Complete convergence for weighted sums of i.i.d. random variables with applications in regression estimation and EV model

In this paper, we obtain complete convergence results for Stout type weighted sums of i.i.d. random variables. A strong law for weighted sums of i.i.d. random variables is also obtained. As the applications of the strong law, the strong consistency and rate of the nonparametric regression estimations and the rates of the strong consistency of LS estimators for the unknown parameters of the simple linear errors in variables (EV) model are given.     

Limit theorems for identically distributed martingale difference

Abstract

In this paper, we establish some general results for the strong law of large numbers and the complete convergence of martingale difference which include the well-known Marcinkiewicz–Zygmund strong law and Spitzer complete convergence.     

Asymptotics for multisample statistics

Consider a family of square-integrable R^d-valued statistics S_k = S_k(X_1,k1; X_2,k2;…; X_m,km), where the independent samples X_i,kj respectively have k_i i.i.d. components valued in some separable metric space X_i. We prove a strong law of large numbers, a central limit theorem and a law of the iterated logarithm for the sequence {S_k}, including both the situations where the sample sizes tend to infinity while m is fixed and those where the sample sizes remain small while m tends to infinity. We also obtain two almost sure convergence results in both these contexts, under the additional assumption that S_k is symmetric in the coordinates of each sample X_i,kj. Some extensions to row-exchangeable and conditionally independent observations are provided. Applications to an estimator of the dimension of a data set and to the Henze-Schilling test statistic for equality of two densities are also presented.     

A note on mixingale limit theorems and stable convergence in law

A gap in the proof of a non stationary mixingale invariance principle is identified and fixed by introducing a skipped subsampling of a partial sum process and letting the skipped interval vanish asymptotically at an appropriate rate as the sample size increases. The corrected proof produces a mixingale limit theorem in the form of a mixing convergence in law, occurring jointly with the stable convergence in law for the same σ-field relative to which they are stable and mixing. The applicability of established results to a high-frequency estimation of the quadratic variation of financial price process is discussed.     

Equivalent conditions of the complete convergence for weighted sums of NSD random variables

Abstract

In this paper, we consider the complete convergence for weighted sums of negatively superadditive-dependent (NSD) random variables without assumptions of identical distribution. Some sufficient and necessary conditions to prove the complete convergence for weighted sums of NSD random variables are presented, which extend and improve the corresponding ones of Naderi et al. As an application of the main results, the Marcinkiewicz–Zygmund type strong law of large numbers for weighted sums of NSD random variables is also achieved.     

Estimation of single index model with missing response at random

Under missing at random, we estimate the unknown link function and the direction parameter in a single index model. The central limit theory and the convergence rate of the law of the iterated logarithm for the estimator of the direction parameter are derived, the optimal convergence rate for the estimator of the link function is obtained and simulation results support the theoretical results of the paper.     

Pointwise and uniform convergence of multivariate kernel density estimators using random bandwidths

We obtain the rates of pointwise and uniform convergence of multivariate kernel density estimators using a random bandwidth vector obtained by some data-based algorithm. We are able to obtain faster rate for pointwise convergence. The uniform convergence rate is obtained under some moment condition on the marginal distribution. The rates are obtained under i.i.d. and strongly mixing type dependence assumptions.     

Complete and complete moment convergence with applications to the EV regression models

In this paper, the complete convergence and complete moment convergence for arrays of rowwise m-extended negatively dependent (m-END, for short) random variables are established. As an application, the Marcinkiewicz-Zygmund type strong law of large numbers for m-END random variables is also achieved. By using the results that we established, we further investigate the strong consistency of the least square estimator in the simple linear errors-in-variables models, and provide some simulations to verify the validity of our theoretical results.     

On the mean square convergence of the convolution representation of linear filters

Mean square convergence is the most frequently considered mode of convergence for the infinite series convolution expressions representing filter outputs in stationary time series analysis. There is confusion, however, also in the literature, about which conditions guarantee that this convergence holds. If only general properties of the input series to the filter are known, it is appropriate to consider the class of series with these properties. For each of several classes of full rank, wide sense stationary, zero-mean, vector time series, a weakest possible condition on the frequency response function of a linear filter is given which guarantees that the time-domain convolution representation of the filter converges to the filter output in mean square, whenever the input series belongs to the class under consideration. The classes considered are (i) the purely nondeterministic series with essentially bounded spectral density matrix, (ii) all purely nondeterministic series, (iii) all series. We then show that more unified resttlts can be obtained if Cesiro sums are utilized to define the convergence of the convolution representation. The mean square convergence of infinite autoregressions is also discussed.     

Asymptotic results for hybrids of empirical and partial sums processes

The present paper is devoted to the study of the hybrids of empirical and partial sums processes. In the first part, we present a synthesis of results related to these processes and their connection with the empirical and compound process. We obtain new results on the precise asymptotics in the law of the logarithm related to complete convergence and a.s. convergence, under some mild conditions, for the hybrids of empirical and partial sums processes. Finally, the weighted bootstrap processes and general hybrid processes are also discussed.     

Asymptotic normality of MRPP statistics from invariance principles of u-statistics

Multi-response permutation procedures (MRPP) were recently introduced to test differences between a priori classified groups of objects ( Mielke, Berry Johnson, 1976; Mielke, 1979 ). The null distributions of the MRPP statistics were initially conjectured to be asymptotically normal for some specified conditions within the setting of a sequence of finite populations due to Madow ( 1948 ).

Asymptotic normality of a class of MRPP statistics (under the null hypothesis) is shown in two cases: (i) the setting which considers the populations to be the samples resulting from sequential independent identically distributed (i.i.d.) sampling (sampling from infinite populations) and (ii) the setting of a sequence of increasingly large finite populations (sampling from finite populations). The results are direct applications of the weak convergence of a U-statistic process in the i.i.d. case to a Brownian motion (Bhattacharyya and Sen, 1977) and of the weak convergence of a U-statistic process in the finite populations case to a Brownian bridge (Sen, 1972). The conditions are milder for the i.i.d. case than for the finite populations case. However, neither case provides a restriction of a practical consequence in applications of MRPP. In either case, convergence is shown to depend on the asymptotic ratios of the group sizes to the population size.     

Limit theorems for dependent Bernoulli variables with statistical inference

We consider a class of dependent Bernoulli variables where the conditional success probability is a linear combination of the last few trials and the original success probability. We obtain its limit theorems including the strong law of large numbers, weak invariance principle, and law of the iterated logarithm. We also derive some statistical inference results which make the model applicable. Simulation results are exhibited as well to show that with small sample size the convergence rate is satisfying and the proposed estimators behave well.     

Distribution of sample mean when observations are correlated

It i s well known that even if the sample observations are correlated and not normal, the sample mean is normal in 1arge samples. But how large is large? This question i s investigated in this paper. In particular , the relation between the rate of convergence and the correlation property of the observations i s explored. It i s observed that the correlation, in general, retards the rate of convergence.     

Uniform Ergodicity of the Particle Gibbs Sampler

The particle Gibbs sampler is a systematic way of using a particle filter within Markov chain Monte Carlo. This results in an off‐the‐shelf Markov kernel on the space of state trajectories, which can be used to simulate from the full joint smoothing distribution for a state space model in a Markov chain Monte Carlo scheme. We show that the particle Gibbs Markov kernel is uniformly ergodic under rather general assumptions, which we will carefully review and discuss. In particular, we provide an explicit rate of convergence, which reveals that (i) for fixed number of data points, the convergence rate can be made arbitrarily good by increasing the number of particles and (ii) under general mixing assumptions, the convergence rate can be kept constant by increasing the number of particles superlinearly with the number of observations. We illustrate the applicability of our result by studying in detail a common stochastic volatility model with a non‐compact state space.     

我国区域经济增长收敛的计量分析——基于面板数据分析

本文综合运用回归分析和时间序列分析法,从不同角度较全面地考察了我国区域经济增长收敛问题。计量结果表明:改革开放以来,我国各省区的经济增长不存在收敛趋势,但东、中、西部三大经济带内基本上可以认为存在具有不同稳定性的"收敛俱乐部"现象,即东部经济带存在稳定的收敛趋势,任何外部冲击都应该是暂时的,而中、西部经济带却经常会由于外部冲击而随时可能呈现发散。     

Interval censored and truncated data: Rate of convergence of NPMLE of the density

We consider survival data that are both interval censored and truncated. Under appropriate assumptions on the involved distributions, the censoring, truncation and survival, we prove the consistency of the NPMLE of the density of the survival, and give the rate of convergence. Finally, we give an example where the joint law of the censoring and truncation can be explicitly computed.     

Precise asymptotics on spectral statistics of random matrices

This paper focuses on the limiting properties of the spectral statistics of Wigner matrices and sample covariance matrices. Following the ideas of Gut and Spaˇtaru (2000a, b), Gut and Steinebach (2013) and Chow (1988) on precise asymptotics of i.i.d. random variables in the context of complete convergence and moment convergence, we will establish the corresponding results on the spectral statistics of random matrices.     

Strong convergence properties for partial sums of asymptotically negatively associated random vectors in Hilbert spaces

Abstract

In this paper, we investigate the almost sure convergence for partial sums of asymptotically negatively associated (ANA, for short) random vectors in Hilbert spaces. The Khintchine-Kolmogorov type convergence theorem, three series theorem and the Kolmogorov type strong law of large numbers for partial sums of ANA random vectors in Hilbert spaces are obtained. The results obtained in the paper generalize some corresponding ones for independent random vectors and negatively associated random vectors in Hilbert spaces.