, Xuewen Lu (2020)

Kybernetika

In this research, strong convergence properties of the tail probability for weighted sums of negatively orthant dependent random variables are discussed. Some sharp theorems for weighted sums of arrays of rowwise negatively orthant dependent random variables are established. These results not only extend the corresponding ones of Cai [4], Wang et al. [19] and Shen [13], but also improve them, respectively.

Convergence rates of orthogonal series regression estimators

Waldemar Popiński (2000)

Applicationes Mathematicae

General conditions for convergence rates of nonparametric orthogonal series estimators of the regression function f(x)=E(Y | X = x) are considered. The estimators are obtained by the least squares method on the basis of a random observation sample (Yi,Xi), i=1,...,n, where X i A d have marginal distribution with density ϱ L 1 ( A ) and Var( Y | X = x) is bounded on A. Convergence rates of the errors E X ( f ( X ) - f ^ N ( X ) ) 2 and f - f ^ N for the estimator f ^ N ( x ) = k = 1 N c ^ k e k ( x ) , constructed using an orthonormal system e k , k=1,2,..., in L 2 ( A ) are obtained.

Convergencia del vector de probabilidad a posteriori bajo una distribución predictiva.

Julián de la Horra (1986)

Trabajos de Estadística

La convergencia casi segura de una sucesión de variables aleatorias, con respecto a PX,Q (distribución predictiva), se estudia en relación con la convergencia casi segura, con respecto a PX,θ (para todo θ ∈ Θ), donde {PX,θ}θ ∈ Θ es una familia de modelos de probabilidad sobre el espacio muestral χ.Como consecuencia, se estudia la convergencia casi segura del vector de probabilidad a posteriori con respecto a PX,Q.

Convexidad y simetría de la J-divergencia generalizada.

Miquel Salicrú Pagés (1990)

Trabajos de Estadística

En este trabajo se caracteriza la simetría de la J-divergencia generalizada en términos del parámetro y de la función que la determina. Se plantea seguidamente la convexidad y la simetrización en función del parámetro, atendiendo a la forma de la función φ(t) que la determina. Finalmente, se revisa la convexidad en función de las variables atendiendo a la concavidad y convexidad de las funciones φ(t) y 1/φ''(t).

Convexity inequalities for estimating generalized conditional entropies from below

Alexey E. Rastegin (2012)

Kybernetika

Generalized entropic functionals are in an active area of research. Hence lower and upper bounds on these functionals are of interest. Lower bounds for estimating Rényi conditional α -entropy and two kinds of non-extensive conditional α -entropy are obtained. These bounds are expressed in terms of error probability of the standard decision and extend the inequalities known for the regular conditional entropy. The presented inequalities are mainly based on the convexity of some functions. In a certain...

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