27 papers · ranked by Valyu relevance
Hameed Ali, Syed Muhammad Asim, Muhammad Ijaz, Tolga Zaman + 1 more
'Soofia Iftikhar'] This paper offers a novel approach to formulate efficient ratio estimator of the population variance using a transformed auxiliary variable. The impact of transformation on auxiliary information has also been discussed. It is observed that incorporating a transformed auxiliary variable result in a…
Mengyin Lu, Matthew Stephens
We consider the problem of estimating variances on a large number of “similar” units, when there are relatively few observations on each unit. This problem is important in genomics, for example, where it is often desired to estimate variances for thousands of genes (or some other genomic unit) from just a few…
Dai Akita
Standard practice obtains an unbiased variance estimator by dividing by N − 1 rather than N. Yet if only half the data are used to compute the mean, dividing by N can still yield an unbiased estimator. We show that an alternative mean estimator Xˆ = PcnXn can produce such an unbiased variance estimator with denominator…
Felix Reichel
Bessel's correction adjusts the denominator in the sample variance formula from n to n − 1 to ensure an unbiased estimator of the population variance. This paper provides rigorous algebraic derivations, geometric interpretations, and visualizations to reinforce the necessity of this correction. It further introduces…
Sohaib Ahmad, Nitesh Kumar Adichwal, Muhammad Aamir, Javid Shabbir + 3 more
'Najwan Alsadat' 'Mohammed Elgarhy' 'Hijaz Ahmad'] In this article, we have suggested a new improved estimator for estimation of finite population variance under simple random sampling. We use two auxiliary variables to improve the efficiency of estimator. The numerical expressions for the bias and mean square error…
Jayant Singh, Viplav K. Singh, Sachin Malik, Rajesh Singh
This paper proposes a class of ratio type estimators of finite population variance, when the population variance of an auxiliary character is known. Asymptotic expression for mean square error (MSE) is derived and compared with the mean square errors of some existing estimators. An empirical study is carried out to…
Mohammed Ahmed Alomair, Syed Aflake Hussain Shah Gardazi
This study is completed for the estimation of unknown population variance for the variable of mean and variance of interest. To accomplish this task, a new generalized class of robust kind of variance estimators proposed utilizing known descriptives of auxiliary variable, for example, Mid-range, Hodges-Lehmann Mean…
Sohaib Ahmad, Sardar Hussain, Kalim Ullah, Erum Zahid + 6 more
'Muhammad Aamir' 'Javid Shabbir' 'Zubair Ahmad' 'Huda M. Alshanbari' 'Wejdan Alajlan' 'Anoop Kumar'] In this article, we proposed an improved finite population variance estimator based on simple random sampling using dual auxiliary information. Mathematical expressions of the proposed and existing estimators are…
Santosh Kumar Chaudhary, Nitin Gupta
In many statistical studies, the measure of uncertainties like entropy, extropy, varentropy and varextropy of a distribution function is of prime interest. This paper proposes estimators of extropy and varextropy. Proposed estimators are consistent. Based on extropy estimator, a test of symmetry is given. The proposed…
Ning Hao, Yue Niu, Xiao Han
The variance of noise plays an important role in many change-point detection procedures and the associated inferences. Most commonly used variance estimators require strong assumptions on the true mean structure or normality of the error distribution, which may not hold in applications. More importantly, the qualities…
E. G. Cooch, D. I. MacKenzie, J. A. Royle
Data augmentation is now a standard device across capture–recapture and occupancy analysis: adding a fixed number M of all-zero encounter histories replaces a model of unknown dimension with one of fixed dimension. Although M is often treated as a computational tuning choice, it also specifies a finite superpopulation…
Daniel Wallach, Taru Palosuo, Henrike Mielenz, Samuel Buis + 31 more
Crop phenology has a major influence on crop yield and is a major aspect of crop response to global warming. Process-based models of phenology are often used to predict the effect of weather on the development rate of crops through their growth phases, but such models are associated with large uncertainties, as…
Eunice J. Kim, Zhengyuan Zhu
Many spatial processes exhibit nonstationary features. We estimate a variance function from a single process observation where the errors are nonstationary and correlated. We propose a difference-based approach for a one-dimensional nonstationary process and develop a bandwidth selection method for smoothing, taking…
Manussaya La-ongkaew, Sa-Aat Niwitpong, Suparat Niwitpong, Alban Kuriqi
'Alban Kuriqi'] The Weibull distribution has been used to analyze data from many fields, including engineering, survival and lifetime analysis, and weather forecasting, particularly wind speed data. It is useful to measure the central tendency of wind speed data in specific locations using statistical parameters for…
Tien-Wen Lee
The General Linear Model (GLM) has been widely used in research, where error term has been treated as noise. However, compelling evidence suggests that in biological systems, the target variables may possess their innate variances. A modified GLM was proposed to explicitly model biological variance and non-biological…
Nicholas Schreck, Hans-Peter Piepho, Martin Schlather
The additive genomic variance in linear models with random marker effects can be defined as a random variable that is in accordance with classical quantitative genetics theory. Common approaches to estimate the genomic variance in random-effects linear models based on genomic marker data can be regarded as the…
Esther Heid, Charles J. McGill, Florence H. Vermeire, William H. Green
Characterizing uncertainty in machine learning models has recently gained interest in the context of machine learning reliability, robustness, safety, and active learning. Here, we separate the total uncertainty into contributions from noise in the data (aleatoric) and shortcomings of the model (epistemic), further…
Daniel L. Rabosky
The concept of variance is the foundation of modern statistics; it reflects our awareness that independent samples from a single population or stochastic process can produce a range of outcomes. A recent pair of articles in the journal Evolution abandons the notion of the sample variance and advocates for uncorrected…
Javid Shabbir, Ronald Onyango, Sajjad Haider Bhatti
In this study, we propose an improved unbiased estimator in estimating the finite population mean using a single auxiliary variable and rank of the auxiliary variable by adopting the Hartley-Ross procedure when some parameters of the auxiliary variable are known. Expressions for the bias and mean square error or…
Xiaoming Ye
- Variance is the evaluation of probability interval of an error, instead of the dispersion of measured value defined by the existing measurement theory. The dispersion of measured value is 0. - Variance is expressed by the dispersion of all possible values of error. - All possible values refer to the test values under…
Faranak Goodarzi, Raheleh Zamini
In this paper, we propose nonparametric estimators for varextropy function of an absolutely continuous random variable. Consistency of the estimators is established under suitable regularity conditions. Moreover, a simulation study is performed to compare the performance of the proposed estimators based on mean squared…
Esther Heid, Charles J. McGill, Florence H. Vermeire, William H. Green
Characterizing uncertainty in machine learning models has recently gained interest in the context of machine learning reliability, robustness, safety, and active learning. Here, we separate the total uncertainty into contributions from noise in the data (aleatoric) and shortcomings of the model (epistemic), further…
Houman Heidarabadi, Melina Graner, Holger Hesse
Profitability, reliability, and efficiency of battery systems across a broad spectrum of applications, including both stationary energy storage and automobile sectors, are critically dependent on accurate battery lifespan predic-tions. Traditional deterministic models for estimating battery longevity are inadequate, as…
Hao Tang, Tianle Yue, Ying Li
Machine learning (ML) has become an important technique in materials science, markedly accelerating the discovery and design of novel materials, and concurrently lowering the burden of experimental costs. Uncertainty quantification (UQ) plays a pivotal role in the accurate prediction and innovative design of novel…
Authors not listed
Optimizing the synthesis conditions of advanced materials is challenging, especially when outcomes are subject to inherent experimental uncertainties. Bayesian optimization is a popular tool for accelerating materials discovery, but its standard risk-neutral framework overlooks the variability of outcomes under…
Authors not listed
We develop a comprehensive theoretical framework for quantum-enhanced risk modeling in financial systems, establishing mathematical foundations for representing and computing risk factors using quantum states and operations. The theory begins by formulating portfolio risk as quantum observables, where correlations…
Authors not listed
Plastic mechanical recycling is the conventional technological step towards circularity. In such aspects, complex mixtures of polyolefin blends are often fed into mechanical recycling systems, resulting in moulded products with uncertain quality. To add to the difficulty of heterogeneous feedstocks, the testing of…