A method for dealing with multivariate analysis of marked spatio-temporal point processes is presented by introducing different partial point characteristics, and by extending the spatial dependence graph model formalism. The approach yields a unified framework for different types of spatio-temporal data, including both, purely qualitatively (multivariate) cases and multivariate cases with additional

The bivariate normal multilevel model (MLM) provides a flexible modelling framework for cost- effectiveness analyses (CEAs) alongside cluster randomized trials (CRTs) as well as for sample size calculations of these trials. The bivariate MLM assumes a joint normal distribution for effects and costs, both within (individual level) and between (cluster level) clusters. A typical problem in CEAs is that

Let Nn be an n×n complex random matrix, each of whose entries is an independent copy of a centered complex random variable z with finite nonzero variance σ2. The strong circular law, proved by Tao and Vu, states that almost surely, as n→∞, the empirical spectral distribution of Nn/(σn) converges to the uniform distribution on the unit disc in ℂ. A crucial ingredient in the proof of Tao and Vu, which

Purchase data from retail chains can provide proxy measures of private household expenditure on items that are the most troublesome to collect in the traditional expenditure survey. Due to the inevitable coverage and selection errors, bias must exist in these proxy measures. Moreover, given the sheer amount of data, the bias completely dominates the variance. To investigate the potential of replacing

Let 𝒳 be a random variable of a Riemannian manifold. We assume that the C2‐probability density function of 𝒳 exists. This research addresses two variational questions. The first concerns sets that maximize their probability among those that have a fixed volume. We prove that such a set must have a probability density function that is constant along its boundary; equivalently, such a set must be a

In this paper, we utilize the enriched Galerkin (EG) finite element method for the flow equation in Biot's system, which provides a robust locally conservative flux in heterogeneous porous media. The computational algorithm to solve the coupled system with the permeability alteration is presented with the linearization and Picard's iterative scheme. The block structure is utilized for the linear system

In this paper, we consider a sorting scheme for potentially spurious scattering resonant pairs in one- and two-dimensional electromagnetic problems and in three-dimensional acoustic problems. The novel sorting scheme is based on a Lippmann-Schwinger type of volume integral equation and can, therefore, be applied to structures with graded materials as well as to configurations including piece-wise constant

This work presents efficient solution techniques for radiative transfer in the smoothed particle hydrodynamics discretization. Two choices that impact efficiency are how the material and radiation energy are coupled, which determines the number of iterations needed to converge the emission source, and how the radiation diffusion equation is solved, which must be done in each iteration. The coupled

The radiation hydrodynamics equations for smoothed particle hydrodynamics are derived by operator splitting the radiation and hydrodynamics terms, including necessary terms for material motion, and discretizing each of the sets of equations separately in time and space. The implicit radiative transfer discussed in the first paper of this series is coupled to explicit smoothed particle hydrodynamics

The R package glmm enables likelihood‐based inference for generalized linear mixed models with a canonical link. No other publicly‐available software accurately conducts likelihood‐based inference for generalized linear mixed models with crossed random effects. glmm is able to do so by approximating the likelihood function and two derivatives using importance sampling. The importance sampling distribution

Statistical inference provides the protocols for conducting rigorous science, but data plots provide the opportunity to discover the unexpected. These disparate endeavors are bridged by visual inference, where a lineup protocol can be employed for statistical testing. Human observers are needed to assess the lineups, typically using a crowd‐sourcing service. This paper describes a new approach for

In this article we establish central limit theorems for multilevel Polyak-Ruppert averaged stochastic approximation schemes. We work under very mild technical assumptions and consider the slow regime in which typical errors decay like N−δ with δ∈(0,12) and the critical regime in which errors decay of order N−1∕2logN in the runtime N of the algorithm.

Combining segmentation, classification, and time-fractional diffusion filtering, an excellent smoothing method of peak-preserving is proposed. First, the signal is divided into equal-length segments. Second, these segments are classified according to the similarity. Third, similar segments in the same class are stacked into a two-dimensional array, and then they are filtered by the two-dimensional

The non-linear frequency modulation (NLFM) waveform is one of the existing waveforms that can be used in high range resolution radar applications. However, a high sampling frequency and consequently an expensive ADC are required. To overcome this drawback while taking advantage of the features of the NLFM waveform, we suggest approximating the wideband NLFM waveform by a piecewise linear waveform and

This paper proposes tests for the null of sphericity and identity matrix for nonparametric time-varying coefficient panel data models with fixed effects. Firstly, based on the local linear smoothing technique, the estimators of the unknown coefficient functions and model residuals are obtained. Secondly, proper test statistics are proposed aiming at tests for sphericity or identity matrix with a large

ABSTRACT Baseline covariates in randomized experiments are often used in the estimation of treatment effects, for example, when estimating treatment effects within covariate-defined subgroups. In practice, however, covariate values may be missing for some data subjects. To handle missing values, analysts can use imputation methods to create completed datasets, from which they can estimate treatment

Skip-lot sampling plan is often applied in industries for reducing the cost and effort of the inspection of the product having excellent quality history. Consequence of skip-lot sampling plans is to reduce the cost of inspection so which are more attractive in economical aspect. In this paper, we develop a sampling plan by incorporating the idea of resampling in two-level skip lot sampling plan and

ABSTRACT In recent days, COVID-19 pandemic has affected several people's lives globally and necessitates a massive number of screening tests to detect the existence of the coronavirus. At the same time, the rise of deep learning (DL) concepts helps to effectively develop a COVID-19 diagnosis model to attain maximum detection rate with minimum computation time. This paper presents a new Residual Network

Motivated by the analysis of data from the UK Millennium Cohort Study on emotional and behavioural disorders, we develop an M‐quantile regression model for multivariate longitudinal responses. M‐quantile regression is an appealing alternative to standard regression models; it combines features of quantile and expectile regression and it may produce a detailed picture of the conditional response variable

We consider the task of determining a football player’s ability for a given event type, for example, scoring a goal. We propose an interpretable Bayesian model which is fit using variational inference methods. We implement a Poisson model to capture occurrences of event types, from which we infer player abilities. Our approach also allows the visualisation of differences between players, for a specific

Abstract This paper present E-Bayesian and Bayesian estimators of parameters of Poisson inverse exponential distribution (PIED) under Squared error loss function (SELF), General entropy loss function (GELF) and Linear Exponential loss function (LINEX) for progressive type-II censored data with binomial removals (PT-II CBRs). The E-Bayesian and corresponding Bayesian estimators are compared in terms

Abstract In the literature of model selection, Vuong’s test and Akaike information criterion aim to find the best statistical model. Both of them are related to the expectation of the log-likelihood function of the rival models, but they are not sensitive to the small difference between rival models. The goal of this study is to develop a simple model selection approach which does not assume a true

The Kohayakawa–Nagle–Rödl‐Schacht conjecture roughly states that every sufficiently large locally d‐dense graph G on n vertices must contain at least (1 − o(1))d|E(H )|n|V (H )| copies of a fixed graph H. Despite its important connections to both quasirandomness and Ramsey theory, there are very few examples known to satisfy the conjecture. We provide various new classes of graphs that satisfy the

We give a detailed asymptotic analysis of the profiles of random symmetric digital search trees, which are in close connection with the performance of the search complexity of random queries in such trees. While the expected profiles have been analyzed for several decades, the analysis of the variance turns out to be very difficult and challenging, and requires the combination of several different

In a seminal paper on finding large matchings in sparse random graphs, Karp and Sipser proposed two algorithms for this task. The second algorithm has been intensely studied, but due to technical difficulties, the first algorithm has received less attention. Empirical results by Karp and Sipser suggest that the first algorithm is superior. In this paper we show that this is indeed the case, at least

Abstract Computer models with gradient information are increasingly used in engineering and science. The gradient-enhanced Gaussian process emulator can be used for emulating such models. Because the size of the covariance matrix increases proportionally with the dimension of inputs and the sample size, it is computationally challenging to fit such an emulator for large data sets. We propose a random

Sequential and temporal data arise in many fields of research, such as quantitative finance, medicine, or computer vision. A novel approach for sequential learning, called the signature method and rooted in rough path theory, is considered. Its basic principle is to represent multidimensional paths by a graded feature set of their iterated integrals, called the signature. This approach relies critically

A hypothetical seismic site is constructed for which the probability law of the seismic ground acceleration process X(t) is specified. Since the seismic hazard is known, the performance of the incremental dynamic analysis- (IDA) and multiple stripe analysis- (MSA) based fragilities, which are used extensively in Earthquake Engineering, can be assessed without ambiguity. It is shown that the IDA- and

The fundamental unit of each evolutionary algorithm is the individual. Each individual represents a potential solution to the problem at hand. Despite the importance of individual solution for multi-objective algorithms’ performance the majority of the existing implementations select a simplistic approach by assuming identical behavior for all candidate solutions of a population. However, from the

The data envelopment analysis technique produces higher efficiency scores for the assessed decision making units (DMUs) when more input/output factors are considered. This feature generates an intuitively unreasonable result in which the efficiency of a DMU measured considering the accompanying undesirable outputs is greater than or equal to that measured without considering them. In order to obtain

Transport markets involve interactions between multiple groups of decision-makers, hence they are good candidates to be analyzed through the prism of game theory. The many models and algorithms published to date tend to reflect different academic disciplines, including operations research, economics and engineering approaches. Models published in the operations research oriented journals often involve

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In this paper, we develop an accelerated conservative sharp-interface method for multiphase flows simulations. Traditional multiphase simulation methods use the minimum time step of all fluids obtained according to the CFL conditions to evolve the fluid states, which limits the computational efficiency, as the sound speed of one fluid may be much larger than of others. To address this issue, based

In this paper we introduce constitutive artificial neural networks (CANNs), a novel machine learning architecture for data-driven modeling of the mechanical constitutive behavior of materials. CANNs are able to incorporate by their very design information from three different sources, namely stress-strain data, theoretical knowledge from materials theory, and diverse additional information (e.g., about

When parameter estimation is solved in a high-dimensional space, the dimensionality reduction strategy becomes the primary consideration for alleviating the tremendous computational cost. In the present study, the discrete empirical interpolation method (DEIM) is explored to retrieve the initial condition (IC) by combining the polynomial chaos (PC) based ensemble Kalman filter (i.e. PC-EnKF), where

Research conducted in US schools shows summer learning loss in test scores. If this summer loss is not incorporated into models of student ability growth, assumptions will be violated because fall scores will be overestimated and spring scores will be underestimated, which can be particularly problematic when evaluating teacher or school effectiveness. Statistical methods for summer loss have remained

Robust concentration inequalities of Bernstein type for sub-exponential random variables are proved with respect to probability measures whose densities are connected by open exponential arcs.