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Community detection in temporal citation network via a tensor-based approach Stat. Interface (IF 0.8) Pub Date : 2024-02-01 Tianchen Gao, Rui Pan, Junfei Zhang, Hansheng Wang
In the era of big data, network analysis has attracted widespread attention. Detecting and tracking community evolution in temporal networks can uncover important and interesting behaviors. In this paper, we analyze a temporal citation network constructed by publications collected from 44 statistical journals between 2001 and 2018. We propose an approach named Tensor-based Directed Spectral Clustering
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A random projection method for large-scale community detection Stat. Interface (IF 0.8) Pub Date : 2024-02-01 Haobo Qi, Hansheng Wang, Xuening Zhu
In this work, we consider a random projection method for a large-scale community detection task. We introduce a random Gaussian matrix that generates several projections on the column space of the network adjacency matrix. The $k$-means algorithm is then applied with the low-dimensional projected matrix. The computational complexity is much lower than that of the classic spectral clustering methods
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Correlated Wishart matrices classification via an expectation-maximization composite likelihood-based algorithm Stat. Interface (IF 0.8) Pub Date : 2024-02-01 Zhou Lan
Positive-definite matrix-variate data is becoming popular in computer vision. The computer vision data descriptors in the form of Region Covariance Descriptors (RCD) are positive definite matrices, which extract the key features of the images. The RCDs are extensively used in image set classification. Some classification methods treating RCDs as Wishart distributed random matrices are being proposed
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Learning conditional dependence graph for concepts via matrix normal graphical model Stat. Interface (IF 0.8) Pub Date : 2024-02-01 Jizheng Lai, Jianxin Yin
Conditional dependence relationships for random vectors are extensively studied and broadly applied. But it is not very clear how to construct the dependence graph for unstructured data like concept words or phrases in text corpus, where the variables(concepts) are not jointly observed with i.i.d. assumption. Using the global embedding methods like GloVe, we get the ‘structured’ representation vectors
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Bayesian tensor-on-tensor regression with efficient computation Stat. Interface (IF 0.8) Pub Date : 2024-02-01 Kunbo Wang, Yanxun Xu
We propose a Bayesian tensor-on-tensor regression approach to predict a multidimensional array (tensor) of arbitrary dimensions from another tensor of arbitrary dimensions, building upon the Tucker decomposition of the regression coefficient tensor. Traditional tensor regression methods making use of the Tucker decomposition either assume the dimension of the core tensor to be known or estimate it
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Multi-way overlapping clustering by Bayesian tensor decomposition Stat. Interface (IF 0.8) Pub Date : 2024-02-01 Zhuofan Wang, Fangting Zhou, Kejun He, Yang Ni
The development of modern sequencing technologies provides great opportunities to measure gene expression of multiple tissues from different individuals. The three-way variation across genes, tissues, and individuals makes statistical inference a challenging task. In this paper, we propose a Bayesian multi-way clustering approach to cluster genes, tissues, and individuals simultaneously. The proposed
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Density-convoluted tensor support vector machines Stat. Interface (IF 0.8) Pub Date : 2024-02-01 Boxiang Wang, Le Zhou, Jian Yang, Qing Mai
With the emergence of tensor data (also known as multi-dimensional arrays) in many modern applications such as image processing and digital marketing, tensor classification is gaining increasing attention. Although there is a rich toolbox of classification methods for vector-based data, these traditional methods may not be adequate for tensor data classification. In this paper, we propose a new classifier
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Bayesian methods in tensor analysis Stat. Interface (IF 0.8) Pub Date : 2024-02-01 Shi Yiyao, Shen Weining
Tensors, also known as multidimensional arrays, are useful data structures in machine learning and statistics. In recent years, Bayesian methods have emerged as a popular direction for analyzing tensor-valued data since they provide a convenient way to introduce sparsity into the model and conduct uncertainty quantification. In this article, we provide an overview of frequentist and Bayesian methods
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Rank-R matrix autoregressive models for modeling spatio-temporal data Stat. Interface (IF 0.8) Pub Date : 2024-02-01 Nan-Jung Hsu, Hsin-Cheng Huang, Ruey S. Tsay, Tzu-Chieh Kao
We develop a matrix-variate autoregressive (MAR) model to analyze spatio-temporal data organized on a regular grid in space. The model is an extension of the bilinear MAR spatial model of Hsu, Huang and Tsay $\href{ https://doi.org/10.1080/10618600.2021.1938587 }{[10]}$ by increasing its flexibility and applicability in empirical applications. Specifically, we propose to model each autoregressive (AR)
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Robust and covariance-assisted tensor response regression Stat. Interface (IF 0.8) Pub Date : 2024-02-01 Ning Wang, Xin Zhang
Tensor data analysis is gaining increasing popularity in modern multivariate statistics. When analyzing real-world tensor data, many existing tensor estimation approaches are sensitive to heavy-tailed data and outliers, in addition to the apparent high-dimensionality. In this article, we develop a robust and covariance-assisted tensor response regression model based on a recently proposed tensor t‑distribution
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Model-based statistical depth for matrix data Stat. Interface (IF 0.8) Pub Date : 2024-02-01 Yue Mu, Guanyu Hu, Wei Wu
The field of matrix data learning has witnessed significant advancements in recent years, encompassing diverse datasets such as medical images, social networks, and personalized recommendation systems. These advancements have found widespread application in various domains, including medicine, biology, public health, engineering, finance, economics, sports analytics, and environmental sciences. While
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Guiding light: An essay for Professor Lincheng Zhao on the occasion of his 80th birthday Stat. Interface (IF 0.8) Pub Date : 2023-11-27 Zhidong Bai
Lincheng Zhao was admitted to the Department of Applied Mathematics of the University of Science and Technology of China (USTC) in 1960, three years before me, and then took a year off due to illness and transferred to the entering class of 1961. We were both not good at socializing, so although we had been classmates for three years, we didn’t know each other. In 1978, when we were both admitted to
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Frequentist Bayesian compound inference Stat. Interface (IF 0.8) Pub Date : 2023-11-27 Jinfeng Xu, Ao Yuan
In practice often either the Bayesian or frequentist method is used, although there are some combined uses of the two methods, a formal unified methodology of the two hasn’t been seen. Here we first give a brief review of the two methods and some combination of the two, then propose a procedure using both the frequentist likelihood and the Bayesian posterior loss in parameter estimation and hypothesis
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Copy number variation detection based on constraint least squares Stat. Interface (IF 0.8) Pub Date : 2023-11-27 Xiaopu Wang, Xueqin Wang, Aijun Zhang, Canhong Wen
Copy number variations (CNVs) are a form of structural variation of a DNA sequence, including amplification and deletion of a particular DNA segment on chromosomes. Due to the huge amount of data in every DNA sequence, there is a great need for a computationally fast algorithm that accurately identifies CNVs. In this paper, we formulate the detection of CNVs as a constraint least squares problem and
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Sieve maximum likelihood estimation for generalized linear mixed models with an unknown link function Stat. Interface (IF 0.8) Pub Date : 2023-11-27 Guoqing Diao, Mengdie Yuan
We study the generalized linear mixed models with an unknown link function for correlated outcome data. We propose sieve maximum likelihood estimation procedures by using B‑splines. Specifically, we estimate the unknown link function in a sieve space spanned by the B‑spline basis of the linear predictor that includes both the fixed and random terms. We establish the consistency and asymptotic normality
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Robust and powerful gene-environment interaction tests using rare genetic variants in case-control studies Stat. Interface (IF 0.8) Pub Date : 2023-11-27 Yanan Zhao, Hong Zhang
Many association analysis methods have been developed to detect disease related rare genetic variants or gene-environment interactions. Most of them are based on prospectively likelihood, so they are robust but might not be powerful enough. On the other hand, retrospective likelihood based methods assuming gene-environment independence can effectively improve the association test power, but they suffer
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Aligning sample size calculations with estimands in clinical trials with time-to-event outcomes Stat. Interface (IF 0.8) Pub Date : 2023-11-27 Yixin Fang, Man Jin, Chengqing Wu
The ICH E9(R1) guidance recommended a framework to align planning, design, conduct, analysis, and interpretation of any clincial trial with its objective and estimand. How to handle intercurrent events (ICEs) is one of the five attributes of an estimand and sample size calculation is a key step in the trial planning and design. Therefore, sample size calculation should be aligned with the estimand
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A nonparametric concurrent regression model with multivariate functional inputs Stat. Interface (IF 0.8) Pub Date : 2023-11-27 Yutong Zhai, Zhanfeng Wang, Yuedong Wang
Regression models with functional responses and covariates have attracted extensive research. Nevertheless, there is no existing method for the situation where the functional covariates are bivariate functions with one of the variables in common with the response function. In this article, we propose a nonparametric function-on-function regression method. We construct model spaces using a Gaussian
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Latent class proportional hazards regression with heterogeneous survival data Stat. Interface (IF 0.8) Pub Date : 2023-11-27 Teng Fei, John J. Hanfelt, Limin Peng
Heterogeneous survival data are commonly present in chronic disease studies. Delineating meaningful disease subtypes directly linked to a survival outcome can generate useful scientific implications. In this work, we develop a latent class proportional hazards (PH) regression framework to address such an interest. We propose mixture proportional hazards modeling, which flexibly accommodates class-specific
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Abnormal sample detection based on robust Mahalanobis distance estimation in adversarial machine learning Stat. Interface (IF 0.8) Pub Date : 2023-11-27 Wan Tian, Lingyue Zhang, Hengjian Cui
This paper addresses the problem of abnormal sample detection in deep learning-based computer vision, focusing on two types of abnormal samples: outlier samples and adversarial samples. The presence of these abnormal samples can significantly degrade the performance and robustness of deep learning models, posing security risks in critical areas. To address this, we propose a method that combines robust
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Asymptotic properties of relative error estimation for accelerated failure time model with divergent number of parameters Stat. Interface (IF 0.8) Pub Date : 2023-11-27 Fei Ye, Hongyi Zhou, Ying Yang
The paper considers the problem of parameter estimation in the accelerated failure time model with divergent number of parameters under fixed design. We propose an estimator based on the general relative error criterion. We show that the proposed estimator is consistent and asymptotically normal under mild regular conditions. We also propose a variable selection procedure and show its oracle property
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A review of nonparametric regression methods for longitudinal data Stat. Interface (IF 0.8) Pub Date : 2023-11-27 Changxin Yang, Zhongyi Zhu
Longitudinal data, which involve measuring a group of subjects repeatedly over time, frequently arise in many clinical and biomedical applications. To identify the complex patterns of change in the outcome and their association with covariates over time, a sufficiently flexible model is always required. Nonparametric regression, known for being data-adaptive and less restrictive than parametric approaches
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Multivariate frailty models using survey weights with applications to twins infant mortality in Ethiopia Stat. Interface (IF 0.8) Pub Date : 2023-04-14 Yehenew G. Kifle, Ding-Geng Chen, Mesfin T. Haileyesus
Several studies have shown that twin birth contributes substantially to infant and child mortality mainly in resource-poor countries. The excess rates among twins call for research in statistical modeling to identify the main causes behind it. In studies involving multiple individuals from the same family, the fundamental independence assumption in the classical statistical modeling is not plausible
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Estimating individualized treatment rules for multicategory type 2 diabetes treatments using electronic health records Stat. Interface (IF 0.8) Pub Date : 2023-04-14 Jitong Lou, Yuanjia Wang, Lang Li, Donglin Zeng
In this article, we propose a general framework to learn optimal treatment rules for type 2 diabetes (T2D) patients using electronic health records (EHRs). We first propose a joint modeling approach to characterize patient’s pretreatment conditions using longitudinal markers from EHRs. The estimation accounts for informative measurement times using inverse-intensity weighting methods. The predicted
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A default Bayesian multiple comparison of two binomial proportions Stat. Interface (IF 0.8) Pub Date : 2023-04-14 Emrah Gecili, Siva Sivaganesan
We consider a default Bayesian approach to multiple testing of equality of two binomial proportions. While our approach is motivated by a scenario where one proportion corresponds to an experimental condition and the other to a control, we find it is also reasonable for comparing two proportions in general. We consider a selection of priors under the alternative(s) including the intrinsic prior and
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Markov-switching Poisson generalized autoregressive conditional heteroscedastic models Stat. Interface (IF 0.8) Pub Date : 2023-04-14 Jichun Liu, Yue Pan, Jiazhu Pan, Abdullah Almarashi
We consider a kind of regime-switching autoregressive models for nonnegative integer-valued time series when the conditional distribution given historical information is Poisson distribution. In this type of models the link between the conditional variance (i.e. the conditional mean for Poisson distribution) and its past values as well as the observed values of the Poisson process may be different
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SIMEX estimation for quantile regression model with measurement error Stat. Interface (IF 0.8) Pub Date : 2023-04-14 Yiping Yang, Peixin Zhao, Dongsheng Wu
The quantile regression model with measurement error is considered. To deal with measurement error, we extend the simulation-extrapolation (SIMEX) method to the case of quantile regressions in the presence of covariate measurement error. The proposed SIMEX estimation corrects the bias caused by the measurement error, and not requires the equal distribution assumption of the regression error and measurement
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A pairwise pseudo-likelihood approach for the additive hazards model with left-truncated and interval-censored data Stat. Interface (IF 0.8) Pub Date : 2023-04-14 Peijie Wang, Yichen Lou, Jianguo Sun
Left-truncated and interval-censored data occur commonly and some approaches have been proposed in the literature for their analysis. However, most of the existing methods are based on the conditional likelihood given left-truncation times, which can be inefficient since the information in the marginal likelihood of the truncation times is ignored. To address this, in this paper, a pairwise pseudo-likelihood
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Metric distributional discrepancy in metric space Stat. Interface (IF 0.8) Pub Date : 2023-04-14 Wenliang Pan, Yujue Li, Jianwu Liu, Pei Dang, Weixiong Mai
Independence analysis is an indispensable step before regression analysis to find out the essential factors that influence the objects. With many applications in machine Learning, medical Learning and a variety of disciplines, statistical methods of measuring the relationship between random variables have been well studied in vector spaces. However, there are few methods developed to verify the relation
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On the optimal configuration of a square array group testing algorithm Stat. Interface (IF 0.8) Pub Date : 2023-04-14 Ugn Čiżikovienė, Viktor Skorniakov
Up to date, only lower and upper bounds for the optimal configuration of a Square Array (A2) Group Testing (GT) algorithm are known. We establish exact analytical formulae and provide a couple of applications of our result. First, we compare the A2 GT scheme to several other classical GT schemes in terms of the gain per specimen attained at optimal configuration. Second, operating under objective Bayesian
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Network vector autoregressive moving average model Stat. Interface (IF 0.8) Pub Date : 2023-04-14 Xiao Chen, Yu Chen, Xixu Hu
Modeling a continuous response of a large-scale network is an important task and it has become prevailing in practice at present. This paper proposes a novel network vector autoregressive moving average (NARMA) model which considers the responses from both an ultra-high dimension vector and the network structure effects. Compared with the network vector autoregressive (NAR, [26]) model, we take into
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Fine-tuned sensitivity analysis for non-ignorable missing data mechanism in linear regression models Stat. Interface (IF 0.8) Pub Date : 2023-04-14 Rong Zhu, Peng Yin, Jian Qing Shi
Missing data is a widespread problem in many fields, such as statistical analysis in medical research. The missing data mechanism (MDM) is overly complicated in many cases, and the most complex one is the non-ignorable missingness. In this paper, we analyse the incomplete data bias of maximum likelihood estimates on the inference of linear regression models with non-ignorable missing covariate specifically
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Online change-point detection for a transient change Stat. Interface (IF 0.8) Pub Date : 2023-04-13 Jack Noonan
We consider a popular online change-point problem of detecting a transient change in distributions of independent random variables. For this change-point problem, several change-point procedures are formulated and some advanced results for a particular procedure are surveyed. Some new approximations for the average run length to false alarm are offered and the power of these procedures for detecting
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Study of impact of COVID-19 on industrial production indices using singular spectrum analysis Stat. Interface (IF 0.8) Pub Date : 2023-04-13 Sofia Borodich Suarez, Andrey Pepelyshev
This paper investigates the impact of the COVID-19 pandemic on 8 different indices of industrial production (IIPs) for three major European countries: France, Germany, and the UK. The analysis is based on applying a combination of Singular Spectrum Analysis (SSA) algorithms, in a way that allows for the proper separation of the trend and seasonal subcycles of the IIPs. The main purpose is to illustrate
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Quantile recurrent forecasting in singular spectrum analysis for stock price monitoring Stat. Interface (IF 0.8) Pub Date : 2023-04-13 Atikur R. Khan, Hossein Hassani
Monitoring of near real-time price movement is necessary for data-driven decision making in opening and closing positions for day traders and scalpers. This can be done effectively by constructing a movement path based on forecast distribution of stock prices. High frequency trading data are generally noisy, nonlinear and nonstationary in nature. We develop a quantile recurrent forecasting algorithm
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Least absolute deviations estimation for nonstationary vector autoregressive time series models with pure unit roots Stat. Interface (IF 0.8) Pub Date : 2023-04-13 Yao Zheng, Jianhong Wu, Wai Keung Li, Guodong Li
This paper derives the asymptotic distribution of the least absolute deviations estimator for nonstationary vector autoregressive time series models with pure unit roots under mild conditions. As this distribution has a complicated form, many commonly used bootstrap techniques cannot be directly applied. To tackle this problem, we propose a novel hybrid bootstrap method by combining the classical wild
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Modified recurrent forecasting in singular spectrum analysis using Kalman filter and its application for bicoid signal extraction Stat. Interface (IF 0.8) Pub Date : 2023-04-13 Reza Zabihi Moghadam, Masoud Yarmohammadi, Hossein Hassani
One of the important topics in Drosophila melanogaster is statistical analysis of bicoid protein gradient. The bicoid protein gradient plays an important role in the segmentation stage of embryo development in the head and thorax and also has considerable noise. Therefore, it has been considered by many researchers. In this paper the state space model and Kalman filter algorithms are used for noise
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Smooth online parameter estimation for time varying VAR models with application to rat local field potential activity data Stat. Interface (IF 0.8) Pub Date : 2023-04-13 Anass El Yaagoubi Bourakna, Marco Pinto, Norbert Fortin, Hernando Ombao
Multivariate time series data appear often as realizations of non-stationary processes where the covariance matrix or spectral matrix smoothly evolve over time. Most of the current approaches estimate the time-varying spectral properties only retrospectively – that is, after the entire data has been observed. Retrospective estimation is a major limitation in many adaptive control applications where
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Approximate hidden semi-Markov models for dynamic connectivity analysis in resting-state fMRI Stat. Interface (IF 0.8) Pub Date : 2023-04-13 Mark B. Fiecas, Christian Coffman, Meng Xu, Timothy J. Hendrickson, Bryon A. Mueller, Bonnie Klimes-Dougan, Kathryn R. Cullen
Motivated by a study on adolescent mental health, we conduct a dynamic connectivity analysis using resting-state functional magnetic resonance imaging (fMRI) data. A dynamic connectivity analysis investigates how the interactions between different regions of the brain, represented by the different dimensions of a multivariate time series, change over time. HiddenMarkov models (HMMs) and hidden semi-Markov
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Modeling water table depth using singular spectrum analysis Stat. Interface (IF 0.8) Pub Date : 2023-04-13 Rahim Mahmoudvand, Mehrdad Barati, Asghar Seif, Sahar Ranjbaran, Paulo Canas Rodrigues
The majority of countries are facing or will face a serious water crisis. As a consequence, we observe a deterioration in the water quality such as the drop in the water table and a salinity increase. Therefore, it is highly recommended to conduct a regular monitoring program on groundwater levels in order to sustain this source. Water table depth (WTD) is an index of water availability that influences
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Hankel low-rank approximation and completion in time series analysis and forecasting: a brief review Stat. Interface (IF 0.8) Pub Date : 2023-04-13 Jonathan Gillard, Konstantin Usevich
In this paper we offer a review and bibliography of work on Hankel low-rank approximation and completion, with particular emphasis on how this methodology can be used for time series analysis and forecasting.We begin by describing possible formulations of the problem and offer commentary on related topics and challenges in obtaining globally optimal solutions. Key theorems are provided, and the paper
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Uniform consistency for local fitting of time series non-parametric regression allowing for discrete-valued response Stat. Interface (IF 0.8) Pub Date : 2023-04-13 Rong Peng, Zudi Lu
Local linear kernel fitting is a popular nonparametric technique for modelling nonlinear time series data. Investigations into it, although extensively made for continuousvalued case, are still rare for the time series that are discrete-valued. In this paper, we propose and develop the uniform consistency of local linear maximum likelihood (LLML) fitting for time series regression allowing response
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Empirical likelihood-based portmanteau tests for autoregressive moving average models with possible infinite variance innovations Stat. Interface (IF 0.8) Pub Date : 2023-04-13 Xiaohui Liu, Donghui Fan, Xu Zhang, Catherine Liu
It is an important task in the literature to check whether a fitted autoregressive moving average (ARMA) model is adequate, while the currently used tests may suffer from the size distortion problem when the underlying autoregressive models have low persistence. To fill this gap, this paper proposes two empirical likelihood-based portmanteau tests. The first one is naive but can serve as a benchmark
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Confidence in the treatment decision for an individual patient: strategies for sequential assessment. Stat. Interface (IF 0.8) Pub Date : 2023-04-14 Nina Orwitz,Thaddeus Tarpey,Eva Petkova
Evolving medical technologies have motivated the development of treatment decision rules (TDRs) that incorporate complex, costly data (e.g., imaging). In clinical practice, we aim for TDRs to be valuable by reducing unnecessary testing while still identifying the best possible treatment for a patient. Regardless of how well any TDR performs in the target population, there is an associated degree of
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Adaptive Clustering and Feature Selection for Categorical Time Series Using Interpretable Frequency-Domain Features. Stat. Interface (IF 0.8) Pub Date : 2023-04-13 Scott A Bruce
This article presents a novel approach to clustering and feature selection for categorical time series via interpretable frequency-domain features. A distance measure is introduced based on the spectral envelope and optimal scalings, which parsimoniously characterize prominent cyclical patterns in categorical time series. Using this distance, partitional clustering algorithms are introduced for accurately
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On dual-asymmetry linear double AR models Stat. Interface (IF 0.8) Pub Date : 2022-07-27 Songhua Tan, Qianqian Zhu
This paper introduces a dual-asymmetry linear double autoregressive (DA‑LDAR) model that can allow for asymmetric effects in both the conditional location and volatility components of time series data. The strict stationarity is discussed for the new model, for which a sufficient condition is established. A self-weighted exponential quasi-maximum likelihood estimator (EQMLE) is proposed for the DA‑LDAR
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Generalized Gaussian time series model for increments of EEG data Stat. Interface (IF 0.8) Pub Date : 2022-07-27 Nikolai N. Leonenko, Željka Salinger, Alla Sikorskii, Nenad Šuvak, Michael Boivin
We propose a new strictly stationary time series model with marginal generalized Gaussian distribution and exponentially decaying autocorrelation function for modeling of increments of electroencephalogram (EEG) data collected from Ugandan children during coma from cerebral malaria. The model inherits its appealing properties from the strictly stationary strong mixing Markovian diffusion with invariant
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Forecasting industrial production indices with a new singular spectrum analysis forecasting algorithm Stat. Interface (IF 0.8) Pub Date : 2022-07-27 Sofia Borodich Suarez, Saeed Heravi, Andrey Pepelyshev
Existing time series analysis and forecasting approaches struggle to produce accurate results in application to time series with complex trend, such as those commonly displayed by indices of industrial production (IIPs). In this study, a new version of the Singular Spectrum Analysis (SSA) technique is developed, namely the Separate Trend and Seasonality (SSA‑STS) forecasting algorithm. Its performance
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Testing threshold effect in single-index models Stat. Interface (IF 0.8) Pub Date : 2022-07-27 Zhaoxing Gao, Zichuan Mi, Shiqing Ling
This paper studies the supremum-type score test for the single-index model against a threshold single-index model. It is shown that the test weakly converges a maxima of a Gaussian process under the null hypothesis. The bootstrap method is used to tackle the bias problem and provide the $p$-values of our test statistic. Simulations are carried out to assess the performance of our procedure and real
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Analyses of the impact of country specific macro risk variables on gold futures contract and its position as an asset class: evidence from India Stat. Interface (IF 0.8) Pub Date : 2022-07-27 Rupel Nargunam, William W. S. Wei, N. Anuradha
This paper discusses the dependence of gold futures prices on macro risk factors using a multiple linear regression model. Recently introduced uncertainty indexes such as geopolitical risk index and economic policy uncertainty index are included in this study. We also examine the investment nature of gold futures contract among other assets. The results provide insights on the influence of these interrelated
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Hierarchical dynamic PARCOR models for analysis of multiple brain signals Stat. Interface (IF 0.8) Pub Date : 2022-07-27 Wenjie Zhao, Raquel Prado
We present an efficient hierarchical model for inferring latent structure underlying multiple non-stationary time series. The proposed model describes the time-varying behavior of multiple time series in the partial autocorrelation domain, which results in a lower dimensional representation, and consequently computationally faster inference, than those required by models in the time and/or frequency
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Robust conditional spectral analysis of replicated time series Stat. Interface (IF 0.8) Pub Date : 2022-07-27 Zeda Li
Classical second-order spectral analysis, which is based on the Fourier transform of the autocovariance functions, focuses on summarizing the oscillatory behaviors of a time series. However, this type of analysis is subject to two major limitations: first, being covariance-based, it cannot captures oscillatory information beyond the second moment, such as time-irreversibility and kurtosis, and cannot
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AutoSpec: detection of narrowband frequency changes in time series Stat. Interface (IF 0.8) Pub Date : 2022-07-27 David S. Stoffer
Most established techniques that search for structural breaks in time series have a difficult time identifying small changes in the process, especially when looking for narrowband frequency changes. The problem is that many of the techniques assume very smooth local spectra and tend to produce overly smooth estimates. The problem of oversmoothing tends to produce spectral estimates that miss slight
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Study of automatic choice of parameters for forecasting in singular spectrum analysis Stat. Interface (IF 0.8) Pub Date : 2022-07-27 Safia Al-Marhoobi, Andrey Pepelyshev
Singular spectrum analysis (SSA) is a popular tool for analysing and forecasting time series. The SSA forecasting algorithms have two parameters which should be chosen by the researcher or using the so-called automatic choice based on the root mean squared errors (RMSE) of retrospective forecasts. We study the sensitivity of the RMSE and investigate the reliability of the automatic choice of parameters
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Low-rank signal subspace: parameterization, projection and signal estimation Stat. Interface (IF 0.8) Pub Date : 2022-07-27 Nikita Zvonarev, Nina Golyandina
The paper contains several theoretical results related to the weighted nonlinear least-squares problem for low-rank signal estimation, which can be considered as a Hankel structured low-rank approximation problem. A parameterization of the subspace of low-rank time series connected with generalized linear recurrence relations (GLRRs) is described and its features are investigated. It is shown how the
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The elliptical Ornstein–Uhlenbeck process Stat. Interface (IF 0.8) Pub Date : 2022-07-27 Adam Sykulski, Sofia Olhede, Hanna Sykulska-Lawrence
We introduce the elliptical Ornstein–Uhlenbeck (OU) process, which is a generalisation of the well-known univariate OU process to bivariate time series. This process maps out elliptical stochastic oscillations over time in the complex plane, which are observed in many applications of coupled bivariate time series. The appeal of the model is that elliptical oscillations are generated using one simple
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Detection of signals by Monte Carlo singular spectrum analysis: multiple testing Stat. Interface (IF 0.8) Pub Date : 2022-07-27 Nina Golyandina
Detection of a signal in a noisy time series using Monte Carlo singular spectrum analysis (MC‑SSA) is studied from the statistical viewpoint. The MC‑SSA test consists of simultaneous testing of several hypotheses related to the presence of different frequencies. The multiple MC‑SSA test procedure is constructed to control the family-wise error rate. The technique to control both the type I and the
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Causal measures using generalized difference-in-difference approach with nonlinear models Stat. Interface (IF 0.8) Pub Date : 2022-03-04 Marcelo M. Taddeo, Leila D. Amorim, Rosana Aquino
To assess the impact of interventions on observational studies, several approaches have been proposed for identification of causal effects. They include propensity score matching, regression discontinuity, instrumental variables and causal graphs. In this paper, we focus on the Differences-in-Differences. We review the subject, discuss its scope and limitations, and extend it to a class of nonlinear
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Sufficient dimension reduction for spatial point processes using weighted principal support vector machines Stat. Interface (IF 0.8) Pub Date : 2022-03-04 Subha Datta, Ji Meng Loh
We consider sufficient dimension reduction (SDR) for spatial point processes. SDR methods aim to identify a lower dimensional sufficient subspace of a data set, in a model-free manner. Most SDR results are based on independent data, and also often do not work well with binary data. [13] introduced a SDR framework for spatial point processes by characterizing point processes as a binary process, and
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Partial profile score feature selection in high-dimensional generalized linear interaction models Stat. Interface (IF 0.8) Pub Date : 2022-03-04 Zengchao Xu, Shan Luo, Zehua Chen
Sequential method is promising for feature selection in high-dimensional models. In this paper, we propose a sequential approach based on partial profile score dubbed as PPSFS to feature selection for a broad class of high-dimensional models, including high-dimensional generalized linear interaction models. The PPSFS approach has a prominent performance in feature selection while it keeps highly scalable