Estimation Of Mean Research Articles

Preliminary estimators of population mean using ranked set sampling in the presence of measurement error and non-response error with applications and simulation study

In order to estimate the population mean in the presence of both non-response and measurement errors that are uncorrelated, the paper presents some novel estimators employing ranked set sampling by utilizing auxiliary information. Up to the first order of approximation, the equations for the bias and mean squared error of the suggested estimators are produced, and it is found that the proposed estimators outperform the other existing estimators analysed in this study. Investigations using simulation studies and numerical examples show how well the suggested estimators perform in the presence of measurement and non-response errors. The relative efficiency of the suggested estimators compared to the existing estimators has been expressed as a percentage, and the impact of measurement errors has been expressed as a percentage computation of measurement errors.

Efficient population mean estimation via stratified sampling with dual auxiliary information: A real estate perspective

Auxiliary information is an essential component in the field of survey sampling since it enables precise estimation of population parameters like mean, variance, distribution function, and so on, which in turn guarantees the best possible outcomes. In order to estimate the population mean of a study variable, this study makes use of auxiliary information in a two-fold approach. Through a stratified random sampling scheme, we introduce a novel class of estimators that utilize auxiliary information and their corresponding ranks. By conducting a thorough evaluation based on metrics such as mean square error and percentage relative efficiency, these proposed estimators have been shown to be effective in the estimation process. Empirical validation is conducted using a real dataset sourced from the domain of real estate. Exploring the relationship between Assessed Value (X) and Sale Amount (Y) during a five-year period extending from 2017 to 2021 is the primary emphasis of the empirical validation process, which is carried out with the assistance of a real dataset of real estate data. Furthermore, in order to demonstrate that our suggested estimator is superior to conventional unbiased estimators, as well as traditional regression estimators and other estimators that have been considered in the literature, a full simulation analysis is carried out. Our proposed estimator appears to be the most effective choice after being subjected to a comparison study against a variety of preexisting approaches. The findings of this study not only make a significant contribution to the development of the methodology of survey sampling but also offer vital insights for predictive modeling within the real estate sector.

Automated estimation of thoracic rotation in chest X-ray radiographs: a deep learning approach for enhanced technical assessment.

This study aims to develop an automated approach for estimating the vertical rotation of the thorax, which can be used to assess the technical adequacy of chest X-ray radiographs (CXRs). Total 800 chest radiographs were used to train and establish segmentation networks for outlining the lungs and spine regions in chest X-ray images. By measuring the widths of the left and right lungs between the central line of segmented spine and the lateral sides of the segmented lungs, the quantification of thoracic vertical rotation was achieved. Additionally, a life-size, full body anthropomorphic phantom was employed to collect chest radiographic images under various specified rotation angles for assessing the accuracy of the proposed approach. The deep learning networks effectively segmented the anatomical structures of the lungs and spine. The proposed approach demonstrated a mean estimation error of less than 2° for thoracic rotation, surpassing existing techniques and indicating its superiority. The proposed approach offers a robust assessment of thoracic rotation and presents new possibilities for automated image quality control in chest X-ray examinations. This study presents a novel deep-learning-based approach for the automated estimation of vertical thoracic rotation in chest X-ray radiographs. The proposed method enables a quantitative assessment of the technical adequacy of CXR examinations and opens up new possibilities for automated screening and quality control of radiographs.

New Modification of Ranked Set Sampling for Estimating Population Mean: Neutrosophic Median Ranked Set Sampling with an Application to Demographic Data

The study addressed the limitations of classical statistical methods when dealing with ambiguous data, emphasizing the importance of adopting neutrosophic statistics as a more effective alternative. Classical methods falter in managing uncertainty inherent in such data, necessitating a shift towards methodologies like neutrosophic statistics. To address this gap, the research introduced a novel sampling approach called “neutrosophic median ranked set sampling” and incorporated neutrosophic estimators tailored for estimating the population mean in the presence of ambiguity. This modification aims to address the inherent challenges associated with estimating the population mean when dealing with neutrosophic data. The methods employed involved modifying traditional ranked set sampling techniques to accommodate neutrosophic data characteristics. Additionally, neutrosophic estimators were developed to leverage auxiliary information within the framework of median-ranked set sampling, enhancing the accuracy of population mean estimation under uncertain conditions. The methods employed involved modifying traditional ranked set sampling techniques to accommodate neutrosophic data characteristics. Bias and mean squared error equations for the suggested estimators were provided, offering insights into their theoretical underpinnings. To illustrate the effectiveness and practical applications of the proposed methodology and estimators, a numerical demonstration and simulation study have been conducted using the R programming language. The key results highlighted the superior performance of the proposed estimators compared to existing alternatives, as demonstrated through comprehensive evaluations based on mean squared error and percentage relative efficiency criteria. The conclusions drawn underscored the effectiveness of the neutrosophic median ranked set sampling approach and suggested estimators in estimating the population mean under conditions of uncertainty, particularly when utilizing neutrosophic auxiliary information and validated real-life applicability. The methodology and estimators presented in the study were shown to yield interval-based results, providing a more realistic representation of uncertainty associated with population parameters. This interval estimation, coupled with minimum mean squared error considerations, enhanced the efficacy of the estimators in determining population mean values. The novelty of the work lies in its introduction of a tailored sampling approach and estimators designed specifically for neutrosophic data, filling a significant gap in the literature. By extending classical statistics to accommodate ambiguity, the study offers a substantial advancement in statistical methodology, particularly in domains where precise data is scarce and uncertainty is prevalent. Furthermore, the empirical validation through numerical demonstrations and simulation studies using the R programming language adds robustness to the proposed methodology and contributes to its practical applicability.

Accounting for regression to the mean under the bivariate -distribution.

Regression to the mean occurs when an unusual observation is followed by a more typical outcome closer to the population mean. In pre- and post-intervention studies, treatment is administered to subjects with initial measurements located in the tail of a distribution, and a paired sample -test can be utilized to assess the effectiveness of the intervention. The observed change in the pre-post means is the sum of regression to the mean and treatment effects, and ignoring regression to the mean could lead to erroneous conclusions about the effectiveness of the treatment effect. In this study, formulae for regression to the mean are derived, and maximum likelihood estimation is employed to numerically estimate the regression to the mean effect when the test statistic follows the bivariate -distribution based on a baseline criterion or a cut-off point. The pre-post degrees of freedom could be equal but also unequal such as when there is missing data. Additionally, we illustrate how regression to the mean is influenced by cut-off points, mixing angles which are related to correlation, and degrees of freedom. A simulation study is conducted to assess the statistical properties of unbiasedness, consistency, and asymptotic normality of the regression to the mean estimator. Moreover, the proposed methods are compared with an existing one assuming bivariate normality. The -values are compared when regression to the mean is either ignored or accounted for to gauge the statistical significance of the paired -test. The proposed method is applied to real data concerning schizophrenia patients, and the observed conditional mean difference called the total effect is decomposed into the regression to the mean and treatment effects.

Mean and covariance estimation for discretely observed high-dimensional functional data: Rates of convergence and division of observational regimes

Estimation of the mean and covariance parameters for functional data is a critical task, with local linear smoothing being a popular choice. In recent years, many scientific domains are producing multivariate functional data for which p, the number of curves per subject, is often much larger than the sample size n. In this setting of high-dimensional functional data, much of developed methodology relies on preliminary estimates of the unknown mean functions and the auto- and cross-covariance functions. This paper investigates the convergence rates of local linear estimators in terms of the maximal error across components and pairs of components for mean and covariance functions, respectively, in both L2 and uniform metrics. The local linear estimators utilize a generic weighting scheme that can adjust for differing numbers of discrete observations Nij across curves j and subjects i, where the Nij vary with n. Particular attention is given to the equal weight per observation (OBS) and equal weight per subject (SUBJ) weighting schemes. The theoretical results utilize novel applications of concentration inequalities for functional data and demonstrate that, similar to univariate functional data, the order of the Nij relative to p and n divides high-dimensional functional data into three regimes (sparse, dense, and ultra-dense), with the high-dimensional parametric convergence rate of log(p)/n1/2 being attainable in the latter two.

Novel logarithmic imputation procedures using multi auxiliary information under ranked set sampling

Ranked set sampling (RSS) is known to increase the efficiency of the estimators while comparing it with simple random sampling. The problem of missingness creates a gap in the information that needs to be addressed before proceeding for estimation. Negligible amount of work has been carried out to deal with missingness utilizing RSS. This paper proposes some logarithmic type methods of imputation for the estimation of population mean under RSS using auxiliary information. The properties of the suggested imputation procedures are examined. A simulation study is accomplished to show that the proposed imputation procedures exhibit better results in comparison to some of the existing imputation procedures. Few real applications of the proposed imputation procedures is also provided to generalize the simulation study.

Utilization of genetic algorithm in tuning the hyper-parameters of hybrid NN-based side-slip angle estimators

This paper proposes a solution to enhance and compare different neural network (NN)-based side-slip angle estimators. The feed-forward neural networks (FFNNs), recurrent neural networks, long short-term memory units (LSTMs), and gated recurrent units are investigated. However, there is a lack in the selection criteria of the architectures’ hyper-parameters. Therefore, the genetic algorithm is integrated with the NN-based estimators to find the optimal hyper-parameters for the studied architectures. The tuned hyper-parameters in this work include the number of neurons, number of layers, activation function, optimizer type, and learning rate. The objective function of the optimization problem is minimizing the root-mean-square error (RMSE) on multiple testing data. The optimal models are further included in the design of a hybrid NN estimator with Kalman filter. In the hybrid estimators, the optimal NN estimators are used as virtual sensors to correct the prediction of the side-slip angle resulting from the mathematical lateral vehicle model. Eventually, the performance of the best selected model is evaluated in terms of different metrics; mean RMSE, mean error variance, mean training time, and mean estimation time. LSTMs are found to achieve the lowest mean RMSE while being tested on highly generalized data yielding the highest training and estimation time. However, FFNNs achieve the lowest RMSE while being tested on low generalized data and the lowest training and estimation time. Meanwhile, it is observed that the hybrid estimators achieved lower RMSE with great enhancement compared to the non-hybridized ones proving the effectiveness of the proposed approach and increasing the side-slip estimation generalization ability in unknown environments with high uncertainties, which are not covered by the training dataset for the NNs estimators.

Using Satellite Data Assimilation Techniques to Combine Infrasound Observations and a Full Ray-Tracing Model to Constrain Stratospheric Variables

Abstract Infrasound waves generated at Earth’s surface can reach high altitudes before returning to the surface to be recorded by microbarometer array stations. These waves carry information about the propagation medium, in particular temperature and winds in the atmosphere. It is only recently that studies on the assimilation of such data into atmospheric models have been published. Intending to advance this line of research, we here use the modulated ensemble transform Kalman filter (METKF)—commonly used in satellite data assimilation—to assimilate infrasound-related observations in order to update a column of three vertically varying variables: temperature and horizontal wind speeds. This includes stratospheric and mesospheric heights, which are otherwise poorly observed. The numerical experiments on synthetic data but with realistic reanalysis product atmospheric specifications (following the observing system simulation experiment paradigm) reveal that a large ensemble is capable of reducing errors, especially for wind speeds in stratospheric heights close to 30–60 km. While using a small ensemble leads to incorrect analysis increments and large estimation errors, the METKF ameliorates this problem and even achieves error reduction from the prior to the posterior mean estimator. Significance Statement The stratosphere and mesosphere have significantly less observational coverage compared to the troposphere, especially for the winds. This lack of information can reduce the accuracy of medium-range weather forecasts. By mimicking a realistic setup, this study paves the way for including novel observations in the estimation of the atmospheric state in these heights using an ensemble data assimilation method. These observations come from a dataset of opportunity containing infrasound-related measurements that are routinely carried out at several stations around the world.

Exponential Ratio Class of Estimators of Finite Population Mean Using Deciles of an Auxiliary Variable

Practitioners of survey sampling have been working to reduce bias and increase efficiency in the estimation of finite population parameters. The present work aims at developing an exponential ratio class of finite population mean estimators with deciles of an auxiliary variable. Using the Taylor's series technique, the mean square error (MSE) of the developed estimators was obtained up to the first order of approximation. The study's suggested estimators outperform competing estimators, according to the findings of a numerical analysis that was done on them in comparison to the existing estimators that were taken into consideration.

Impact of government expenditure on unemployment in Asian countries: does institutional quality matter?

Purpose Asian countries have had persistent unemployment levels. The purpose of this paper is to investigate the impact of government spending on unemployment. Furthermore, this paper investigates the moderating role of institutional quality on the government spending–unemployment nexus. Design/methodology/approach Using data from 35 Asian countries from 2000 to 2022, the dynamic ordinary least squares and fully modified ordinary least squares technique is used to tackle with aforementioned issue. In addition, pooled mean group estimation is applied to verify the robustness of the findings. Findings The results show that an increase in government expenditure and better institutions reduce the unemployment rate. Interestingly, the negative impact of government expenditure on unemployment will enhance and intensify with better institutional quality. Furthermore, trade openness and foreign direct investment decrease unemployment in Asian countries. The results are robust to various specifications. Practical implications Findings from this study provide important implications for governments. Governments should use public expenditure efficiently and enhance and improve institutional quality to reduce unemployment. Originality/value To the best of the author’s knowledge, this study pioneers the investigation of the moderating role of institutional quality in the relationship between government expenditure and unemployment in Asian countries.

Spatiotemporal methods for estimating subsurface ocean thermal response to tropical cyclones

Abstract. Tropical cyclones (TCs), driven by heat exchange between the air and sea, pose a substantial risk to many communities around the world. Accurate characterization of the subsurface ocean thermal response to TC passage is crucial for accurate TC intensity forecasts and an understanding of the role that TCs play in the global climate system. However, that characterization is complicated by the high-noise ocean environment, correlations inherent in spatiotemporal data, relative scarcity of in situ observations, and the entanglement of the TC-induced signal with seasonal signals. We present a general methodological framework that addresses these difficulties, integrating existing techniques in seasonal mean field estimation, Gaussian process modeling, and nonparametric regression into an ANOVA decomposition model. Importantly, we improve upon past work by properly handling seasonality, providing rigorous uncertainty quantification, and treating time as a continuous variable, rather than producing estimates that are binned in time. This ANOVA model is estimated using in situ subsurface temperature profiles from the Argo fleet of autonomous floats through a multistep procedure, which (1) characterizes the upper-ocean seasonal shift during the TC season, (2) models the variability in the temperature observations, and (3) fits a thin-plate spline using the variability estimates to account for heteroskedasticity and correlation between the observations. This spline fit reveals the ocean thermal response to the TC passage. Through this framework, we obtain new scientific insights into the interaction between TCs and the ocean on a global scale, including a three-dimensional characterization of the near-surface and subsurface cooling along the TC storm track and the mixing-induced subsurface warming on the track's right side.

A New Rank Estimator for Least Squares Estimation of Weibull Modulus

The Weibull distribution is widely used in reliability analysis to evaluate the failure behavior and lifetime characteristics of various systems and components. One of the most commonly used methods for estimating the parameters of the Weibull distribution is the ordinary least squares (OLS) technique, which is based on fitting a linear regression model to the transformed data. This paper proposes a new rank estimator for ordinary least squares estimation of Weibull modulus, a key parameter used as a measure of variability in the data. The new rank estimator is a quadratic function of the order statistic number, with three parameters that are optimized by Monte Carlo simulations. Using relative efficiency as a criterion, the performance of the new rank estimator is compared with three commonly used rank estimators, mean, median and hazen rank estimators, which are linear functions of the order statistic number. The results show that the new rank estimator has a significant advantage over the other rank estimators for any sample size between 3 and 150. The findings also imply that other nonlinear functions, such as cubic polynomials, could be applied to further improve the efficiency of the parameter estimators of the ordinary least squares method.

Estimation of Population Mean Using Some Improved Imputation Methods for Missing Data in Sample Surveys

. In this research article, we present novel imputation methods designed to address missing data challenges in sample surveys. We then introduce innovative estimation procedures for calculating population means based on these methods. Our study thoroughly examines the properties of these new estimation procedures, assessing their biases and mean square errors. Through the use of both real and simulated data sets, we demonstrate the superior performance of our proposed estimators compared to existing methods in similar scenarios. In conclusion, we offer practical recommendations for survey practitioners.

Enhancing estimation efficiency with proposed estimator: A comparative analysis of Poisson regression-based mean estimators

In many studies, the Poisson regression model is mostly intended for modelling count responses. Recently, it was shown that the exploitation of the Poisson regression coefficient within the Koç (2021) ratio estimator increases the efficiency of the estimator. This study uses a new Poisson regression-based regression-type mean estimator with simple random sampling and finds its related mean square error formula. Essentially, we contrast the suggested estimators' mean square errors with those of previously published estimators. For the real data study, estimators were calculated for three real populations and the superior performance of the proposed estimator was observed. Similar results were obtained from the simulation study. As an outcome of these estimations, the proposed estimators are more effective than existing estimators. The empirical results verified the theoretical results to be remarkable.

Impact of crop rotation and tillage operations on mitigating greenhouse gas emissions and evaluation of sustainability index in rice- wheat-green gram cropping system of north Bihar

In North Bihar (NB), the conventional rice-wheat cropping system has led to soil, water, and environmental degradation, alongside low profitability, threatening sustainability. To address these concerns, a thorough field research was conducted over the course of three years to assess different methods of tillage and crop establishment in a rice, wheat, and greengram cycle. The experiment involved five scenarios with different combinations of crop rotation, tillage techniques, seeding procedures, fertilizer use, and irrigation strategies. Uncertainty analysis showed no significant change in mean and variance estimation among seven scenario replications at 5% significance level. Compared to traditional farming (SN-1), managing DSR-rice (SN-5) increased profitability by 17.56%, improved energy use efficiency (EUE) by 32.16%, and reduced irrigation by 24.76% and global warming potential (GWP) by 23.46%. Similarly, substituting zero tillage wheat (ZTW) SN-5 resulted in comparable profitability gains (18.25%) and significant improvements in irrigation (10 %), EUE (+48.65%), and GWP (−20 %) compared to SN-1. Green gram ZT also showed increased profitability (17.35%), with notable improvements in EUE (+38.31%) and GWP (−12.92%) compared to SN-1. Principal component and correlation analyses revealed relationships between total energy inputs, yields, economic returns, and sustainability indices, highlighting the benefits of crop rotation and tillage practices in optimizing resource use. The study suggests that compared to conventional systems, significant improvements in productivity, profitability, energy-use efficiency, and environmental mitigation can be achieved with Crop Rotation and Tillage Operations techniques.

An effective computational and simulation study for population mean estimation on current occasion

An effective computational and simulation study for population mean estimation on current occasion

Geometric Properties Connected with a Certain Multiplier Integral q−Analogue Operator

The topic concerning the introduction and investigation of new classes of analytic functions using subordination techniques for obtaining certain geometric properties alongside coefficient estimates and inclusion relations is enriched by the results of the present investigation. The prolific tools of quantum calculus applied in geometric function theory are employed for the investigation of a new class of analytic functions introduced by applying a previously defined generalized q−integral operator and the concept of subordination. Investigations are conducted on the new class, including coefficient estimates, integral representation for the functions of the class, linear combinations, forms of the weighted and arithmetic means involving functions from the class, and the estimation of the integral means results.

Enhanced Discrete Wavelet Transform–Non-Local Means for Multimode Fiber Optic Vibration Signal

Real-time monitoring of heartbeat signals using multimode fiber optic microvibration sensing technology is crucial for diagnosing cardiovascular diseases, but the heartbeat signals are very weak and susceptible to noise interference, leading to inaccurate diagnostic results. In this paper, a combined enhanced discrete wavelet transform (DWT) and non-local mean estimation (NLM) denoising method is proposed to remove noise from heartbeat signals, which adaptively determines the filtering parameters of the DWT-NLM composite method using objective noise reduction quality assessment metrics by denoising different ECG signals from multiple databases with the addition of additive Gaussian white noise (AGW) with different signal-to-noise ratios (SNRs). The noise reduction results are compared with those of NLM, enhanced DWT, and conventional DWT combined with NLM method. The results show that the output SNR of the proposed method is significantly higher than the other methods compared in the range of −5 to 25 dB input SNR. Further, the proposed method is employed for noise reduction of heartbeat signals measured by fiber optic microvibration sensing. It is worth mentioning that the proposed method does not need to obtain the exact noise level, but only the adaptive filtering parameters based on the autocorrelation nature of the denoised signal. This work greatly improves the signal quality of the multimode fiber microvibration sensing system and helps to improve the diagnostic accuracy.

The role of financial inclusion and tourism in tackling environmental challenges of industrialization and energy consumption: Redesigning Sustainable Development Goals policies

AbstractThe Next 11 (N‐11) countries are facing many challenges in achieving the objectives of Sustainable Development Goals (SDGs), as preserving environmental quality has become a major challenge for them. Specifically, achieving SDG 3, SDG 7, SDG 9, and SDG 13, the current research scrutinizes the influence of industrialization, tourism, and renewable and fossil fuel energy consumption with the moderating role of financial development on the ecological footprint in the N‐11 countries during 1995–2018. By testing all nine hypotheses through the augmented mean group, common correlated effects mean group estimators, and the Dumitrescu and Hurlin causality approach, various interesting findings are discovered. The findings verified that industrialization, tourism, and non‐renewable energy play major roles in driving environmental pollution. However, renewables and financial development increase environmental quality. Moreover, the interacting role of financial expansion with industrialization and non‐renewables significantly deteriorates the environment. In contrast, the moderating role of financial development with tourism and renewable energy protects environmental excellence. In addition, the growth hypothesis is discovered from tourism and renewables to ecological footprint, and the feedback hypothesis is discovered between industrialization, financial development, and ecological footprint. Following the empirical findings, we recommend several policy implications to address the objectives of SDG 3, SDG 7, SDG 9, and SDG 13.