Bessel's correction adjusts the denominator in the sample variance formula from n to n − 1 to produce an unbiased estimator for the population variance. This paper includes rigorous derivations, geometric interpretations, and visualizations. It then introduces the concept of "bariance," an...

This paper takes a deep dive into mesh routing protocols, unraveling how they hold up under the pressures of varying node densities and the hustle and bustle of mobility. This paper included robust and advanced non-parametric statistical tests-think Kruskal-Wallis and Mann-Whitney-to figure out...

Thermodynamic laws are dominant and not deniable, we need to consider those in the context of system theory. From the system's perspective the entropy issue is decisive for the evaluation of future economic options and constraints. The paper sensitizes for entropic adequate firm acts in regard...

In study described in this article, we developed a memory type estimator for the population mean in stratified successive sampling. We used the past sample information together with the current sample information through hybrid exponentially weighted moving averages statistics. We have also used...

This paper compares the statistical and economic performance of state-of-the-art highfrequency based multivariate volatility models with a simpler, widely used alternative-the Exponentially Weighted Moving Average (EWMA) filter. Using over two decades of 100 U.S. stock returns (2002-2023), we...

Performing an analysis of variance (ANOVA) on a large dataset spanning many dimensions becomes computationally challenging or even infeasible. We develop a new, fast procedure, ANOVA-HDFE, which uses sequential linear regressions and builds on recent advances in regression analysis with...

This paper proposes a nonparametric estimator of the spot volatility matrix with high-frequency data. Our newly proposed Positive Definite Fourier (PDF) estimator produces symmetric positive semi-definite estimates and is consistent with a suitable choice of the localizing kernel. The PDF...

A class of estimators of finite population variance (S_y^2) using auxiliary information has been proposed under simple random sampling without replacement (SRSWOR) scheme. An attempt has been made to derive the minimum variance unbiased estimator of finite population variance from the proposed...

This paper studies covariate adjusted estimation of the average treatment effect in stratified experiments. We work in a general framework that includes matched tuples designs, coarse stratification, and complete randomization as special cases. Regression adjustment with treatment‐covariate...

We investigate portfolio selection performance as in Markowitz by evaluating variance matrix estimation criteria in the currency market. This study challenges theoretically rigorous shrinkage covariance estimators using multiple evaluation metrics: systematic loss function, risk profile of...

This paper reviews the literature on firm wage differences and the fixed effects methods typically used to measure these differences. High wage firms tend to be more productive, larger, more sought after by workers, and to employ more credentialed and higher wage workers. The latest evidence...

Gaussian Structured Additive Regression provides a flexible framework for additive decomposition of the expected value with nonlinear covariate effects and time trends, unit- or cluster-specific heterogeneity, spatial heterogeneity, and complex interactions between covariates of different types....

We examine two major topics in the field of cryptocurrencies. On the one hand, we investigate possible long-run equilibrium relationships among ten major cryptocurrencies by applying two different cointegration tests. This analysis aims at constructing cointegrated portfolios that enable...

This paper examines optimal portfolio selection using quantile-based risk measures such as Valueat-Risk (VaR) and Conditional Value-at-Risk (CVaR). We address the case of a singular covariance matrix of asset returns, which leads to an optimization problem with infinitely many solutions. An...

Modeling policy effects in the context of high-dimensional data requires a balanced consideration of omitted interaction bias and overfitting problems. This paper investigates the role of machine learning algorithms in stabilizing estimates and demonstrates the possible regularization bias...

This paper is inspired by medical studies in which the same patients with multiple sclerosis are examined at several successive visits (doctor's appointments) and described by fractional anisotropy tract profiles, which can be represented as f unctions. Since the observations for each patient...

This study develops a flexible and efficient generalized ratio-product cum regression-type estimator of population variance utilizing auxiliary variable in two-phase sampling that incorporates the properties of ratio-type and product-type estimators. The properties of the estimator were derived...

Covariates selection is essential when faced with many variables in modern causal inference in a data-rich environment. Particularly, the efficiency of the average causal effect (ACE) can be improved by including covariates only related to the outcome and reduced by including covariates related...

In this study, we examine the nexus between oil tail risks and the realized variance of consumer prices in six advanced economies, namely, Canada, France, Germany, Japan, the United Kingdom, and the United States. Importantly, we estimate the oil tail risks following the Conditional...

This note provides closed-form expressions for spatial Greeks (Delta and Gamma) for discretely monitored realized variance swaps under several common parametric model assumptions. We derive closed-form results for stochastic volatility and exponential L´evy models, as well as some...

In this paper, we perform a comprehensive study of different covariance and precision matrix estimation methods in the context of minimum variance portfolio allocation. The set of models studied by us can be broadly categorized as: Gaussian Graphical Model (GGM) based methods, Shrinkage Methods,...

This study proposes a novel multivariate neural network model, coined as Realized Covariance Matrix NBEATSx (RCM-NBEATSx), for forecasting realized covariance matrices (RCMs) of financial securities. RCM-NBEATSx ensures that the predicted RCMs are positive semidefinite through the employment of...