Efficient Estimation Research Articles

A New Modified Confidence Interval Estimator of Location Parameter for Skewed Distribution

A new modified confidence interval has been proposed which incorporates a new point estimator of the location parameter mean for skewed data distribution. Traditionally, the trimmed mean confidence interval estimator (Trm-ci) is a robust method for dealing with the skewness of the underlying data distribution. However, the Trm-ci method trims a certain fraction of endpoint observations to address skewness, which may result in a loss data information. The Students’t confidence interval (t-ci) estimator, while the most efficient estimator at normal models, becomes impractical in a situation where observed data is subject to non-normality due to robustness. In between the two, the median confidence interval estimator (Med-ci) is expected to retain the robustness of Trm-ci and the efficiency of t-ci. The idea behind the proposed new modified confidence interval estimator (Mod-ci) is to consider both the sample mean and sample median simultaneously, while also using end-point information without trimming any observations. As such, the proposed Mod-ci is expected to be as good as or better than other underlying methods regarding robustness and efficiency when dealing with skewed data distributions. In this study, we examine the performance of the new method compared to most commonly used methods through examples and simulating data from skewed distribution with varying degree of skewness. The results of examples and simulations suggest that the proposed method is as good as, or better than other estimators relevant to this study, as measured by estimated coverage probability and the width of associated confidence interval. Therefore, this method is recommended for practical application while dealing with real-life data exhibiting skewness.

Fast DOA Estimation of IGS Noncircular Signals Under Coprime Arrays

Traditional direction-of-arrival (DOA) estimation methods often encounter challenges such as low accuracy and high computational complexity, particularly in complex signal environments with multiple sources. To address these issues, this paper proposes a fast DOA estimation method for improper Gaussian signaling (IGS) non-circular signals, based on a coprime array; this approach involves extending the array model by leveraging the unique properties of non-circular signals. Subsequently, this study introduces the ANCM algorithm, a method designed for fast DOA estimation of IGS non-circular signals within a non-uniform noise background. The proposed ANCM algorithm is then compared to classical algorithms, through simulations in a non-uniform noise environment. Experimental results demonstrate that the ANCM algorithm significantly outperforms traditional methods in such conditions. Therefore, the development of new, efficient DOA estimation methods holds substantial theoretical and practical value.

A non-invasive heart rate prediction method using a convolutional approach.

The research focuses on leveraging convolutional neural networks (CNNs) to enhance the analysis of physiological signals, specifically photoplethysmogram (PPG) data which is a valuable tool for non-invasive heart rate prediction. Recognizing the global challenge of heart failure, the study seeks to provide a rapid, accurate, and non-invasive alternative to traditional, uncomfortable blood pressure cuffs. To achieve more accurate and efficient heart rate estimates, a k-fold CNN model with an optimal number of convolutional layers is employed. While the findings show promising results, the study addresses potential sources of error in cuffless PPG-based heart rate measurement, including motion artifacts and skin color variations, emphasizing the need for validation against gold standard methods. The research optimizes a CNN model with optimal layers, operating on 1D arrays of 8-s data slices and employing k-fold cross-validation to mitigate probabilistic uncertainties. Finally, the model yields a remarkable minimum absolute error (MAE) rate of 6.98 beats per minute (bpm), marking a significant 10% improvement over recent studies. The study also advances medical diagnostics and data analysis, then lays a strong foundation for developing cost-effective, reliable devices that offer a more comfortable and efficient way of predicting heart rate.

Joint Modeling of Longitudinal and Survival Data

In medical studies, time-to-event outcomes such as time to death or relapse of a disease are routinely recorded along with longitudinal data that are observed intermittently during the follow-up period. For various reasons, marginal approaches to model the event time, corresponding to separate approaches for survival data/longitudinal data, tend to induce bias and lose efficiency. Instead, a joint modeling approach that brings the two types of data together can reduce or eliminate the bias and yield a more efficient estimation procedure. A well-established avenue for joint modeling is the joint likelihood approach that often produces semiparametric efficient estimators for the finite-dimensional parameter vectors in both models. Through a transformation survival model with an unspecified baseline hazard function, this review introduces joint modeling that accommodates both baseline covariates and time-varying covariates. The focus is on the major challenges faced by joint modeling and how they can be overcome. A review of available software implementations and a brief discussion of future directions of the field are also included.

Tuning properties of binary functionalization of Sc2C MXenes for supercapacitor electrodes

Surface functionalization of two-dimensional (2D) materials like Sc2C MXenes offers a promising avenue for tailoring their properties for various applications. At the same time, significant research has focused on pure terminations involving oxygen (-O), fluorine (-F), or hydroxyl (-OH) groups. Binary surface functionalization still needs to be explored. Our work addresses this gap by investigating binary functionalized Sc2C monolayers, specifically Sc2CFN and Sc2COS. Using first-principles calculations, we explore the structural, electronic, and quantum capacitance (CQ) properties of pristine Sc2C and functionalized Sc2CFN and Sc2COS. The CQ measurements reveal distinctive electronic behaviour, with Sc2CFN and Sc2COS exhibiting indirect bandgaps of 0.90 and 1.48 eV, respectively. Introducing functional groups induces a metal-to-semiconductor transition, enhancing charge storage at the cathode and increasing the CQ to 371.64 and 341.23 μF/cm2 for Sc2CFN and Sc2COS, respectively. As far as energy applications are concerned, we also calculate the Shockley-Queisser (SQ) efficiency, which is a widely accepted quantity to get an estimate of the photovoltaic efficiency of 28.77 % for Sc2CFN and 32.97 % for Sc2COS materials. Additionally, we analyze the current-voltage (IV) characteristics, uncovering negative differential conductance (NDC) effects in Sc2COS, indicative of its potential for fast molecular switching applications. Our study provides valuable insights into the properties of binary functionalized Sc2C MXenes, paving the way for their utilization in energy storage and nanoelectronic devices.

Evaluation of Indian Lightning Location Network (ILLN) and characterization of cloud-to-ground lightning over Lucknow and Shillong using an ordinary camera

Abstract. This work is showing the performance of Indian Lightning Location Network (ILLN) and lightning characteristics around Lucknow and Shillong India. Observation of ground truth using ordinary camera show the location accuracy of lightning location network. By analysing the optically observed data the average cloud to ground lightning flash detection efficiency is 78.9 percent but some of them misclassified. In addition, throughout the observation in Lucknow, authors found the average inter-stroke intervals were 139.2 ms that ranges from 34 ms to 442 ms. Further, average multiplicity and multi-channel termination of these strokes were 2.3 and 1.2 respectively. The single-stroke flashes were below 23% out of the total number of negative cloud-to-ground flashes. In contrary to the Lucknow, all the flashes observed in Shillong seem single-stroke which have lower return stroke peak currents than Lucknow. This estimation of detection efficiency and characteristics will definitely assist to understand the different scenarios of lightning in plane and hilly area over the country and help to forecasters, and modelers.

Optimized Energy Management Strategy for an Autonomous DC Microgrid Integrating PV/Wind/Battery/Diesel-Based Hybrid PSO-GA-LADRC Through SAPF

This study focuses on microgrid systems incorporating hybrid renewable energy sources (HRESs) with battery energy storage (BES), both essential for ensuring reliable and consistent operation in off-grid standalone systems. The proposed system includes solar energy, a wind energy source with a synchronous turbine, and BES. Hybrid particle swarm optimizer (PSO) and a genetic algorithm (GA) combined with active disturbance rejection control (ADRC) (PSO-GA-ADRC) are developed to regulate both the frequency and amplitude of the AC bus voltage via a load-side converter (LSC) under various operating conditions. This approach further enables efficient management of accessible generation and general consumption through a bidirectional battery-side converter (BSC). Additionally, the proposed method also enhances power quality across the AC link via mentoring the photovoltaic (PV) inverter to function as shunt active power filter (SAPF), providing the desired harmonic-current element to nonlinear local loads as well. Equipped with an extended state observer (ESO), the hybrid PSO-GA-ADRC provides efficient estimation of and compensation for disturbances such as modeling errors and parameter fluctuations, providing a stable control solution for interior voltage and current control loops. The positive results from hardware-in-the-loop (HIL) experimental results confirm the effectiveness and robustness of this control strategy in maintaining stable voltage and current in real-world scenarios.

A shrinkage based diagonal T 2 control chart for high dimensional data

Multivariate control charts are commonly used in manufacturing engineering to identify abnormal changes in the process. In a high-dimensional paradigm, where the number of variables (p) exceeds the number of Phase I subgroups (m), the sample covariance matrix is ill-conditioned and will become singular. This situation makes the classical T 2 control chart inefficient and even invalid to employ for monitoring a mean vector in statistical process control. This study proposes a new multivariate shrinkage-based diagonal T 2 control chart for high dimensional data, where p is very large compared to m with individual observation. A shrinkage estimator is used to estimate a diagonal covariance matrix to obtain an invertible, well-conditioned, and efficient estimate of the sample covariance matrix. The proposal's performance is also evaluated using the probability of detecting a shift. A simulation study reveals that the proposed control chart has an efficient sensitivity in detecting shifts for the high dimensional data. Results also show that m has a minor effect on the probability of shift detection for out-of-control data. In general, this probability improves for larger p, when shift is introduced in all p.

What drives the overall technical efficiency of farm businesses in the Wheatbelt of Western Australia? Analysis of persistent and transient efficiencies in the presence of firm effects

AbstractThis study enhances our comprehension of the performance of farm businesses in Western Australia by estimating persistent and transient technical efficiency measures in the presence of firm‐level effects. The analysis is based on a balanced panel data set of 54 farm businesses from the years 2002 to 2011. We employ Kumbhakar et al.'s (2014, Journal of Productivity Analysis, 41, 321) model to estimate persistent efficiency and firm effects separately, as well as Kumbhakar's and Heshmati's (1995, American Journal of Agricultural Economics, 77, 660) model where these two factors are confounded. Furthermore, we investigate the factors that influence transient and persistent technical efficiency. Our findings reveal that failing to differentiate between persistent technical efficiency and firm effects underestimates the estimates of persistent and overall technical efficiency. This underestimation may result in misguided policy recommendations for improving the technical performance of farm businesses. We also find that persistent efficiency dominates overall technical efficiency. The significant determinants of persistent overall technical efficiency include the regional rainfall zone where a farm business is located, the managerial competency of the farm operator denoted by their age and off‐farm activities. Off‐farm income plays a crucial role in determining transient technical efficiency.

Quantile adaptive lasso: variable selection for quantile treatment effect estimation

Quantile treatment effects (QTEs) characterize different causal effects of treatment on outcome distribution. Propensity score (PS) methods are commonly employed for estimating QTEs in non-randomized studies. Previous studies have shown that insufficient and unnecessary adjustment for covariates in PS models leads to bias and efficiency loss of treatment effects. Confounders may vary across different quantiles of outcome distribution. To allow for the selection of covariates related to outcome at interesting quantiles corresponding to QTE, we proposed quantile adaptive lasso (QAL) to select covariates that can provide unbiased and efficient QTE estimation. QAL utilized linear quantile regression models to construct penalty weights. Theoretical analyses showed that QAL enjoys several asymptotic properties. Simulations showed the superiority of QAL over existing methods in variable selection and QTE estimation. We applied QAL to datasets from the China Health and Retirement Longitudinal Study to explore the impact of smoking on the severity of depression.

Efficient estimation of rotation-induced bias to reconstructed CMB lensing power spectrum

Efficient estimation of rotation-induced bias to reconstructed CMB lensing power spectrum

Enhancing Efficiency: Halton Draws in the Generalized True Random Effects Model

This paper measures the impact of the number of Halton draws in excess of ⌈n⌉ on technical efficiency in the generalized true random effects (four-component) stochastic frontier model estimated by simulated maximum likelihood. A substantial set of Monte Carlo simulations demonstrates that increasing the number of Halton draws to ⌈n3/4⌉ (⌈n2/3⌉) decreases the mean squared error of the total technical efficiency estimates by 6.1 (4.9) percent. Furthermore, increasing the number of Halton draws either improves or has no detrimental impact on correlation, mean squared error, relative bias, and upward bias for persistent, transient, and total technical efficiency. An energy sector application is included, to demonstrate how these issues can arise in practice, and how increasing Halton draws can improve parameter and efficiency estimates in empirical work.

An optimal subsampling design for large-scale Cox model with censored data

Subsampling designs are useful for reducing computational load and storage cost for large-scale data analysis. For massive survival data with right censoring, we propose a class of optimal subsampling designs under the widely-used Cox model. The proposed designs utilize information from both the outcome and the covariates. Different forms of the design can be derived adaptively to meet various targets, such as optimizing the overall estimation accuracy or minimizing the variation of specific linear combination of the estimators. Given the subsampled data, the inverse probability weighting approach is employed to estimate the model parameters. The resultant estimators are shown to be consistent and asymptotically normally distributed. Simulation results indicate that the proposed subsampling design yields more efficient estimators than the uniform subsampling by using subsampled data of comparable sample sizes. Additionally, the subsampling estimation significantly reduces the computational load and storage cost relative to the full data estimation. An analysis of a real data example is provided for illustration.

Efficient Handling of Non-response in Sample Surveys: Development of Ratio-type Variance Estimators

The present study has been under taken to address the issue of presence and absence of non-response problems in Sample surveys. We have developed some new efficient ratio-type variance estimators for the computation of the finite population variance in presence and absence of non-response, by incorporating Positional parameters as auxiliary information. The expression for mean square errors of proposed estimators has been derived up to the first order of approximation. To test the efficiency of new developed estimators, practical demonstration has been carried to ascertain the performance of suggested Estimators.

New Modified Efficient Variance Estimators for the Estimation of Population Variance in Survey Sampling

For efficient and precise estimate of population variance, we have proposed some new ratio type variance estimators in the current research work by incorporating the linear combination of conventional and non-conventional parameters of auxiliary information. Bias and mean square error have been computed up to the first order of approximation. Numerical demonstration has been carried out to test the efficiency of new proposed estimators against existing estimators.

Efficient High Heating Value estimation using Latin Hypercube Sampling and Artificial Neural Network-based approach.

To maximize energy recovery in waste-to-energy (WTE) systems, the High Heating Value (HHV) of municipal solid waste (MSW) must be accurately estimated. To forecast the HHV of MSW, this study proposes a unique method that combines an Artificial Neural Network (ANN) model with Latin Hypercube Sampling (LHS), with a focus on Solan City, Himachal Pradesh, India. In the present study, the elemental characteristics of waste have been used to deal with uncertainty and to find the suitable parameters responsible for the HHV of the MSW. Initially, Latin Hypercube Sampling (LHS) has been used to deal with uncertainty in the elemental composition of MSW, which includes carbon (C), hydrogen (H), nitrogen (N), sulfur (S), and oxygen (O) content. This elemental composition has been used as input parameters to the ANN model for predicting the HHV of MSW. The network 5-28-5-1 offered a minimum MAPE value of 2.18%, MSE, RMSE and R2 values are 0.012, 0.107 and 0.767, respectively. Thereafter, a synaptic weight approach was used to find the most significant parameters responsible for HHV in MSW. It was observed that carbon is the most suitable parameter for HHV of MSW. By dealing with the uncertainty in MSW characteristics, the integration of LHS strengthens the robustness of the model. The results offer an accurate and economical approach for HHV estimation, which will be useful for improving the MSW management and WTE conversion procedures.

Gamma Process Based Degradation Model with Fractional Gaussian Noise

In modern industrial and engineering systems, stochastic degradation models are widely used for reliability analysis and maintenance decision-making. However, due to imperfect sensors and environmental influences, it is difficult to directly observe the latent degradation states. Traditional degradation models typically assume that measurement errors have simple statistical properties, but this assumption often does not hold in practical applications. To address this issue, this paper constructs a degradation model based on the Gamma process (GP) and assumes that measurement noise can be characterized by the fractional Gaussian noise (FGN). Furthermore, this paper proposes a method combining Gibbs sampling with the stochastic expectation-maximization (SEM) algorithm to achieve efficient estimation of the model parameters and accurate inference of the latent degradation states. Simulation results indicate that the proposed model exhibits better generalizability compared to the GP model with Gaussian noise.

Multiscale assessment of land use efficiency in functional urban areas of Munich and Augsburg

Abstract. This study aims to understand the spatio-temporal variations in land use efficiency (LUE) estimations at different scales of assessment, focusing on the per-capita built-up area within the Functional Urban Areas (FUAs) of Munich and Augsburg, Germany. The analysis is done for the time epochs 1985, 1995, 2005, 2015 and 2020. FUAs are defined as the economic area of influence in each urban area, making the study of land consumption in FUAs crucial for spatial planning and policy analysis. Land use efficiency is computed using built up surface and population data extracted from the Global Human Settlement Layer. This is done at municipal level and 100 m grid level. Furthermore, the effects of using high resolution building footprints for LUE computations are assessed. The analysis shows that urban cores and the adjacent regions are densifying with lower per capita built surface consumption while smaller urban areas utilise more per capita built surface leading to inefficent land use. It is also observed that higher resolution assessments helps in identifying varying intra-regional LUE estimates for a focused urban planning strategy development. Our study concludes that land use efficiency has generally decreased over time in the commuting zones of FUAs while the urban cores have remained efficient.

Assessing variable importance in survival analysis using machine learning

SUMMARY Given a collection of features available for inclusion in a predictive model, it may be of interest to quantify the relative importance of a subset of features for the prediction task at hand. For example, in HIV vaccine trials, participant baseline characteristics are used to predict the probability of HIV acquisition over the intended follow-up period, and investigators may wish to understand how much certain types of predictors, such as behavioral factors, contribute toward overall predictiveness. Time-to-event outcomes such as time to HIV acquisition are often subject to right censoring, and existing methods for assessing variable importance are typically not intended to be used in this setting. We describe a broad class of algorithm-agnostic variable importance measures for prediction in the context of survival data. We propose a nonparametric efficient estimation procedure that incorporates flexible learning of nuisance parameters, yields asymptotically valid inference, and enjoys double-robustness. We assess the performance of our proposed procedure via numerical simulations and analyze data from the HVTN 702 vaccine trial to inform enrollment strategies for future HIV vaccine trials.

Estimation of Recovery Efficiency in High‐Temperature Aquifer Thermal Energy Storage Considering Buoyancy Flow

AbstractWith their high storage capacity and energy efficiency as well as the compatibilities with renewable energy sources, high‐temperature aquifer thermal energy storage (HT‐ATES) systems are frequently the target today in the design of temporally and spatially balanced and continuous energy supply systems. The inherent density‐driven buoyancy flow is of greater importance with HT‐ATES, which may lead to a lower thermal recovery efficiency than conventional low‐temperature ATES. In this study, the governing equations for HT‐ATES considering buoyancy flow are nondimensionalized, and four key dimensionless parameters regarding thermal recovery efficiency are determined. Then, using numerical simulations, recovery efficiency for a sweep of the key dimensionless parameters for multiple cycles and storage volumes is examined. Ranges of the key dimensionless parameters for the three displacement regimes, that is, a buoyancy‐dominated regime, a conduction‐dominated regime, and a transition regime, are identified. In the buoyancy‐dominated regime, recovery efficiency is mainly correlated to the ratio between the Rayleigh number and the Peclet number. In the conduction‐dominated regime, recovery efficiency is mainly correlated to the product of a material‐related parameter and the Peclet number. Multivariable regression functions are provided to estimate recovery efficiency using the dimensionless parameters. The recovery efficiency estimated by the regression function shows good agreement with the simulation results. Additionally, well screen designs for optimizing recovery efficiency at various degrees of intensity of buoyancy flow are investigated. The findings of this study can be used for a quick assessment and characterization of the potential HT‐ATES systems based on the geological and operational parameters.