1,601 Results Found

The bootstrap method is often used for variance estimation in sample surveys with a stratified multistage sampling design. It is typically implemented by producing a set of bootstrap weights that is made available to users and that accounts for the c...

Multi-stage sampling designs are often used in household surveys because a sampling frame of elements may not be available or for cost considerations when data collection involves face-to-face interviews. In this context, variance estimation is a com...

Heart disease is a serious threat to human health. Accurate prediction is very important for disease prevention and treatment. The purpose of this study is to establish a more suitable prediction model of heart disease. Based on LightGBM, we have dee...

In medical care, it is important to evaluate any new diagnostic test in the form of diagnostic accuracy studies. These new tests are compared to gold standard tests, where the performance of binary diagnostic tests is usually measured by sensitivity...

Rubin’s variance estimator of the multiple imputation estimator for a domain mean is not asymptotically unbiased. Kim et al. derived the closed-form bias for Rubin’s variance estimator. In addition, they proposed an asymptotically unbiase...

Wind turbine main bearings are critical components in the drivetrain and are characterised by long service life, low failure rates, and limited failure-interval samples, which increases uncertainty in reliability assessment and maintenance decision-m...

We propose a two-sample testing procedure for high-dimensional time series. To obtain the asymptotic distribution of our ${\ell }_{\infty }$-type test statistic under the null hypothesis, we establish high-dimensional central limit theorems (HCLTs) for an...

In finite populations, pseudo-population bootstrap is the sole method preserving the spirit of the original bootstrap performed from iid observations. In spatial sampling, theoretical results about the convergence of bootstrap distributions to the ac...

Optical fiber pre-warning systems (OFPS) based on Φ-OTDR are applied to many different scenarios such as oil and gas pipeline protection. The recognition of fiber vibration signals is one of the most important parts of this system. According to t...

The point and interval estimations for the unknown parameters of an exponentiated half-logistic distribution based on adaptive type II progressive censoring are obtained in this article. At the beginning, the maximum likelihood estimators are derived...

Studies addressing many ecological problems require accurate evaluation of the total population size. In this paper, we revisit a sampling procedure used for the evaluation of the abundance of an invertebrate population from assessment data collected...

Left-truncated and right-censored data are widely used in lifetime experiments, biomedicine, labor economics, and actuarial science. This article discusses how to resolve the problems of statistical inferences on the unknown parameters of the exponen...

Air pollution is one of the big concerns for smart cities. The problem of applying big data analytics to sampling bias in the context of urban air quality is studied in this paper. A nonparametric estimator that incorporates kernel density estimation...

The aim of this study is to obtain the Bayes estimators and the maximum likelihood estimators (MLEs) for the unknown parameters of the Rayleigh–Weibull (RW) distribution based on progressive type-II censored samples. The approximate Bayes estim...

Sustainable forest management depends on reliable compartment-level growing stock volume (GSV) estimates, which inform the prescribed cut. At the University Forest of Pertouli (Greece), four management-plan inventories (1989–2028) span a method...

Acerola (Malpighia emarginata DC.) is a tropical fruit known for its high vitamin C (ascorbic acid) content. This study aimed to determine the optimal sample size (OSS) required to reliably estimate postharvest quality traits in acerola. A total of 5...

Generic parameter calibration for crop growth models is a very important step in model use. However, studies of the effect of sample size and sampling methods on the calibration and validation of genotypic parameters have seldom been conducted. Scien...

Comprehensive knowledge of built-in batteries in waste electrical and electronic equipment (WEEE) is required for sound and save WEEE management. However, representative sampling is challenging due to the constantly changing composition of WEEE flows...

This paper explores the likelihood and Bayesian estimation of the stress–strength reliability parameter ($\mathcal{R}$) based on a lower record ranked set sampling scheme from the generalized Rayleigh distribution. Maximum likelihood and Bayesian estimator...

It is agreed by industry experts that manufacturing processes are evaluated using quantitative indicators of units produced from this process. For example, the $C_{py}$ process capability index is usually unknown and therefore estimated based on a sample...

To evaluate the effectiveness of the low-carbon innovation development of enterprises, this paper proposes a neutral dynamic network cross-efficiency model and introduces the bootstrap sampling method to correct the model. The model categorizes the l...

Although linear regression models are fundamental tools in statistical science, the estimation results can be sensitive to outliers. While several robust methods have been proposed in frequentist frameworks, statistical inference is not necessarily s...

Human gut microbiome recovery estimates depend on when samples are collected, which reference is used and how community differences are measured. We reanalyzed public profiles from three perturbation cohorts and two longitudinal comparison cohorts, t...

In this paper, we study the statistical inference of the generalized inverted exponential distribution with the same scale parameter and various shape parameters based on joint progressively type-II censored data. The expectation maximization (EM) al...

We explore the role of carbon convenience yields in forecasting the probability density of carbon returns. While theory suggests that convenience yields contain forward-looking information, their predictive content for carbon returns—especially...

Land cover change processes are accelerating at the regional to global level. The remote sensing community has developed reliable and robust methods for wall-to-wall mapping of land cover changes; however, land cover changes often occur at rates bel...

Point and interval estimations are taken into account for a progressive first-failure censored left-truncated normal distribution in this paper. First, we derive the estimators for parameters on account of the maximum likelihood principle. Subsequent...

In reliability engineering and life-testing experiments, exact measurements can be costly or destructive, whereas ranking information and upper record observations are often easier to obtain. Upper record ranked set sampling (RRSS) provides an effici...

We develop a simple Quantile Spacing (QS) method for accurate probabilistic estimation of one-dimensional entropy from equiprobable random samples, and compare it with the popular Bin-Counting (BC) and Kernel Density (KD) methods. In contrast to BC,...

A photovoltaic producer must select a day-ahead supply bid and decide whether to submit it. Positive revenue estimates obtained during bid selection may not persist under new price scenarios, motivating an independent assessment of the selected bid....

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...

This paper presents an eight-channel time-interleaved (TI) 2.6 GS/s 8-bit successive approximation register (SAR) analog-to-digital converter (ADC) prototype in a 55-nm complementary metal-oxide-semiconductor (CMOS) process. The channel-selection-emb...

Dry machining of AISI 1045 steel is attractive for sustainable manufacturing but makes it more challenging to control surface roughness R_a. This work investigates dry turning of AISI 1045 using a 2³ factorial design with three center points (11 runs)...

(1) Background: Let the continuous parameter X be a proxy variable for the outcome of an intervention R. Quasi-experimental studies are designed to evaluate the effect of R over X when forming a randomized control group (without the intervention) is...

Adsorption heat transformation (AHT) systems can play a major role in protecting our environment by decreasing the usage of fossil fuels and utilizing natural and alternative working fluids. The adsorption isotherm is the most important feature in ch...

Small public multimodal datasets invite overstated claims that wearable signals classify attention-deficit/hyperactivity disorder (ADHD). The HYPERAKTIV dataset served as a methodological case study: 103 adult psychiatric outpatients, 51 with ADHD an...

One important aspect of construct validity is structural validity. Structural validity refers to the degree to which scores of a psychological test are a reflection of the dimensionality of the construct being measured. A factor analysis, which assum...

We examine the performance of asymptotic inference as well as bootstrap tests for the Alphabeta and Kobus–Miłoś family of inequality indices for ordered response data. We use Monte Carlo experiments to compare the empirical size and statistical...

Naive Bayes (NB) classifiers are widely used for their simplicity, computational efficiency, and interpretability. However, their predictive performance can degrade significantly in real-world settings where the conditional independence assumption is...

Goodness-of-fit testing remains a fundamental problem in statistical inference with broad practical importance. In this paper, we introduce two new goodness-of-fit tests grounded in entropy-based density estimation techniques. The first is a boundary...

We propose a methodology for training neural networks in which ensembles of under-trained neural networks are used to obtain broadly repeatable predictions, and we augment their performance by disrupting their training, with each neural network in th...

This study investigates statistical inference for a heterogeneous competing risks model under an improved adaptive Type-II progressive censoring scheme, which effectively controls testing time while ensuring sufficient failure data. Assuming the late...

Building energy code compliance is the crucial link between the actual energy savings and the efficiency prescribed in energy codes. A research project aiming to identify opportunities to reduce energy consumption in new single-family residential con...

Canopy reflectance sensors are a viable technology to optimize the fertilization management of crops. In this research, canopy reflectance was measured through a passive sensor to evaluate the effects of either crop features (N fertilization, soil mu...

With the development of modern data collection techniques, researchers often encounter high-dimensional data across various research fields. An important problem is to determine whether several groups of these high-dimensional data originate from the...

With the development of the internet and mobile devices, tourists have been able to obtain information and then travel to various places or participate in diverse tourism activities in one travel excursion. This study investigates the patterns of tou...

This study provides an in-depth analysis of the frequency of extreme streamflow in Italy, adopting the innovative perspective of the theory of records, and focusing on record-breaking floods. (i.e., annual maximum series, AMS) observed in Italy betwe...

This paper evaluates bootstrap inference methods for quantile regression panel data models. We propose to construct confidence intervals for the parameters of interest using percentile bootstrap with pairwise resampling. We study three different boot...

Partial least squares structural equations modeling (PLS-SEM) uses sampling bootstrapping to calculate the significance of the model parameter estimates (e.g., path coefficients and outer loadings). However, when data are time series, as in marketing...

When the bootstrap sample size is moderate, bootstrap confidence regions tend to have undercoverage. Improving the coverage is known as calibrating the confidence region. Consider testing $H_0:\mathit{\theta }={\mathit{\theta }}_0$ versus $H_1:\mathit{\theta }\ne {\mathit{\theta }}_0$. We reject $H_{}$...