Moderate Deviation Theorem Research Articles

Cramér-type moderate deviation of normal approximation for unbounded exchangeable pairs

In Stein’s method, the exchangeable pair approach is commonly used to estimate the approximation errors in normal approximation. In this paper, we establish a Cramér-type moderate deviation theorem of normal approximation for unbounded exchangeable pairs. As applications, Cramér-type moderate deviation theorems for the sums of local statistics and general Curie–Weiss model are obtained.

Refined Cramér-type moderate deviation theorems for general self-normalized sums with applications to dependent random variables and winsorized mean

Let {(Xi,Yi)}i=1n be a sequence of independent bivariate random vectors. In this paper, we establish a refined Cramér-type moderate deviation theorem for the general self-normalized sum ∑ i=1nXi/(∑i=1nYi2)1/2, which unifies and extends the classical Cramér (Actual. Sci. Ind. 736 (1938) 5–23) theorem and the self-normalized Cramér-type moderate deviation theorems by Jing, Shao and Wang (Ann. Probab. 31 (2003) 2167–2215) as well as the further refined version by Wang (J. Theoret. Probab. 24 (2011) 307–329). The advantage of our result is evidenced through successful applications to weakly dependent random variables and self-normalized winsorized mean. Specifically, by applying our new framework on general self-normalized sum, we significantly improve Cramér-type moderate deviation theorems for one-dependent random variables, geometrically β-mixing random variables and causal processes under geometrical moment contraction. As an additional application, we also derive the Cramér-type moderate deviation theorems for self-normalized winsorized mean.

Cramér-type moderate deviation theorems for nonnormal approximation

A Cram\'er-type moderate deviation theorem quantifies the relative error of the tail probability approximation. It provides theoretical justification when the limiting tail probability can be used to estimate the tail probability under study. Chen Fang and Shao (2013) obtained a general Cram\'er-type moderate result using Stein's method when the limiting was a normal distribution. In this paper, Cram\'er-type moderate deviation theorems are established for nonnormal approximation under a general Stein identity, which is satisfied via the exchangeable pair approach and Stein's coupling. In particular, a Cram\'er-type moderate deviation theorem is obtained for the general Curie--Weiss model and the imitative monomer-dimer mean-field model.

High-dimensional consistent independence testing with maxima of rank correlations

Testing mutual independence for high-dimensional observations is a fundamental statistical challenge. Popular tests based on linear and simple rank correlations are known to be incapable of detecting nonlinear, nonmonotone relationships, calling for methods that can account for such dependences. To address this challenge, we propose a family of tests that are constructed using maxima of pairwise rank correlations that permit consistent assessment of pairwise independence. Built upon a newly developed Cramér-type moderate deviation theorem for degenerate U-statistics, our results cover a variety of rank correlations including Hoeffding’s $D$, Blum–Kiefer–Rosenblatt’s $R$ and Bergsma–Dassios–Yanagimoto’s $\tau^{*}$. The proposed tests are distribution-free in the class of multivariate distributions with continuous margins, implementable without the need for permutation, and are shown to be rate-optimal against sparse alternatives under the Gaussian copula model. As a by-product of the study, we reveal an identity between the aforementioned three rank correlation statistics, and hence make a step towards proving a conjecture of Bergsma and Dassios.

Cramér type moderate deviation theorems for self-normalized processes

Cram\'er type moderate deviation theorems quantify the accuracy of the relative error of the normal approximation and provide theoretical justifications for many commonly used methods in statistics. In this paper, we develop a new randomized concentration inequality and establish a Cram\'er type moderate deviation theorem for general self-normalized processes which include many well-known Studentized nonlinear statistics. In particular, a sharp moderate deviation theorem under optimal moment conditions is established for Studentized $U$-statistics.

Cramér-type moderate deviations for Studentized two-sample $U$-statistics with applications

Two-sample $U$-statistics are widely used in a broad range of applications, including those in the fields of biostatistics and econometrics. In this paper, we establish sharp Cramér-type moderate deviation theorems for Studentized two-sample $U$-statistics in a general framework, including the two-sample $t$-statistic and Studentized Mann–Whitney test statistic as prototypical examples. In particular, a refined moderate deviation theorem with second-order accuracy is established for the two-sample $t$-statistic. These results extend the applicability of the existing statistical methodologies from the one-sample $t$-statistic to more general nonlinear statistics. Applications to two-sample large-scale multiple testing problems with false discovery rate control and the regularized bootstrap method are also discussed.

A Cramér moderate deviation theorem for Hotelling’s $T^{2}$-statistic with applications to global tests

A Cramer moderate deviation theorem for Hotelling's $T^2$-statistic is proved under a finite $(3+\delta)$th moment. The result is applied to large scale tests on the equality of mean vectors and is shown that the number of tests can be as large as $e^{o(n^{1/3})}$ before the chi-squared distribution calibration becomes inaccurate. As an application of the moderate deviation results, a global test on the equality of m mean vectors based on the maximum of Hotelling's $T^2$-statistics is developed and its asymptotic null distribution is shown to be an extreme value type I distribution. A novel intermediate approximation to the null distribution is proposed to improve the slow convergence rate of the extreme distribution approximation. Numerical studies show that the new test procedure works well even for a small sample size and performs favorably in analyzing a breast cancer dataset.

Self-normalized limit theorems: A survey

Let $X_{1},X_{2},\ldots,$ be independent random variables with $EX_{i}=0$ and write $S_{n}=\sum_{i=1}^{n}X_{i}$ and $V_{n}^{2}=\sum_{i=1}^{n}X_{i}^{2}$. This paper provides an overview of current developments on the functional central limit theorems (invariance principles), absolute and relative errors in the central limit theorems, moderate and large deviation theorems and saddle-point approximations for the self-normalized sum $S_{n}/V_{n}$. Other self-normalized limit theorems are also briefly discussed.

Moderate Deviations for Stable Random Walks in Random Scenery

In this paper, a moderate deviation theorem for one-dimensional stable random walks in random scenery is proved. The proof relies on the analysis of maximum local times of stable random walks, and the comparison of moments between random walks in random scenery and self-intersection local times of the underlying random walks.

Moderate Deviations for Stable Random Walks in Random Scenery

In this paper, a moderate deviation theorem for one-dimensional stable random walks in random scenery is proved. The proof relies on the analysis of maximum local times of stable random walks, and the comparison of moments between random walks in random scenery and self-intersection local times of the underlying random walks.

Moderate deviations and law of the iterated logarithm in [formula omitted] for kernel density estimators

Let f n ( x ) be the non-parametric kernel density estimator of a density function f ( x ) based on a kernel function K . In this paper, we first prove two moderate deviation theorems in L 1 ( R d ) for { f n ( x ) , n ≥ 1 } . Then, as an application of the moderate deviations, we obtain a law of the iterated logarithm for { ‖ f n − E f n ‖ 1 , n ≥ 1 } .

The probability of large deviations for the sum functions of spacings

Let 0 = U0,n ≤ U1,n ≤ ⋯≤Un−1,n ≤ Un,n = 1 be an ordered sample from uniform [0, 1] distribution, and Din = Ui,n − Ui−1,n, i = 1, 2, …, n; n = 1, 2, …, be their spacings, and let f1n, …, fnn be a set of measurable functions. In this paper, the probabilities of the moderate and Cramer‐type large deviation theorems for statistics Rn(D) = f1n(nD1n) + ⋯+fnn(nDnn) are proved. Application of these theorems for determination of the intermediate efficiencies of the tests based on Rn(D)‐type statistic is presented here too.

Strong Moderate Deviation Theorems

Strong moderate deviation theorems are concerned with relative errors in the tails caused by replacing the exact distribution function by its limiting distribution function. A new approach for deriving such theorems is presented using strong approximation inequalities. In this way a strong moderate deviation theorem is obtained for statistics of the form $T(\alpha_n)$, where $T$ is a sublinear functional and $\alpha_n$ is the empirical process. The basic theorem is also applied on linear combinations of order statistics, leading to a substantial improvement of previous results.

On Moderate and Large Deviations in Multinomial Distributions

In this paper moderate and large deviation theorems are presented for the likelihood ratio statistic and Pearson's chi squared statistic in multinomial distributions. Let $k$ be the number of parameters and $n$ the number of observations. Moderate and large deviation theorems are available in the literature only if $k$ is kept fixed when $n \rightarrow \infty$. Although here attention is focussed on $k = k(n) \rightarrow \infty$ as $n \rightarrow \infty$, explicit inequalities are obtained for both $k$ and $n$ fixed. These inequalities imply results for the whole scope of moderate and large deviations both for fixed $k$ and for $k(n) \rightarrow \infty$ as $n \rightarrow \infty$. It turns out that the $\chi^2$ approximation continues to hold in some sense, even if $k \rightarrow \infty$. The results are applied in studying the influence of the choice of the number of classes on the power in goodness-of-fit tests, including a comparison of Pearson's chi squared test and the likelihood ratio test. Also the question of combining cells in a contingency table is discussed.

Cramer Type Large Deviations for Linear Combinations of Order Statistics

Let $T_n = n^{-1} \sum^n_{i=1} c_{in}X_{in}$ be a linear combination of order statistics and put $T^\ast_n = (T_n - E(T_n))/ \sqrt{\operatorname{Var}(T_n)}$. Sufficient conditions on the $c_{in}$ and on the moments of the underlying distribution are established under which the ratio $P(T^\ast_n > x)/(1 - \Phi(x))$ tends to 1, either uniformly in the range $-A \leq x \leq c \sqrt{\ln n}(A \geq 0, c > 0)$ (moderate deviation theorem) or uniformly in the range $-A \leq x \leq o(n^\alpha)(A \geq 0)$ (Cramer type large deviation theorem). The proof relies on Helmers' approximation method and on the corresponding results for U-statistics.