Journal of Multivariate Analysis最新文献

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SFQRA: Scaled factor-augmented quantile regression with aggregation in conditional mean forecasting

IF 1.4 3区数学 Q2 STATISTICS & PROBABILITY

Journal of Multivariate Analysis

Pub Date : 2025-01-03 DOI: 10.1016/j.jmva.2024.105405

Lei Shu , Yifan Hao , Yu Chen , Qing Yang

Achieving robust forecasts for a single time series with many covariates and possible nonlinear effects is a problem worth investigating. In this paper, a scaled factor-augmented quantile regression with aggregation (SFQRA) method is proposed for an effective prediction. It first estimates different conditional quantiles by introducing scaled covariates to the factor-augmented quantile regression, which not only combats the curse of dimensionality but also includes the target information in the estimation. Then the different conditional quantiles are aggregated appropriately to a robust forecast. Moreover, combining SFQRA with feature screening via an aggregated quantile correlation allows it to be extended to handle cases when only a portion of covariates is informative. The effectiveness of the proposed methods is justified theoretically, under the framework of large cross-sections and large time dimensions while no restriction is imposed on the relation between them. Various simulation studies and real data analyses demonstrate the superiority of the newly proposed method in forecasting.

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引用次数: 0

Semi-functional varying coefficient mode-based regression

IF 1.4 3区数学 Q2 STATISTICS & PROBABILITY

Journal of Multivariate Analysis

Pub Date : 2025-01-01 DOI: 10.1016/j.jmva.2024.105402

Tao Wang

We propose estimating semi-functional varying coefficient regression based on the mode value through a kernel objective function, where the bandwidth included is treated as a tuning parameter to achieve efficiency and robustness. For estimation, functional principal component basis functions are utilized to approximate the slope function and functional predictor variable, while B-spline functions are employed to approximate the varying coefficient component. Under mild regularity conditions, the convergence rates of the resulting estimators for the unknown slope function and varying coefficient are established under various cases. To numerically estimate the proposed model, we recommend employing a computationally efficient mode expectation–maximization algorithm with the aid of a Gaussian kernel. The tuning parameters are selected using the mode-based Bayesian information criterion and cross-validation procedures. Built upon the generalized likelihood technique, we further develop a goodness-of-fit test to assess the constancy of varying coefficient functions and put forward a wild bootstrap procedure for estimating the corresponding critical values. The finite sample performance of the developed estimators is illustrated through Monte Carlo simulations and real data analysis related to the Tecator data. The results produced by the propounded method are compared favorably with those obtained from alternative estimation techniques.

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引用次数: 0

Sequential estimation of high-dimensional signal plus noise models under general elliptical frameworks

IF 1.4 3区数学 Q2 STATISTICS & PROBABILITY

Journal of Multivariate Analysis

Pub Date : 2024-12-30 DOI: 10.1016/j.jmva.2024.105403

Li Yanpeng , Xie Jiahui , Zhou Guoliang , Zhou Wang

High dimensional data analysis has attracted considerable interest and is facing new challenges, one of which is the increasingly available data with noise corrupted and in a streaming manner, such as signals and stocks. In this paper, we develop a sequential method to dynamically update the estimates of signal and noise strength in signal plus noise models. The proposed sequential method is easy to compute based on the stored statistics and the current data point. The consistency and, more importantly, the asymptotic normality of the estimators of signal strength and noise level are demonstrated for high dimensional settings under mild conditions. Simulations and real data examples are further provided to illustrate the practical utility of our proposal.

引用次数: 0

On the consistency of the jackknife estimator of the asymptotic variance of spatial median

IF 1.4 3区数学 Q2 STATISTICS & PROBABILITY

Journal of Multivariate Analysis

Pub Date : 2024-12-15 DOI: 10.1016/j.jmva.2024.105399

František Rublík

It is shown that the usual delete-1 jackknife variance estimator of the asymptotic variance of spatial median is consistent. This is proved under the assumptions that the dimension of the data

d \geq 3

, the sampled distribution possesses a density with respect to the Lebesgue measure and this density is bounded on every bounded subset of

R^{d}

引用次数: 0

High-dimensional projection-based ANOVA test

IF 1.4 3区数学 Q2 STATISTICS & PROBABILITY

Journal of Multivariate Analysis

Pub Date : 2024-12-15 DOI: 10.1016/j.jmva.2024.105401

Weihao Yu , Qi Zhang , Weiyu Li

In bioinformation and medicine, an enormous amount of high-dimensional multi-population data is collected. For the inference of several-samples mean problem, traditional tests do not perform well and many new theories mainly focus on normal distribution and low correlation assumptions. Motivated by the weighted sign test, we propose two projection-based tests which are robust against the choice of correlation matrix. One test utilizes Scheffe’s transformation to generate a group of new samples and derives the optimal projection direction. The other test is adaptive to projection direction and is generalized to the assumption of the whole elliptical distribution and independent component model. Further the theoretical properties are deduced and numerical experiments are carried out to examine the finite sample performance. They show that our tests outperform others under certain circumstances.

引用次数: 0

Quadratic inference with dense functional responses

IF 1.4 3区数学 Q2 STATISTICS & PROBABILITY

Journal of Multivariate Analysis

Pub Date : 2024-12-14 DOI: 10.1016/j.jmva.2024.105400

Pratim Guha Niyogi , Ping-Shou Zhong

We address the challenge of estimation in the context of constant linear effect models with dense functional responses. In this framework, the conditional expectation of the response curve is represented by a linear combination of functional covariates with constant regression parameters. In this paper, we present an alternative solution by employing the quadratic inference approach, a well-established method for analyzing correlated data, to estimate the regression coefficients. Our approach leverages non-parametrically estimated basis functions, eliminating the need for choosing working correlation structures. Furthermore, we demonstrate that our method achieves a parametric

\sqrt{n}

-convergence rate, contingent on an appropriate choice of bandwidth. This convergence is observed when the number of repeated measurements per trajectory exceeds a certain threshold, specifically, when it surpasses

n^{a_{0}}

, with

n

representing the number of trajectories. Additionally, we establish the asymptotic normality of the resulting estimator. The performance of the proposed method is compared with that of existing methods through extensive simulation studies, where our proposed method outperforms. Real data analysis is also conducted to demonstrate the proposed method.

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引用次数: 0

Efficient estimation of a partially linear panel data model with cross-sectional dependence

IF 1.4 3区数学 Q2 STATISTICS & PROBABILITY

Journal of Multivariate Analysis

Pub Date : 2024-12-05 DOI: 10.1016/j.jmva.2024.105393

Alexandra Soberon , Massimiliano Mazzanti , Antonio Musolesi , Juan M. Rodriguez-Poo

This paper considers efficiency improvements in a partially linear panel data model that accounts for possible nonlinear effects of common covariates and allows for cross-sectional dependence arising simultaneously from unobserved common factors and spatial dependence. A generalized least squares-type estimator is proposed by taking into account this dependence structure. Also, possible gains in terms of the rate of convergence are studied. A Monte Carlo study is carried out to investigate the proposed estimators’ finite sample performance. Further, an empirical application is conducted to assess the impact of the carbon price linked to the European Union Emission Trading System on carbon dioxide emissions.

引用次数: 0

Equality tests of covariance matrices under a low-dimensional factor structure

IF 1.4 3区数学 Q2 STATISTICS & PROBABILITY

Journal of Multivariate Analysis

Pub Date : 2024-12-03 DOI: 10.1016/j.jmva.2024.105397

Masashi Hyodo , Takahiro Nishiyama , Hiroki Watanabe , Tomoyuki Nakagawa , Kouji Tahata

We propose an equality test to compare two covariance matrices in a high-dimensional framework while accommodating a low-dimensional latent factor model. We show that null limiting distributions of the test statistics follow a weighted mixture of chi-square distributions under a high-dimensional asymptotic regime combined with weak technical conditions. This distribution depends on the noise covariance matrix and the number of latent factors. Because latent factors are often unknown, we employ an estimation that builds on recent advances in random matrix theory. A numerical study demonstrates the asymptotic power of the proposed test and confirms its favorable analytical properties compared to existing procedures.

引用次数: 0

Gaussian dependence structure pairwise goodness-of-fit testing based on conditional covariance and the 20/60/20 rule

IF 1.4 3区数学 Q2 STATISTICS & PROBABILITY

Journal of Multivariate Analysis

Pub Date : 2024-11-29 DOI: 10.1016/j.jmva.2024.105396

Jakub Woźny , Piotr Jaworski , Damian Jelito , Marcin Pitera , Agnieszka Wyłomańska

We present a novel data-oriented statistical framework that assesses the presumed Gaussian dependence structure in a pairwise setting. This refers to both multivariate normality and normal copula goodness-of-fit testing. The proposed test clusters the data according to the 20/60/20 rule and confronts conditional covariance (or correlation) estimates on the obtained subsets. The corresponding test statistic has a natural practical interpretation, desirable statistical properties, and asymptotic pivotal distribution under the multivariate normality assumption. We illustrate the usefulness of the introduced framework using extensive power simulation studies and show that our approach outperforms popular benchmark alternatives. Also, we apply the proposed methodology to exemplary commodity and equity market data.

引用次数: 0

Alteration detection of tensor dependence structure via sparsity-exploited reranking algorithm

IF 1.4 3区数学 Q2 STATISTICS & PROBABILITY

Journal of Multivariate Analysis

Pub Date : 2024-11-28 DOI: 10.1016/j.jmva.2024.105395

Li Ma , Shenghao Qin , Yin Xia

Tensor-valued data arise frequently from a wide variety of scientific applications, and many among them can be translated into an alteration detection problem of tensor dependence structures. In this article, we formulate the problem under the popularly adopted tensor-normal distributions and aim at two-sample correlation/partial correlation comparisons of tensor-valued observations. Through decorrelation and centralization, a separable covariance structure is employed to pool sample information from different tensor modes to enhance the power of the test. Additionally, we propose a novel Sparsity-Exploited Reranking Algorithm (SERA) to further improve the multiple testing efficiency. Such efficiency gain is achieved by incorporating a carefully constructed auxiliary tensor sequence to rerank the

p

-values. Besides the tensor framework, SERA is also generally applicable to a wide range of two-sample large-scale inference problems with sparsity structures, and is of independent interest. The asymptotic properties of the proposed test are derived and the algorithm is shown to control the false discovery at the pre-specified level. We demonstrate the efficacy of the proposed method through intensive simulations and two scientific applications.

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引用次数: 0

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