A statistical predictive model in which a high-dimensional time-series regenerates at the end of each day is used to model road traffic. Due to the regeneration, prediction is based on a daily modeling using a vector autoregressive model that combines linearly the past observations of the day. Due to the high-dimension, the learning algorithm follows from an ℓ1-penalization of the regression coefficients

Since the inception of the FIRST Robotics Competition (FRC) and its special playoff system, robotics teams have longed to appropriately quantify the strengths of their designed robots. The FRC includes a playground draft-like phase (alliance selection), arguably the most game-changing part of the competition, in which the top-8 robotics teams in a tournament based on the FRC’s ranking system assess

Feature screening has become an indispensable statistical modelling tool for ultrahigh dimensional data analysis. This article introduces a new model-free marginal feature screening approach for ultrahigh dimensional survival data with right censoring. The new procedure could be used for survival data with both ultrahigh dimensional categorical and continuous covariates. Motivated by Cui et al. (2015)

We propose a method for testing hypothesis on parametric family of transition probabilities of a Markov sequence, when the asymptotic distribution of the empirical processes involved is, largely, independent from the specific form of the parametric family. We first consider function-parametric empirical process for the Markov sequence and describe its weak limit as a certain projection. Then we establish

Most existing methods of variable selection in partially linear models (PLM) with ultrahigh dimensional covariates are based on partial residuals, which involve a two-step estimation procedure. While the estimation error produced in the first step may have an impact on the second step, multicollinearity among predictors adds additional challenges in the model selection procedure. In this paper, we

Spatio-temporal processes with a continuous index in space and time are useful for modeling spatio-temporal data in many scientific disciplines such as environmental and health sciences. However, approaches that enable simultaneous estimation of the mean and covariance functions for such spatio-temporal processes are limited. Here, we propose a flexible spatio-temporal model with partially linear regression

Data envelopment analysis (DEA) is a useful tool for measuring the relative efficiencies of participating nations in the Olympic Games. DEA models with restricted multipliers have been used to refine efficiency evaluations by imposing additional information. Existing DEA models for evaluating Olympic medals do not focus on multiplier restrictions regarding input. To fill this research gap, this study

We derive a one-sided Vysochanskii-Petunin inequality, providing probability bounds for random variables analogous to those given by Cantelli’s inequality under the additional assumption of unimodality, potentially relevant for applied statistical practice across a wide range of disciplines. As a possible application of this inequality in a financial context, we examine refined bounds for the individual

We make a comparison of the finite difference and multiresolution method for solving the elliptic equations on irregular domains. The Dirichlet boundary condition is treated by the ghost fluid method (GFM) for the finite difference method and the Lagrange multiplier for the multiresolution method. Numerical results illustrate the improved convergence rate of errors and their gradients with the multiresolution

In this paper we present a mathematical model and a numerical workflow for the simulation of a thermal single-phase flow with reactive transport in porous media, in the presence of fractures. The latter are thin regions which might behave as high or low permeability channels depending on their physical parameters, and are thus of paramount importance in underground flow problems. Chemical reactions

This work presents, for the first time, a dual time stepping (DTS) approach to solve the global system of equations that appears in the hybridisable discontinuous Galerkin (HDG) formulation of convection-diffusion problems. A proof of the existence and uniqueness of the steady state solution of the HDG global problem with DTS is presented. The stability limit of the DTS approach is derived using a

Comparing individual contributions in a strongly interacting system of stochastic growth processes can be a very difficult problem. This is particularly the case when new growth processes are initiated depending on the state of previous ones and the growth rates of the individual processes are themselves random. We propose a novel technique to deal with such problems and show how it can be applied

In this paper, an IPW-based robust estimator is developed for the spatial autoregressive model with response missing at random. Its consistency and asymptotical normality are proved and its finite-sample performance is investigated by simulations.

We derive new upper and lower bounds for probabilities that r or at least r out of n events occur. These bounds are optimal since they can turn to equalities. We describe a method of constructing of such bounds. It can be applied in case of measurable spaces and measures with sign as well. We also obtain bounds for conditional probabilities of combinations of events given σ-field. Averaging of both

Introducing a new method for studying general probability distributions on Rn, we generalize some results about the least singular value and the condition number of random matrices with i.i.d. gaussian entries to the whole class of random matrices with i.i.d. rows.

This paper presents a multisegment optimization design algorithm for both even- and odd-order variable fractional-delay filters. The proposed algorithm is based on segmenting the fractional delay and separately solving the filter coefficients in each segment. Moreover, the lower bound of the polynomial order and the upper bound of the subfilter design error are derived. These bounds enable us to prove

In this paper, we propose a mutual interference alignment (IA) method for interference elimination in co-existing multiple-input multiple-output (MIMO) radar and multi-user MIMO communication systems, namely, radar-communication systems. Traditional IA methods proposed for communication systems cannot be directly adopted in the radar-communications due to failure to fulfill the requirements of radar

We show that, when performance on a math test depends on students’ endurance, longer tests do not necessarily provide a more accurate measure of students’ competencies or a higher correlation between the ranking of classes (or schools) based on test performance and the ranking based on the underlying competencies. We revisit the finding that female teenagers are better able to sustain performance during

It is natural, when contemplating an historical record of events, to base a simple estimator of the event‐rate on that recent part of the record where the recording probability is thought to be effectively 1. After all, this avoids the downward bias which would be incurred by ‘overshooting’ into a time where the recording probability was less than 1. However, there is a trade‐off, because overshooting

Abstract In many applications, smooth processes generate data that is recorded under a variety of observational regimes, including dense sampling and sparse or fragmented observations that are often contaminated with error. The statistical goal of registering and estimating the individual underlying functions from discrete observations has thus far been mainly approached sequentially without formal

Abstract Statistical inferences for quadratic functionals of linear regression parameter have found wide applications including signal detection, global testing, inferences of error variance and fraction of variance explained. Classical theory based on ordinary least squares estimator works perfectly in the low-dimensional regime, but fails when the parameter dimension pn grows proportionally to the

Abstract In ordinary quantile regression, quantiles of different order are estimated one at a time. An alternative approach, which is referred to as quantile regression coefficients modeling (qrcm), is to model quantile regression coefficients as parametric functions of the order of the quantile. In this paper, we describe how the QRCM paradigm can be applied to longitudinal data. We introduce a two-level

Abstract Multisample covariance estimation—that is, estimation of the covariance matrices associated with k distinct populations—is a classical problem in multivariate statistics. A common solution is to base estimation on the outcome of a test that these covariance matrices show some given pattern. Such a preliminary test may, e.g., investigate whether or not the various covariance matrices are equal

We propose a nonparametric method for density estimation over (possibly complicated) spatial domains. The method combines a likelihood approach with a regularization based on a differential operator. We demonstrate the good inferential properties of the method. Moreover, we develop an estimation procedure based on advanced numerical techniques, and in particular making use of finite elements. This

Population means and standard deviations are the most common estimands to quantify effects in factorial layouts. In fact, most statistical procedures in such designs are built toward inferring means or contrasts thereof. For more robust analyses, we consider the population median, the interquartile range (IQR) and more general quantile combinations as estimands in which we formulate null hypotheses

We consider the problem of ascertaining daily patterns using passive sensors to establish a baseline for elderly people living alone. The data are whether or not some movement, or human related activity, has occurred in the previous 15 min. We seek to segment the broad patterns within a day, for example, awake/sleep times or potentially more activity around meal‐times. To address this problem we use

The question of association between outcome and feature is generally framed in the context of a model based on functional and distributional forms. Our motivating application is that of identifying serum biomarkers of angiogenesis, energy metabolism, apoptosis and inflammation, predictive of recurrence after lung resection in node‐negative non‐small cell lung cancer patients with tumour stage T2a or

Abstract This note develops a score-like test for the existence of threshold effect in regression kink model. The test statistics is based on the CUSUM process of subgradients, and only requires fitting the model under null hypothesis. The critical values can be obtained by residual bootstrap. Four kinds of methods are also proposed for estimating the kink point and other model coefficients: grid-search

Abstract Statistical inference for precision matrix is of fundamental importance nowadays for learning conditional dependence structure in high-dimensional graphical models. Despite the fast growing literature, how to develop scalable inference with insensitive tuning of the regularization parameters still remains unclear in high dimensions. In this paper, we develop a new method called the graphical

Abstract A new class of statistics obtained by ordering the absolute values of the observations arising from absolutely continuous distributions which are symmetrically distributed about zero is introduced in this paper. The statistics generated by the above method are named as absolved order statistics (AOS) of the given sample. The association of the distribution of these statistics with the distribution

When comparing survival times of treatment and control groups under a more realistic nonproportional hazards scenario, the standard log‐rank (SLR) test may be replaced by a more efficient weighted log‐rank (WLR) test, such as the Fleming‐Harrington (FH) test. Designing a group‐sequential clinical trial with one or more interim looks during which a FH test will be performed, necessitates correctly quantifying

Inverse probability of treatment weighting (IPTW) may be biased by influential observations, which can occur from misclassification of strong exposure predictors.

A periodic-review insurance model is studied under the following assumptions. One-period insurance claims form a sequence of independent identically distributed nonnegative random variables with a finite mean. At the beginning of each period a quota δ of the company surplus is invested in a non-risky asset for m periods. Theoretical expressions for finite-time and ultimate ruin probabilities, in terms

ABSTRACT Studies using structural equation modeling (SEM) to evaluate theories against observed data rely on multiple sources of evidence to support a proposed model, such as fit indices, variance explained, and comparison of alternative models. Additional evidence can be obtained by evaluating the model results’ sensitivity to an omitted confounder. The phantom variable approach to SEM sensitivity

Abstract Let {X(t),t≥0} be a Wiener process with infinitesimal mean μ and variance 1, starting at X(0)∈[0,1]. Assume that X(0) is a random variable and define τ to be the first time that X(t) = 0 or 1. We look for probability density functions of X(0) such that P[X(τ)=0]=q∈(0,1). Various cases are considered both when μ = 0 and μ≠0.

Inferring causal effect from observational studies is a central topic in many scientific fields, including social science, health and medicine. The statistical methodology for estimating population average causal effect has been well established. However, the methods for identifying and estimating subpopulation causal effects are relatively less developed. Part of the challenge is that the subgroup

In survey domain estimation, a priori information can often be imposed in the form of linear inequality constraints on the domain estimators. Wu et al. (2016) formulated the isotonic domain mean estimator, for the simple order restriction, and methods for more general constraints were proposed in Oliva-Avilés et al. (2020). When the assumptions are valid, imposing restrictions on the estimators will

Mobile medical units (MMUs) are customized vehicles fitted with medical equipment that are used to provide primary care in rural environments. As MMUs can be easily relocated, they enable a demand-oriented, flexible, and local provision of health services. In this paper, we investigate the strategic planning of an MMU service by deciding where MMU operation sites should be set up and how often these

This study presents an approach to discover the significant terms in partial differential equations (PDEs) that describe particular phenomena based on the measured data. The relationship between the known field data and its continuous representation of PDEs is achieved through a linear regression model. It specifically employs the peridynamic differential operator (PDDO) and sparse linear regression

Statistical (machine learning) tools for equation discovery require large amounts of data that are typically computer generated rather than experimentally observed. Multiscale modeling and stochastic simulations are two areas where learning on simulated data can lead to such discovery. In both, the data are generated with a reliable but impractical model, e.g., molecular dynamics simulations, while

Simulations of turbulent fluid flow around long cylindrical structures are computationally expensive because of the vast range of length scales, requiring simplifications such as dimensional reduction. Current dimensionality reduction techniques such as strip-theory and depth-averaged methods do not take into account the natural flow dissipation mechanism inherent in the small-scale three-dimensional

Detailed description of the transport processes in plasma is crucial for many disciplines. When the mean-free-path of the electrons is comparable or exceeds a characteristic length scale of the plasma profile, non-local behavior can be observed. Predictions of the diffusion theory are not valid and non-local electric and magnetic fields are generated. Kinetic modeling of these phenomena on time scales

In this paper, we introduce a new locally implicit sonic point processing algorithm for one-dimensional shallow-water equations. The algorithm is based on transferring the shallow-water invariants along the characteristics around the sonic point, and it can be coupled with conservative-characteristic methods, which have problems modelling the transonic flows. For each sonic point, a system of two non-linear

An arbitrary order finite difference method for curved boundary domains with Cartesian grid is proposed. The technique handles in a universal manner Dirichlet, Neumann or Robin conditions. We introduce the Reconstruction Off-site Data (ROD) method, that transfers in polynomial functions the information located on the physical boundary to the computational domain. Three major advantages are: (1) a simple

In this paper, we study a parallel-in-time (PinT) algorithm for all-at-once system from a non-local evolutionary equation with weakly singular kernel where the temporal term involves a non-local convolution with a weakly singular kernel and the spatial term is the usual Laplacian operator with variable coefficients. Such a problem has been intensively studied in recent years thanks to the numerous

Researchers often trim observations with small values of the denominator A when they estimate moments of the form $\mathbb {E}[B/A]$. Large trimming is common in practice to reduce variance, but it incurs a large bias. This paper provides a novel method of correcting the large trimming bias. If a researcher is willing to assume that the joint distribution between A and B is smooth, then the trimming

Backdoor data poisoning attacks add mislabeled examples to the training set, with an embedded backdoor pattern, so that the classifier learns to classify to a target class whenever the backdoor pattern is present in a test sample. Here, we address posttraining detection of scene-plausible perceptible backdoors, a type of backdoor attack that can be relatively easily fashioned, particularly against

Under the classical long-span asymptotic framework, we develop a class of generalized laplace (GL) inference methods for the change-point dates in a linear time series regression model with multiple structural changes analyzed in, e.g., Bai and Perron (1998, Econometrica 66, 47–78). The GL estimator is defined by an integration rather than optimization-based method and relies on the LS criterion function

It is of great interest to characterize the spiking activity of individual neurons in a cell ensemble. Many different mechanisms, such as synaptic coupling and the spiking activity of itself and its neighbors, drive a cell's firing properties. Though this is a widely studied modeling problem, there is still room to develop modeling solutions by simplifications embedded in previous models. The first

We propose a novel biologically plausible solution to the credit assignment problem motivated by observations in the ventral visual pathway and trained deep neural networks. In both, representations of objects in the same category become progressively more similar, while objects belonging to different categories become less similar. We use this observation to motivate a layer-specific learning goal

Pairwise similarities and dissimilarities between data points are often obtained more easily than full labels of data in real-world classification problems. To make use of such pairwise information, an empirical risk minimization approach has been proposed, where an unbiased estimator of the classification risk is computed from only pairwise similarities and unlabeled data. However, this approach has

This letter introduces a new framework for quantifying predictive uncertainty for both data and models that rely on projecting the data into a gaussian reproducing kernel Hilbert space (RKHS) and transforming the data probability density function (PDF) in a way that quantifies the flow of its gradient as a topological potential field quantified at all points in the sample space. This enables the decomposition

The relationship between complex brain oscillations and the dynamics of individual neurons is poorly understood. Here we utilize maximum caliber, a dynamical inference principle, to build a minimal yet general model of the collective (mean field) dynamics of large populations of neurons. In agreement with previous experimental observations, we describe a simple, testable mechanism, involving only a

We study the uniform convergence to quasi-stationarity of multidimensional processes absorbed when one of the coordinates vanishes. Our results cover competitive or weakly cooperative Lotka–Volterra birth and death processes and Feller diffusions with competitive Lotka–Volterra interaction. To this aim, we develop an original non-linear Lyapunov criterion involving two functions, which applies to general

We study the Fleming–Viot particle process formed by N interacting continuous-time asymmetric random walks on the cycle graph, with uniform killing. We show that this model has a remarkable exact solvability, despite the fact that it is non-reversible with non-explicit invariant distribution. Our main results include quantitative propagation of chaos and exponential ergodicity with explicit constants

Let X be a BMO martingale with continuous paths and let 2≤q≤p<∞ be given parameters. The paper contains the proof of the Lorentz-norm inequality ‖X∞‖p,q≤2−1∕pp∕q(q+1)∕qΓ(q+1)1∕q‖X‖BMO,and the constant is shown to be the best possible.

This paper considers the problem of robust angle of arrival (AOA) source localization in the presence of outliers by using sparsity regularization. Firstly, the adaptive regularization (AR) and group-based regularization (GR) are respectively developed based on the cluster information of intersections of pairwise bearing lines to handle outliers. However, the estimated source position based on pseudolinear

A novel blind source separation (BSS) method for time-delayed mixtures in underdetermined case is studied in this paper. The proposed method not only addresses the problem of source separation with limited sensors but also avoids the influence of propagation delay. Firstly, the sparse domain is converted by utilizing the spectrum of observed signals to perform modulus operation in time-frequency (TF)

The statistical modeling of multivariate count data observed on a space–time lattice has generally focused on using a hierarchical modeling approach where space–time correlation structure is placed on a continuous, latent, process. The count distribution is then assumed to be conditionally independent given the latent process. However, in many real-world applications, especially in the modeling of

A multistage variable selection method is introduced for detecting association signals in structured brain‐wide and genome‐wide association studies (brain‐GWAS). Compared to conventional methods that link one voxel to one single nucleotide polymorphism (SNP), our approach is more efficient and powerful in selecting the important signals by integrating anatomic and gene grouping structures in the brain