2,713 Results Found

Expected shortfall (ES) is a tail functional whose estimation precision is governed by the effective tail sample size $n\alpha$ rather than by the nominal calibration size n. The resulting ${\left(n\alpha \right)}^{-1/2}$ information limit is well established, ye...

Selective prediction requires a statistically reliable rule for deciding which model outputs can be returned while controlling the error rate among accepted predictions. In large language model (LLM) applications, uncertainty scores provide useful ra...

We discuss determining a finite sample linear time-invariant (FS-LTI) system’s impulse response function, $h\left[n\right],$ in the frequency domain when the input testing function is a uniform window function with a width of L and the output is limited to...

We consider the recovery of a common denominator in a finite fractional-power law $y_i=p\left(x_i^{1/n_0}\right)+{\epsilon }_i,p\left(t\right)={\displaystyle \sum _{k=0}^d}{a}_k{t}^k,$ from observations with positive abscissae. For every candidate denominator n, the vectors generated by $1,x^{1/n},\dots ,x^{d/n}$...

Poisson mixed-effects models are essential for analyzing repeated count data, relying on latent random effects to account for unobserved heterogeneity and longitudinal dependence. However, the validity of likelihood-based inference in these models is...

Normalizing-flow Markov chain Monte Carlo (MCMC), such as flowMC, augments local moves with a learned global flow proposal; a natural reliability idea is to pool samples from several such samplers. On 6 targets in 20 and 50 dimensions, a 5-member div...

PCHZ-SIS (permutation-centered tie-aware Henze–Zirkler sure independence screening) is a specialized marginal screening method for mixed data containing continuous and ordered–discrete predictors with extensive ties. The method combines t...

An order-up-to level serving a share $\alpha$ of replenishment cycles is a quantile of an unknown demand law, and practice substitutes a normal quantile of the forecast error—an approximation hardest to defend for the intermittent, integer-value...

Accurate onboard velocity updating is essential for UAV strapdown inertial navigation, especially under GNSS-degraded and high-dynamic conditions. Instead of relying only on local Taylor-series cancellation as λ → 0 or directly transferri...

Given the relevance and wide use of the Generative Adversarial (GA) methodology, this paper focuses on finite samples to better understand its benefits and pitfalls. We focus on its finite-sample properties from both statistical and numerical perspec...

The work proposed a reliability demonstration test (RDT) process, which can be employed to determine whether a finite population is accepted or rejected. Bayesian and non-Bayesian approaches were compared in the proposed RDT process, as were lot and...

Computational statistics is a critical skill for professionals in fields such as data science, statistics, and related disciplines. One essential aspect of computational statistics is the ability to simulate random variables from specified probabilit...

This paper proposes a Bayesian RC-frame finite element model updating (FEMU) and damage state estimation approach using the nonlinear acceleration time history based on nested sampling. Numerical RC-frame finite element model (FEM) parameters are sel...

Rehabilitation robots can establish a direct connection between the user’s nerve signals and the robot’s actuators by integrating with the human nervous system. However, uncertainties in these systems limit their performance and accuracy....

Wireline formation testing is an important technique in the exploration and development of oil fields. Not only can real fluid samples be prepared from the formation directly obtained to know exactly whether the oil existed in the formation or not, b...

We consider a Student process based on independent copies of a random variable X. If X is in the domain of attraction of the normal law (DAN), a weighted version of the Student process is known to follow a functional Central Limit Theorem (FCLT). Acc...

Finite-sample bounds on the accuracy of Bhattacharya’s plug-in estimator for Fisher information are derived. These bounds are further improved by introducing a clipping step that allows for better control over the score function. This leads to superi...

Estimation of a finite population mean under systematic random sampling can be affected by skewness and extreme observations in the auxiliary variable. This study develops a new estimator that combines ratio, difference, and exponential transformatio...

Nonprobability sampling has been increasingly used in epidemiologic research, yet direct inference based on such samples is subject to selection bias. Current adjustment methods commonly rely on a reference probability-based survey sample that shares...

This paper derives the statistical properties of a two-step approach to estimating multivariate rotated GARCH-BEKK (RBEKK) models. From the definition of RBEKK, the unconditional covariance matrix is estimated in the first step to rotate the observed...

In studying the asymptotic and finite sample properties of quasi-maximum likelihood (QML) estimators for the spatial linear regression models, much attention has been paid to the spatial lag dependence (SLD) model; little has been given to its compan...

When the inverse finite element method (inverse FEM) is used to reconstruct the deformation field of a multi-element structure with strain measurements, strain measurement errors can lower the reconstruction accuracy of the deformation field. Further...

Time-limited life tests may terminate before both competing failure causes are observed. We derive the finite-sample distributions of ordinary maximum likelihood estimators (MLEs) for two independent exponential risks under combined Type-II generaliz...

Structural Equation Modeling (SEM) is a key framework for analyzing complex economic relationships involving latent variables, mediation effects, and endogeneity, yet the choice between frequentist and Bayesian estimation remains theoretically and pr...

The computational homogenization of hyperelastic solids in the geometrically nonlinear context has yet to be treated with sufficient efficiency in order to allow for real-world applications in true multiscale settings. This problem is addressed by a...

Selective labels create a support failure for prediction along dependent stochastic processes: alert-triggered events are observed, whereas silent periods are usually unlabeled. We model this mechanism as predictable inclusion on a filtered probabili...

Binary segmentation is the classic greedy algorithm which recursively splits a sequential data set by optimizing some loss or likelihood function. Binary segmentation is widely used for changepoint detection in data sets measured over space or time,...

With the development of power switches and processor performance in recent years, the control frequency of inverters has been significantly improved. However, limited by technology and price, the sensor sampling frequency in large-scale industrial ap...

In an infinite-/super-population (SP) setup, regression analysis of longitudinal data, which involves repeated responses and covariates collected from a sample of independent individuals or correlated individuals belonging to a cluster such as a hous...

In reliability analysis, scale-relative reversed hazard aging describes how retrospective failure risk in a lifetime distribution changes across proportional ages. Reversed hazard elasticity and log time curvature characterize its directions and conn...

Financial tail-risk observations are non-exchangeable: serial dependence and regime shifts make their joint law depend on the time ordering, invalidating the exchangeability that standard conformal guarantees assume, and expected shortfall is not eli...

Collision statistics provide a finite-resolution view of information by measuring how often independent samples fall on the same state and form the basis of integer-order Rényi entropies. Here, we use low-order Rényi entropies to charac...

Space-time adaptive processing (STAP) is a fundamental topic in airborne radar applications due to its clutter suppression ability. Reduced-dimension (RD)-STAP can release the requirement of the number of training samples and reduce the computational...

Quantifying geographic variation is crucial for policy evaluation, yet researchers often rely on complex national surveys not designed for sub-national inference. This design-analysis mismatch creates two challenges when decomposing variance across d...

Continuous-variable quantum communication (CVQC) relies on finite-window estimation of phase space moments, making receiver decisions sensitive to finite measurement resolution, calibration uncertainty, and confidence-calibrated tolerances. This pape...

Finite mixtures normal regression (FMNR) models are widely used to investigate the relationship between a response variable and a set of explanatory variables from several unknown latent homogeneous groups. However, the classical EM algorithm and Gib...

Recently, finite rate of innovation methods have been successfully applied to achieve low sampling rates in many areas, such as for ultrasound and radio signals. However, to the best of our knowledge, there are no journal publications applying this t...

Non-probability samples (NPS) are increasingly used in practice because they are relatively inexpensive and often contain the study outcome variable. However, NPS may suffer from selection bias and usually do not provide survey weights, making finite...

Information theoretic estimators for the first-order spatial autoregressive model are introduced, small sample properties are investigated, and the estimator is applied empirically. Monte Carlo experiments are used to compare finite sample performanc...

Many natural and engineered systems evolve under constraints that restrict the set of admissible states. Classical frameworks study invariant sets, viability regions, survival probabilities, and exit-time events, while the explicit treatment of thres...

It has been shown that, despite being consistent and in some cases efficient, maximum pseudo-likelihood (MPL) estimation for copula models overestimates the level of dependence, especially for small samples with a low level of dependence. This is esp...

This work deals with the effect of targeted field investigation on the reliability of axially loaded piles, aiming at an optimal serviceability and ultimate limit state design. This is done in a Random Finite Element Method (RFEM) framework properly...

This paper introduces the concept of targeted field investigation on the reliability of earth-retaining structures in an active state, which is implemented in a random finite element method (RFEM) framework. The open source RFEM software REARTH2D was...

This paper concentrates on the study of logic-based switching adaptive control. Two different cases will be considered. In the first case, the finite time stabilization problem for a class of nonlinear system is studied. Based on the recently develop...

The present paper deals with the practical problem of reducing statistical uncertainty in elastic settlement analysis of shallow foundations by relying on targeted field investigation with the aim of an optimal design. In a targeted field investigati...

In one of their recent works, the authors examined parametrically the effect of targeted field investigation on reducing statistical uncertainty in active state analysis of earth retaining structures based on 2165 different cases for each of the slid...

Various problems in physics and engineering lead to the problem of run- length probability distribution function (pdf) in a finite time series. In this study to find the pdf of run-lengths in such a series, first infinite sequence properties are revi...

Due to the inevitable presence of random defects, unpredictable grain boundaries in macroscopic samples, stress concentration at clamping points, and unknown load distribution in the investigation of graphene sheets, uncertainties are crucial and cha...

The limited length and structural evolution of time series for water-sector energy consumption constrain reliable multi-step forecasting. To address this problem, this research proposed a gate-recycling fractional-order grey model named GRGM(r,k,1),...

This study proposes a flexible family of mean estimators for finite populations under stratified systematic sampling by combining power transformations with an exponential adjustment. The proposed family allows different estimator forms through suita...