Generalized additive models (GAMs) are a well-established statistical tool for modeling complex nonlinear relationships between covariates and a response assumed to have a conditional distribution in the exponential family. To make inference in this model class, a fast and flexible approach is considered based on Bayesian P-splines and the Laplace approximation. The proposed Laplace-P-spline model

In many complex systems studied in statistical physics, inter-arrival times between events such as solar flares, trades and neuron voltages follow a heavy-tailed distribution. The set of event times is fractal-like, being dense in some time windows and empty in others, a phenomenon which has been dubbed “bursty”. A new model for the inter-exceedance times of such events above high thresholds is proposed

Latent block models are used for probabilistic biclustering, which is shown to be an effective method for analyzing various relational data sets. However, there has been no statistical test method for determining the row and column cluster numbers of latent block models. Recent studies have constructed statistical-test-based methods for stochastic block models, which assume that the observed matrix

The incident/dynamic time-dependent AUC (Area Under the ROC Curve) is an appealing measure to express the discriminative value of a dynamic survival model. However, estimation of this measure is not straightforward. Four recently proposed estimation approaches are studied. In an extensive simulation study, a head-to-head comparison between these four estimation methods is made. The estimation algorithms

Wind loads on structures are commonly described as stationary phenomena that occur in neutral atmospheric conditions at the synoptic scale with velocity profiles in equilibrium with the atmospheric boundary layer. Nevertheless, structural systems can be also affected by thunderstorm outflows, which are non-stationary local phenomena at the mesoscale that occur in convective conditions with totally

The projection median as introduced by Durocher and Kirkpatrick (2005); Durocher and Kirkpatrick (2009) is a robust multivariate, nonparametric location estimator. It is a weighted average of points in a sample, where each point’s weight is proportional to the fraction of directions in which that point is a univariate median. The projection median has the highest possible asymptotic breakdown and is

The asymptotic behavior of the distribution of the squared singular values of the sample autocovariance matrix between the past and the future of a high-dimensional complex Gaussian uncorrelated sequence is studied. Using Gaussian tools, it is established that the distribution behaves as a deterministic probability measure whose support 𝒮 is characterized. It is also established that the squared singular

A new approach to model and compute the metric tensor corresponding to a-priori prescribed surface and volume boundary layers is presented. This is achieved by the use of sources that model the combined anisotropic metric. Algorithms are presented to both evaluate the resulting metric value at any point in space and the efficient approximation of the minimum and maximum metric over an axis-aligned

We develop an energy-stable scheme for simulating fluid-particle interaction problems governed by a coupled system consisting of the incompressible Navier-Stokes (NS) equations defined in a time-dependent fluid domain and Newton's second law for particle motion. A modified temporary arbitrary Lagrangian-Eulerian (tALE) method is designed based on a bijective mapping between the fluid regions at different

In this paper we present a mathematical and numerical model for human cardiac perfusion which accounts for the different length scales of the vessels in the coronary tree. Epicardial vessels are represented with fully three-dimensional (3D) fluid-dynamics, whereas intramural vessels are modeled as a multi-compartment porous medium. The coupling of these models takes place through interface conditions

In this study we have developed a flexible and efficient numerical scheme for the simulation of three–dimensional incompressible flows in spherical coordinates. The main idea, inspired by a similar strategy as [1] for cylindrical coordinates, consists of a change of variables combined with a discretization on a staggered mesh and the special treatment of few discrete terms that remove the singularities

We propose an efficient solver for saddle point problems arising from finite element approximations of nonlocal multi-phase Allen–Cahn variational inequalities. The solver is seen to behave mesh independently and to have only a very mild dependence on the number of phase field variables. In addition we prove convergence, in three GMRES iterations, of the approximation of the two phase problem, regardless

By adopting an identification strategy of difference‐in‐difference estimation combined with propensity score matching between liberalized and closed countries, this paper provides robust evidence that opening the capital account is associated with an increase in income inequality in developing countries. Specifically, capital account liberalization, in the long run, is associated with a reduction in

The brain may be considered as a synchronized dynamic network with several coherent dynamical units. However, concerns remain whether synchronizability is a stable state in the brain networks. If so, which index can best reveal the synchronizability in brain networks? To answer these questions, we tested the application of the spectral graph theory and the Shannon entropy as alternative approaches

In this study, we integrated neural encoding and decoding into a unified framework for spatial information processing in the brain. Specifically, the neural representations of self-location in the hippocampus (HPC) and entorhinal cortex (EC) play crucial roles in spatial navigation. Intriguingly, these neural representations in these neighboring brain areas show stark differences. Whereas the place

Hierarchical sparse coding (HSC) is a powerful model to efficiently represent multidimensional, structured data such as images. The simplest solution to solve this computationally hard problem is to decompose it into independent layer-wise subproblems. However, neuroscientific evidence would suggest interconnecting these subproblems as in predictive coding (PC) theory, which adds top-down connections

Recent remarkable advances in experimental techniques have provided a background for inferring neuronal couplings from point process data that include a great number of neurons. Here, we propose a systematic procedure for pre- and postprocessing generic point process data in an objective manner to handle data in the framework of a binary simple statistical model, the Ising or generalized McCulloch–Pitts

Measuring functional connectivity from fMRI recordings is important in understanding processing in cortical networks. However, because the brain's connection pattern is complex, currently used methods are prone to producing false functional connections. We introduce differential covariance analysis, a new method that uses derivatives of the signal for estimating functional connectivity. We generated

According to the neuromuscular model of virtual trajectory control, the postures and movements of limbs are performed by shifting the equilibrium positions determined by agonist and antagonist muscle activities. In this study, we develop virtual trajectory control for the reaching movements of a multi-joint arm, introducing a proportional-derivative feedback control scheme. In virtual trajectory control

Marked point process models have recently been used to capture the coding properties of neural populations from multiunit electrophysiological recordings without spike sorting. These clusterless models have been shown in some instances to better describe the firing properties of neural populations than collections of receptive field models for sorted neurons and to lead to better decoding results.

A complex-valued Hopfield neural network (CHNN) with a multistate activation function is a multistate model of neural associative memory. The weight parameters need a lot of memory resources. Twin-multistate activation functions were introduced to quaternion- and bicomplex-valued Hopfield neural networks. Since their architectures are much more complicated than that of CHNN, the architecture should

Spiking neural networks (SNNs) with the event-driven manner of transmitting spikes consume ultra-low power on neuromorphic chips. However, training deep SNNs is still challenging compared to convolutional neural networks (CNNs). The SNN training algorithms have not achieved the same performance as CNNs. In this letter, we aim to understand the intrinsic limitations of SNN training to design better

The cerebellum is known to have an important role in sensing and execution of precise time intervals, but the mechanism by which arbitrary time intervals can be recognized and replicated with high precision is unknown. We propose a computational model in which precise time intervals can be identified from the pattern of individual spike activity in a population of parallel fibers in the cerebellar

A reflex is a simple closed-loop control approach that tries to minimize an error but fails to do so because it will always react too late. An adaptive algorithm can use this error to learn a forward model with the help of predictive cues. For example, a driver learns to improve steering by looking ahead to avoid steering in the last minute. In order to process complex cues such as the road ahead,

This letter proves that a ReLU network can approximate any continuous function with arbitrary precision by means of piecewise linear or constant approximations. For univariate function f(x), we use the composite of ReLUs to produce a line segment; all of the subnetworks of line segments comprise a ReLU network, which is a piecewise linear approximation to f(x). For multivariate function f(x), ReLU

This letter considers a class of biologically plausible cost functions for neural networks, where the same cost function is minimized by both neural activity and plasticity. We show that such cost functions can be cast as a variational bound on model evidence under an implicit generative model. Using generative models based on partially observed Markov decision processes (POMDP), we show that neural

For high dimensional data, the standard empirical estimator for the covariance matrix is very poor, and thus many methods have been proposed to more accurately estimate the covariance structure of high dimensional data. In this article, we consider estimation under the assumption of sparsity, but regularize with respect to the individual false positive rate for incorrectly including a matrix entry

Expectile is a generalization of the expected value in probability and statistics. In finance and risk management, the expectile is considered to be an important risk measure due to its connection with gain‐loss ratio and its coherent and elicitable properties. Linear multiple expectile regression was proposed in 1987 for estimating the conditional expectiles of a response given a set of covariates

Finding independent sets of maximum size in fixed graphs is well known to be an NP-hard task. Using scaling limits, we characterise the asymptotics of sequential degree-greedy explorations and provide sufficient conditions for this algorithm to find an independent set of asymptotically optimal size in large sparse random graphs with given degree sequences. In the special case of sparse Erdös–Rényi

We consider a class of perpetuities which admit direct characterization of asymptotics of the key truncated moment. The class contains perpetuities without polynomial decay of tail probabilities thus not satisfying Kesten’s theorem. We show how to apply this result in deriving a new weak law of large numbers for solutions to stochastic recurrence equations and a new central limit theorem for GARCH(1

We analyze the empirical spectral distribution of random periodic band matrices with correlated entries. The correlation structure we study was first introduced in Hochstättler et al. (2015) by Hochstättler, Kirsch and Warzel, who named their setup almost uncorrelated and showed convergence to the semicircle distribution in probability. We strengthen their results which turn out to be also valid almost

We study the following interacting particle system. There are ρn particles, ρ<1, moving clockwise (“right”), in discrete time, on n sites arranged in a circle. Each site may contain at most one particle. At each time, a particle may move to the right-neighbor site according to the following rules. If its right-neighbor site is occupied by another particle, the particle does not move. If the particle

In this paper, we study properties of the stationary harmonic measure which are unique to the stationary case. We prove that any subset with an appropriate sub-linear horizontal growth has a non-zero stationary harmonic measure. On the other hand, we show that any subset with at least linear horizontal growth will have a 0 stationary harmonic measure at every point. This result is fundamental to any

We consider a system of d non-linear stochastic fractional heat equations in spatial dimension 1 driven by multiplicative d-dimensional space–time white noise. We establish a sharp Gaussian-type upper bound on the two-point probability density function of (u(s,y),u(t,x)). From this result, we deduce optimal lower bounds on hitting probabilities of the process {u(t,x):(t,x)∈[0,∞[×R} in the non-Gaussian

This paper analyzes a class of infinite-time-horizon stochastic games with singular controls motivated from the partially reversible problem. It provides an explicit solution for the mean-field game (MFG), and presents sensitivity analysis to compare the solution for the MFG with that for the single-agent control problem. It shows that in the MFG, model parameters not only affect the optimal strategies

Let Xk1k=1∞,Xk2k=1∞,…,Xkdk=1∞ be d independent sequences of Bernoulli random variables with success-parameters p1,p2,…,pd respectively, where d≥2 is a positive integer, and 0

We establish the existence and uniqueness of global (in time) positive strong solutions for a generalized population dynamics equation with environmental noise, while the global existence fails for the deterministic equation. Particularly, we prove the global existence of positive strong solutions for the following stochastic differential equation dXt=(θXtm0+kXtm)dt+εXtm+12φ(Xt)dWt,t>0,Xt>0,m>m0⩾1

The Frisch–Waugh–Lovell Theorem states the equivalence of the coefficients from the full and partial regressions. I further show the equivalence between various standard errors. Applying the new result to stratified experiments reveals the discrepancy between model-based and design-based standard errors.

We prove some analogues of the Skitovich-Darmois, Heyde characterization theorems and other theorems of mathematical statistics for Q-independent random variables with values in a Banach space.

In recent years, several mixtures of skew factor analyzers have been proposed. These models adopt various skew distributions for either the factors or the errors, but not both. This paper examines the connections between these formulations and introduces a unified model that allows for skewness in both the factors and errors.

Fitting an ARMA‐GARCH model has become a common practice in financial econometrics. Because the asymptotic normality of the quasi maximum likelihood estimation (QMLE) requires finite fourth moment for both errors and the sequence itself, self‐weighted quasi maximum exponential likelihood estimation (SWQMELE) has been proposed to reduce the moment constraints but requires the errors to have zero median

In machine-type communications (MTC), a large number of devices are connected to an access point (AP), but only a few devices are active at a time. This sparse activity of devices makes compressed sensing (CS) technique a possible solution for joint activity detection and channel estimation problem in MTC. In this paper, we improve the performance of greedy CS based detection in MTC, by exploiting

Linear zero forcing (ZF) precoding can obtain near-optimal performance as the number of base station (BS) antennas increases, but involve complex matrix inversion calculations whose computational complexity is cubic with respect to the number of user equips (UEs) in massive multiple-input multiple-output (MIMO) systems. In this paper, a low-complexity modified successive over-relaxation-based ZF (MSOR-ZF)

We present a multivariate occupancy model to simultaneously model the presence/absence of multiple species, and demonstrate its use with a goal of estimating parameters related to occupancy. The proposed model accounts for both spatial and temporal dependence within each species, as well as dependence across all species. These dependencies are addressed through random effects, defined so there is no

Teleconnection, the strong dependence between two distant locations, provides interesting information for discovering the structures in spatial data. While teleconnections are often sparse and estimated through sample correlations, there are also abundant correlations among nearby locations. We propose a far-near covariance model that simultaneously models the abundant short-distance dependencies and

Data depth is a well-known and useful nonparametric tool for analyzing functional data. It provides a novel way of ranking a sample of curves from the center outwards and defining robust statistics, such as the median or trimmed means. It has also been used as a building block for functional outlier detection methods and classification. Several notions of depth for functional data were introduced in

This paper explores the effect of measurement errors on a joint monitoring control chart by using three different techniques (i) covariate method (ii) multiple measurements (iii) linear increasing variance. The joint monitoring control chart in the presence of measurement errors are discussed by using an exponentially weighted moving average statistic in the generalized likelihood ratio (GLR) test

Testing for differences between two groups is among the most frequently carried out statistical methods in empirical research. The traditional frequentist approach is to make use of null hypothesis significance tests which use p values to reject a null hypothesis. Recently, a lot of research has emerged which proposes Bayesian versions of the most common parametric and nonparametric frequentist two-sample

Many university rankings have been proposed in recent decades. The remarkable divergence among various rankings leads to confusion for decision-makers. In this paper, we propose to generate a comprehensive world university ranking by aggregating existing individual university rankings. We present a new graph-based rank aggregation method by defining a competition graph of universities, in which each

Monte Carlo experiments produce samples in order to estimate features such as means and quantiles of a given distribution. However, simultaneous estimation of means and quantiles has received little attention. In this setting we establish a multivariate central limit theorem for any finite combination of sample means and quantiles under the assumption of a strongly mixing process, which includes the

In this paper, we investigate robust parameter estimation and variable selection for binary regression models with grouped data. We investigate estimation procedures based on the minimum-distance approach. In particular, we employ minimum Hellinger and minimum symmetric chi-squared distances criteria and propose regularized minimum-distance estimators. These estimators appear to possess a certain degree

Joint modeling of associated mixed biomarkers in longitudinal studies leads to a better clinical decision by improving the efficiency of parameter estimates. In many clinical studies, the observed time for two biomarkers may not be equivalent and one of the longitudinal responses may have recorded in a longer time than the other one. In addition, the response variables may have different missing patterns

There have been numerous tests proposed to determine whether or not the exponential model is suitable for a given data set. In this article, we propose a new test statistic based on spacings to test whether the general progressive Type-II censored samples are from exponential distribution. The null distribution of the test statistic is discussed and it could be approximated by the standard normal distribution

A reinforcement algorithm introduced by Simon (Biometrika 42(3/4):425–440, 1955) produces a sequence of uniform random variables with long range memory as follows. At each step, with a fixed probability \(p\in (0,1)\), \({\hat{U}}_{n+1}\) is sampled uniformly from \({\hat{U}}_1, \ldots , {\hat{U}}_n\), and with complementary probability \(1-p\), \({\hat{U}}_{n+1}\) is a new independent uniform variable

Nearest-neighbour methods based on first differences are an approach to spatial analysis of field trials with a long history, going back to the early work by Papadakis first published in 1937. These methods are closely related to a geostatistical model that assumes spatial covariance to be a linear function of distance. Recently, P-splines have been proposed as a flexible alternative to spatial analysis

Recently developed accelerometer devices have been used in large epidemiological studies for continuous and objective monitoring of physical activities. Typically, physical movements are summarized as minutes in light, moderate, and vigorous physical activities in each wearing day. Because of preponderance of zeros, zero‐inflated distributions have been used for modeling the daily moderate or higher

In medical diagnostic studies, verification of the true disease status might be partially missing based on results of diagnostic tests and other characteristics of subjects. Because estimates of area under the ROC curve (AUC) based on partially validated subjects are usually biased, it is usually necessary to estimate AUC from a bias‐corrected ROC curve. In this article, various direct estimation methods

A typical challenge facing the design and analysis of immuno‐oncology (IO) trials is the prevalence of nonproportional hazards (NPH) patterns manifested in Kaplan‐Meier curves under time‐to‐event endpoints. The NPH patterns would violate the proportional hazards assumption, and yet conventional design and analysis strategies often ignore such a violation, resulting in underpowered or even falsely negative

Sliced orthogonal arrays are useful combinatorial tools that can be used to construct sliced space-filling designs for planning computer experiments with both quantitative and qualitative input variables. A new class of sliced orthogonal arrays called the sliced orthogonal arrays of parallel-flats type is proposed in this paper to address this practical issue. Specifically, a set of sufficient and

We propose a new Gini correlation to measure dependence between a categorical and numerical variables. Analogous to Pearson R2 in ANOVA model, the Gini correlation is interpreted as the ratio of the between‐group variation and the total variation, but it characterizes independence (zero Gini correlation mutually implies independence). Closely related to the distance correlation, the Gini correlation