On lower bounds for errors of prior density estimators

Lower bounds for errors of prior density estimators are presented for a wide class of conditional densities of observations. Translated fromStatisticheskie Metody Otsenivaniya i Proverki Gipotez, pp. 77–88, Perm, 1993.     

Large deviations and moderate deviations for kernel density estimators of directional data

Let fn be the non-parametric kernel density estimator of directional data based on a kernel function K and a sequence of independent and identically distributed random variables taking values in d-dimensional unit sphere Sd-1. It is proved that if the kernel function is a function with bounded variation and the density function f of the random variables is continuous, then large deviation principle and moderate deviation principle for {sup x∈sd-1 ｜fn（x） - E（fn（x））｜, n ≥ 1} hold.     

关于大偏差概率的一个界

研究得到了关于随机和S(t)=∑N(t)i=1Xi,t≥0大偏差的幂的一个界,其中(N(t))t≥0是一族非负整值随机变量,(Xn)n∈N是独立同分布的随机变量,其共同的分布函数是F与(N(t))t≥0独立.本结论是在假设分布函数F的右尾属于ERV族的情况下得到的.     

On moderate deviations

Summary In this paper we study necessary and sufficient conditions for the validity of limit theorems on moderate deviations. Moreover we analyse the remainder term appearing in the asymptotic relations.     

On the lower bounds for mean square error of empirical bayes estimators

The usual empirical Bayes setting is considered with θ being a shift or a scale parameter. A class of empirical Bayes estimators of a function b(θ) is proposed. The properties of the estimates are studied and mean square errors are calculated. The lower bounds are constructed for mean square errors of the empirical Bayes estimators over the class of all empirical Bayes estimators of b(θ). The results are applied to the case b(θ)=θ. The examples of the upper and lower bounds for mean square error are presented for the most popular families of conditional distributions. Added to the English translaion.     

Deviation inequalities and moderate deviations for estimators of parameters in TAR models

In this paper, we establish some deviation inequalities and the moderate deviation principles for the least squares estimators of the parameters in the threshold autoregressive model under the assumption that the noise random variable satisfies a logarithmic Sobolev inequality.     

Large and moderate deviations principles for kernel estimators of the multivariate regression

In this paper, we prove large deviations principle for the Nadaraya-Watson estimator and for the semi-recursive kernel estimator of the regression in the multidimensional case. Under suitable conditions, we show that the rate function is a good rate function. We thus generalize the results already obtained in the one-dimensional case for the Nadaraya-Watson estimator. Moreover, we give a moderate deviations principle for these two estimators. It turns out that the rate function obtained in the moderate deviations principle for the semi-recursive estimator is larger than the one obtained for the Nadaraya-Watson estimator.      

Deviation inequalities for quadratic Wiener functionals and moderate deviations for parameter estimators

We study deviation inequalities for some quadratic Wiener functionals and moderate deviations for parameter estimators in a linear stochastic differential equation model. Firstly, we give some estimates for Laplace integrals of the quadratic Wiener functionals by calculating the eigenvalues of the associated Hilbert-Schmidt operators. Then applying the estimates, we establish deviation inequalities for the quadratic functionals and moderate deviation principles for the parameter estimators.     

Shannon–McMillan theorems for discrete random fields along curves and lower bounds for surface-order large deviations

The notion of a surface-order specific entropy h _c (P) of a two-dimensional discrete random field P along a curve c is introduced as the limit of rescaled entropies along lattice approximations of the blowups of c. Existence is shown by proving a corresponding Shannon–McMillan theorem. We obtain a representation of h _c (P) as a mixture of specific entropies along the tangent lines of c. As an application, the specific entropy along curves is used to refine Föllmer and Ort’s lower bound for the large deviations of the empirical field of an attractive Gibbs measure from its ergodic behaviour in the phase-transition regime.     

Probabilities of large and moderate deviations for maximum likelihood estimates

One proves theorems on the probabilities of large and moderate deviations for maximum likelihood estimates while for less restrictive assumptions one establishes coarse asymptotics for probabilities of large deviations in the regular case. Similar results are obtained in the case of observations in white Gaussian noise.Translated from Zapiski Nauchnykh Seminarov Leningradskogo Otdeleniya Matematicheskogo Instituta im. V. A. Steklova AN SSSR, Vol. 108, pp. 154–169, 1981.The author expresses his deep gratitude to Io Ao Ibragimov fort he formulation of the problem, for his constant care and for useful advice.     

On asymptotic behavior of probabilities of large and moderate deviations for some iterated stochastic processes

We derive logarithmic asymptotics for probabilities of large deviations for some iterated processes. We show that under appropriate conditions, these asymptotics are the same as those for sums of independent random variables. When these conditions do not hold, the asymptotics of large deviations for iterated processes are quite different. When the iterated process is a homogeneous process with independent increments in which time is replaced by random one, the behavior of large and moderate deviations is studied in the case of finite variance. For this case, the following one-sided moment restriction are considered: the Cramèr condition, the Linnik condition, and the existence of moment of order p > 2 for a positive part. Bibliography: 6 titles.     

On large deviations and density functions

Suppose thatX ₁,X ₂, ... is a sequence of absolutely continuous or integer valued random variables with corresponding probability density functionsf _n (x). Let {φ _n } _n=1 ^∞ be a sequence of real numbers, then necessary and sufficient conditions are given forn ⁻¹ logf _n (φ _n )-n ⁻¹ log P (X _n >φ _n )=0(1) asn→∞.     

Some generalizations of wentzell's lower estimates on large deviations

Wentzell Obtained a Large Deviation Principle for Markov Processes with Lévy Generatiors. In this Paper the Lower Estimates On Large Deviations are Obtained Under Weaker Assumptions than Wentzell's.     

On lower and upper bounds of matrices

Using an approach of Bergh, we give an alternate proof of Bennett's result on lower bounds for non-negative matrices acting on non-increasing non-negative sequences in lp when p?1 and its dual version, the upper bounds when 0<p?1. We also determine such bounds explicitly for some families of matrices.     

Probabilities of moderate deviations

Let X₁, X₂, ... be a sequence of independent, identically distributed random variables, and let. The rate of convergence of probabilities of the form andis studied for any > ₀ and some r and ₀. Moreover, necessary and sufficient conditions are given that the relations be satisfied uniformly with respect to x in the region 0 x clog n, where and c are some positive constants, and. Local limit theorems are also presented.Translated from Zapiski Nauchnykh Seminarov Leningradskogo Otdeleniya Matematicheskogo Instituts im. V. A. Steklova AN SSSR, Vol. 85, pp. 6–16, 1979.     

Sample path large and moderate deviations for risk model with delayed claims

Sample path large and moderate deviation principles for Markov modulated risk models with delayed claims are proved by the exponential martingale method. As applications, asymptotic estimates and exponential bounds of the ruin probability are also studied.