A novel test statistic is proposed to identify important predictors for the conditional mean function in regression. The stepwise regression algorithm based on the proposed test statistic guarantees variable selection consistency without specifying the functional form of the conditional mean. When the predictors are ultrahigh dimensional, a model-free screening procedure is introduced to precede the

A prescription is presented for a new and practical correlation coefficient, ϕK, based on several refinements to Pearson’s hypothesis test of independence of two variables. The combined features of ϕK form an advantage over existing coefficients. Primarily, it works consistently between categorical, ordinal and interval variables, in essence by treating each variable as categorical, and can therefore

A review of theoretical concepts and diverse applications of sparse representations and compressive sampling (CS) approaches in engineering mechanics problems is provided from a broad perspective. First, following a presentation of well-established CS concepts and optimization algorithms, attention is directed to currently emerging tools and techniques for enhancing solution sparsity and for exploiting

SummaryWe propose a near-explosive random coefficient autoregressive model (NERC) to obtain predictive probabilities of the apparition and devolution of bubbles. The distribution of the autoregressive coefficient of this model is allowed to be centred at an O(T−α) distance of unity, with α ∈ (0, 1). When the expectation of the autoregressive coefficient lies on the explosive side of unity, the NERC

We establish an invariance principle and a large deviation principle for a biased random walk ${\text{RW}}_\lambda$ with $\lambda\in [0,1)$ on $\mathbb{Z}^d$ . The scaling limit in the invariance principle is not a d-dimensional Brownian motion. For the large deviation principle, its rate function is different from that of a drifted random walk, as may be expected, though the reflected biased random

We propose and analyse a fully adaptive strategy for solving elliptic PDEs with random data in this work. A hierarchical sequence of adaptive mesh refinements for the spatial approximation is combined with adaptive anisotropic sparse Smolyak grids in the stochastic space in such a way as to minimize the computational cost. The novel aspect of our strategy is that the hierarchy of spatial approximations

In this paper, we generalize the exponential energy-preserving integrator proposed in the recent paper [SIAM J. Sci. Comput. 38(2016) A1876-A1895] for conservative systems, which now becomes linearly implicit by further utilizing the idea of the scalar auxiliary variable approach. Comparing with the original exponential energy-preserving integrator which usually leads to a nonlinear algebraic system

The paper describes a flexible computational method that can be used to represent the complex geometric evolution of a fluid-driven fracture front using an unstructured triangular element mesh. A specific motivation for the method is to allow subsequent treatment of non-planar fracture growth problems. Elastic interactions between the crack opening displacements and the surrounding medium are calculated

Let b(x) be the probability that a sum of independent Bernoulli random variables with parameters p1,p2,p3,…∈[0,1) equals x, where λ≔p1+p2+p3+⋯ is finite. We prove two inequalities for the maximum of the density ratio b(x)∕πλ(x), where πλ is the probability mass function of the Poisson distribution with parameter λ.

This paper investigates the problem of testing independence of two random vectors of general dimensions. For this, we give for the first time a distribution-free consistent test. Our approach combines distance covariance with the center-outward ranks and signs developed in Hallin (2017). In technical terms, the proposed test is consistent and distribution-free in the family of multivariate distributions

Although institutional affiliation is not mandatory in scholarly publishing, a new trend of multiple and simultaneous affiliations, which I will call “octopus affiliations” or “octaffiliations” in short, is increasingly noticeable as a distorted consequence of academic ranking and evaluation systems. Institutions offer financial and/or technical supports to authors whose contributions are, in turn

Research on the determinants of scholarly productivity is flourishing, driven both by long-standing curiosity about its wide variation, and by recent concern over race and gender inequalities. Beyond standard structural and demographic determinants of research output, some studies point to the role of individual psychology. We contribute to scholarship on personality and productivity by showing not

China has greatly increased the number of published works indexed in the Web of Science since 1980. This corpus of scholarship allows analysis of the creativity or innovation of that contribution, where creativity is indicated by deep conventionality and high novelty. A test is applied to analyze reference pairs in articles to search for unexpected referencing combinations at the journal–journal level

The COVID-19 pandemic is creating a global health emergency. Mapping this health emergency in scientific publications demands multiple approaches to obtain a picture as complete as possible. To progress in the knowledge of this pandemic and to control its effects, international collaborations between researchers are essentials, as well as having open and immediate access to scientific publications

We consider a linear elliptic system in divergence form with random coefficients and study the random fluctuations of large-scale averages of the field and the flux of the solution operator. In the context of the random conductance model, we developed in a previous work a theory of fluctuations based on the notion of homogenization commutator: we proved that the two-scale expansion of this special

Medical costs are often skewed to the right, heteroscedastic, and having a sophisticated relation with covariates. Moreover, medical cost datasets are always massive, such as in the New York Statewide Planning and Research Cooperative System Expenditure Study. Different observations can depend on each other as the spatial distribution of diseases induces complex correlation among patients coming from

We propose a new symmetric extension of the bivariate Farlie-Gumbel-Morgenstern (FGM) copula with given marginals. The main feature of this new extension is the higher dependence level it permits between random variables. It is shown that the range of Spearman’s Rho is [−.33, 1]. We use this new extension to estimate reliability in a dependent stress-strength model with an application to the Egyptian

In this paper, a model averaging approach is developed for the linear regression models with response missing at random. It is shown that the proposed method is asymptotically optimal in the sense of achieving the lowest possible squared error. A Monte Carlo study is conducted to investigate the finite sample performance of our proposal by comparing with some related methods, and the simulation results

Asymptotic unbiasedness and L2-consistency are established for various statistical estimates of mutual information in the mixed models framework. Such models are important, e.g., for analysis of medical and biological data. The study of the conditional Shannon entropy as well as new results devoted to statistical estimation of the differential Shannon entropy are employed essentially. Theoretical results

After establishing the moderate deviation principle by the classical Azencott method, we prove the Strassen’s compact law of the iterated logarithm (LIL) for a class of stochastic partial differential equations (SPDEs). As an application, we obtain this type of LIL for two population models known as super-Brownian motion and Fleming-Viot process. In addition, the classical LIL is shown for the class

In this article, we describe how to fit panel-data ordered logit models with fixed effects using the new community-contributed command feologit. Fixed-effects models are increasingly popular for estimating causal effects in the social sciences because they flexibly control for unobserved time-invariant heterogeneity. The ordered logit model is the standard model for ordered dependent variables, and

In this article, we present a new command, qcte, that implements several methods for estimation and inference for quantile treatment-effects models with a continuous treatment. We propose a semiparametric two-step estimator, where the first step is based on a flexible Box–Cox model, as the default model of the command. We develop practical statistical inference procedures using bootstrap. We implement

In this article, we present the eqregsel command, which estimates and provides bootstrap inference for sample-selection models via extremal quantile regression. eqregsel estimates a semiparametric sample-selection model without an instrument or a large support regressor and outputs the point estimates of the homogeneous linear coefficients, their bootstrap standard errors, and the p-value for a specification

Coefficient plots are a popular tool for visualizing regression estimates. The appeal of these plots is that they visualize confidence intervals around the estimates and generally center the plot around zero, meaning that any estimate that crosses zero is statistically nonsignificant at least at the alpha level around which the confidence intervals are constructed. For models with statistical significance

markdoc is a general-purpose literate programming package for generating dynamic documents, dynamic presentation slides, Stata help files, and package vignettes in various formats. In this article, I introduce markdoc version 5.0, which performs independently of any third-party software, using the mini engine. The mini engine is a lightweight alternative to Pandoc (MacFarlane [2006, https://pandoc

Cluster randomized trials, where clusters (for example, schools or clinics) are randomized to comparison arms but measurements are taken on individuals, are commonly used to evaluate interventions in public health, education, and the social sciences. Analysis is often conducted on individual-level outcomes, and such analysis methods must consider that outcomes for members of the same cluster tend to

In this article, we describe how to test for the presence of measurement error in explanatory variables. First, we discuss the test of such hypotheses in parametric models such as linear regressions and then introduce a new command, dgmtest, for a nonparametric test proposed in Wilhelm (2018, Working Paper CWP45/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies). To illustrate

In this article, I describe the lclogit2 command, an enhanced version of lclogit (Pacifico and Yoo, 2013, Stata Journal 13: 625–639). Like its predecessor, lclogit2 uses the expectation-maximization algorithm to fit latent class conditional logit (LCL) models. But it executes the expectation-maximization algorithm’s core algebraic operations in Mata, so it runs considerably faster as a result. It also

Vector autoregression (VAR) estimation is a vital tool in economic studies. VARs, however, can be dimensionally cumbersome and overparameterized. The vgets command allows for a general-to-specific estimation of VARs— overcoming the potential overparameterization—and provides tests for Granger causality, estimates of the long-run effects, and the cumulative impulse–response of each variable in the system;

Self-reported survey data are often plagued by the presence of heaping. Accounting for this measurement error is crucial for the identification and consistent estimation of the underlying model (parameters) from such data. In this article, we introduce two commands. The first command, heapmph, estimates the parameters of a discrete-time mixed proportional hazard model with gammaunobserved heterogeneity

In this article, we introduce the commands twexp and twgravity, which implement the estimators developed in Jochmans (2017, Review of Economics and Statistics 99: 478–485) for exponential regression models with two-way fixed effects. twexp is applicable to generic n × m panel data. twgravity is written for the special case where the dataset is a cross-section on dyadic interactions between n agents

A previous column (Cox, 2009, Stata Journal 9: 137–157) gave a review of methods for working rowwise in Stata. Here rows means observations in a dataset, and the concern is calculations in each observation with a bundle of variables. For example, a row mean variable can be generated as the mean of some numeric variables in each observation. This column is an update. It is briefly flagged that official

dm0048_4: Finding variables. N. J. Cox. Stata Journal 15: 605; 12: 167; 10: 691; 10: 281–296.

Date: Thursday, July 30, and Friday, July 31, 2020

Theory of Probability &Its Applications, Volume 65, Issue 1, Page 175-178, January 2020. Obituary of outstanding mathematician Vladimir Mikhailovich Zolotarev, who passed away November 7, 2019. He is remembered as a supreme master of both modern and classical techniques of analysis who enriched the store of probability theory through his contributions of novel analytical methods and tools.

Theory of Probability &Its Applications, Volume 65, Issue 1, Page 173-174, January 2020. An obituary of Dmitrii Mikhailovich Chibisov, the former deputy editor-in-chief of Theory of Probability and Its Applications, who passed away on November 20, 2019.

Theory of Probability &Its Applications, Volume 65, Issue 1, Page 121-172, January 2020. This paper presents abstracts of the talks presented at The Fourth International Conference on Stochastic Methods (ICSM-4), which was held June 2--9, 2019 at Divnomorskoe (near the town of Gelendzhik) at the Raduga sports and fitness center of the Don State Technical University. Participants included many leading

Theory of Probability &Its Applications, Volume 65, Issue 1, Page 114-120, January 2020. We consider Boolean functions $f$ defined on Boolean cube $\{-1,1\}^n$ of half-spaces, i.e., functions of the form $f(x)=\operatorname{sign}(\omega\cdot x-\theta)$. Half-space functions are often called linear threshold functions. We assume that the Boolean cube $\{-1,1\}^n$ is equipped with a uniform measure.

Theory of Probability &Its Applications, Volume 65, Issue 1, Page 111-113, January 2020. We study the form of excursions of a Gaussian stationary process intersecting a high level $u$. We show that the trajectories fluctuate in this case in a narrow tube around the expected motion. We also find an upper bound for the probability that the trajectory intersects the boundary of this tube as $u$ goes off

Theory of Probability &Its Applications, Volume 65, Issue 1, Page 102-110, January 2020. We consider a stationary linear $\operatorname{AR}(p)$-model with observations subject to gross errors (outliers). The autoregression parameters and the distribution of innovations are unknown. Based on the residuals from the parameter estimators, we construct an analogue of an empirical distribution function and

Theory of Probability &Its Applications, Volume 65, Issue 1, Page 82-101, January 2020. We derive sufficient conditions for the differentiability of all orders for the flow of stochastic differential equations with jumps and prove related $L^p$-integrability results for all orders. Our results extend similar results obtained by H. Kunita [Stochastic differential equations based on Lévy processes and

Theory of Probability &Its Applications, Volume 65, Issue 1, Page 62-81, January 2020. We explore the asymptotic behavior of densities of sums of independent random variables that are convoluted with a small continuous noise.

Theory of Probability &Its Applications, Volume 65, Issue 1, Page 49-61, January 2020. We consider the problem of constructing guarantee procedures of statistical inference with fixed minimal observation number $n^*$ for discrimination of two hypotheses $H_0\colon\theta\in[\theta_1,\theta_2]$ and $H_1\colon\theta\notin[\theta_1,\theta_2]$ with a one-dimensional parameter $\theta$ under the so-called

Theory of Probability &Its Applications, Volume 65, Issue 1, Page 32-48, January 2020. Sample statistics of samples from a fractional Brownian motion with Hurst exponent $H$, and in particular, autocovariance statistics, are considered. Two statistics characterizing the covariate dependence between the increments of this process are studied; in particular, their asymptotic properties and the limit

Theory of Probability &Its Applications, Volume 65, Issue 1, Page 17-31, January 2020. We examine the asymptotic behavior of integral quadratic functionals defined on time-varying Ornstein--Uhlenbeck processes. We find an upper function that majorizes with probability $1$ the deviation of the integral from its expected value as time increases. The results obtained are applied to evaluate the control

Theory of Probability &Its Applications, Volume 65, Issue 1, Page 1-16, January 2020. We consider statistics of the form $T =\sum_{j=1}^n \xi_{j} f_{j}+ \mathcal R $, where $\xi_j, f_j$, $j=1, \dots, n$, and $\mathcal R$ are $\mathfrak M$-measurable random variables for some $\sigma$-algebra $ \mathfrak M$. Assume that there exist $\sigma$-algebras $\mathfrak M^{(1)}, \dots, \mathfrak M^{(n)}$, $ \mathfrak

Nonlinear fluid viscous dampers have been widely used in energy-dissipating structures due to their stable and high dissipation capacity and low maintenance cost. However, the literature on stochastic optimization of nonlinear viscous dampers under nonstationary excitations is limited. This paper is devoted to the stochastic response and sensitivity analysis of large-scale energy-dissipating structures

We consider the multivariate independence testing problem between pairs of random vectors for high-dimensional data and develop three high-dimensional nonparametric independence tests based on spatial sign and spatial rank, which have greater power than many existing popular tests, especially for heavy-tailed distributions. Under the elliptically symmetric distributions, which are much more general

We study a noisy graph isomorphism problem, where the goal is to perfectly recover the vertex correspondence between two edge‐correlated graphs, with an initial seed set of correctly matched vertex pairs revealed as side information. We show that it is possible to achieve the information‐theoretic limit of graph sparsity in time polynomial in the number of vertices n . Moreover, we show the number

A new computational strategy is proposed for determining all elastic scatterer characteristics including the shape, the material properties (Lamé coefficients and density), and the location from the knowledge of far-field pattern (FFP) measurements. The proposed numerical approach is a multi-stage procedure in which a carefully designed regularized iterative method plays a central role. The adopted

We review and extend the list of stability and convergence properties satisfied by Runge-Kutta (RK) methods that are associated with Summation-By-Parts (SBP) operators, herein called RK-SBP methods. The analysis covers classical, generalized as well as upwind SBP operators. Previous work on the topic has relied predominantly on energy estimates. In contrast, we derive all results using a purely algebraic

In this work, we propose a unified gas kinetic particle (UGKP) method for solving the nonlinear thermal radiative transfer equations. The UGKP method is a multiscale method in that the macro and microscopic variables are coupled and updated in a consistent way. It employs a finite volume formulation for the macroscopic variable evolution, and a particle-based Monte Carlo solver for tracking the non-equilibrium

In the literature on nonlinear, projection-based model order reduction for computational fluid dynamics problems, it is often claimed that due to modal truncation, a projection-based reduced-order model (PROM) does not resolve the dissipative regime of the turbulent energy cascade and therefore is numerically unstable. Efforts at addressing this claim have ranged from attempting to model the effects

In a discrete time and multiple-priors setting, we propose a new characterisation of the condition of quasi-sure no-arbitrage which has become a standard assumption. We show that it is equivalent to the existence of a subclass of priors having the same polar sets as the initial class and such that the uni-prior no-arbitrage holds true for all priors in this subset. This characterisation shows that

We propose a new extended regression model based on the logarithm of the generalized odd log-logistic Weibull distribution with four systematic components for the analysis of survival data. This regression model can be very useful and could give more realistic fits than other special regression models. We obtain the maximum likelihood estimates of the model parameters for censored data and address

We investigate a framework for improving predictions from models for spatio-temporal data. The framework is based on minimising the mean squared prediction error and can be applied to many models. We applied the framework to a model for monthly rainfall data in the Murray-Darling Basin in Australia. Across a range of prediction situations, we improved the predictive accuracy compared to predictions

It is well known that the diversity profile provides a complete picture about the evenness of the relative abundance distribution of an ecological population. This complexity measure is a continuous function evaluated on a suitable grid of values x ≥ 0 that determine the measure’s sensitivity to the most dominant species. In this paper, a functional design‐based estimation of diversity profiles is

In the face of increased competition and globalization, supply chain management has become a crucial aspect of securing competitive advantage. Within this, the quality offered by suppliers is vital. Thus, in this paper, we develop a model to select the best supplier based on process quality from the perspective of the buyer. We first employ the process capability index S p k , which can provide an

In this paper, we suggest a new efficient method, that yields two new point predictors, for predicting future order statistics, when the sample size remains fixed and when it is enlarged. The two suggested point predictors are constructed based on some characterization properties of order statistics via their distributions. A comprehensive simulation study is conducted on several distributions, among

Collinearity discovery through diagnostic tools is an important analysis step when performing linear regression. Despite their wide-spread use, collinearity indices such as the variance inflation factor and the condition number have limitations and may not be effective in some applications. In this article we will contribute to the study of conventional collinearity indices through theoretical and