Linear Estimation Research Articles

Variable vertical land motion and its impacts on sea level rise projections.

Coastal vertical land motion (VLM), including uplift and subsidence, can greatly alter relative sea level projections and flood mitigations plans. Yet, current projection frameworks, such as the IPCC Sixth Assessment Report, often underestimate VLM by relying on regional linear estimates. Using high-resolution (90-meter) satellite data from 2015 to 2023, we provide local VLM estimates for California and assess their contribution to sea level rise both now and in future. Our findings reveal that regional estimates substantially understate sea level rise in parts of San Francisco and Los Angeles, projecting more than double the expected rise by 2050. Additionally, temporally variable (nonlinear) VLM, driven by factors such as hydrocarbon and groundwater extraction, can increase uncertainties in 2050 projections by up to 0.4 meters in certain areas of Los Angeles and San Diego. This study highlights the critical need to include local VLM and its uncertainties in sea level rise assessments to improve coastal management and ensure effective adaptation efforts.

THE IMPACT OF ESG PRACTICES ON THE RISK PORTFOLIO OF LISTED OIL AND GAS FIRMS IN NIGERIA USING A MULTILAYERED CRITERION

This study investigates the impact of Environmental, Social, and Governance (ESG) factors on the risk-adjusted returns of Nigerian oil and gas firms listed on the Nigerian Exchange Group (NGX) over a 11-year period (2012–2022). The study was anchored on signalling theory. Utilizing a correlational research design, data was collected from eight firms meeting inclusion criteria, focusing on ESG scores as independent variables, with Firm Size as a control variable, and risk-adjusted returns as the dependent variable. Diagnostic tests ensured adherence to Best Linear Unbiased Estimator (BLUE) assumptions. Employing both Ordinary Least Squares (OLS) and Two-Stage Least Squares (2SLS) regression techniques, the study addresses potential endogeneity, using industry norms as an instrumental variable (IV) in the 2SLS model. Findings indicate significant, positive relationships between ESG factors and risk-adjusted returns, emphasizing the financial viability of sustainable practices in a sector known for environmental and social risks. Hence, to strengthen financial and operational resilience, Nigerian oil and gas firms are encouraged to prioritize robust environmental practices, including emission reduction, waste management, and prevention of oil spills. Given the social challenges in regions like the Niger Delta, firms should focus on building trust and maintaining positive relationships with local communities through initiatives in healthcare, education, and infrastructure. This study provides key insights into how ESG engagement in Nigerian oil and gas firms may influence firm stability, resilience, and investor confidence, underscoring the role of signalling theory in linking ESG performance to enhanced corporate valuation.

A Novel Goodness-of-Fit Test for Lifetime Models Based on Type II Censored Data

In this paper, a novel test statistic is introduced to evaluate the goodness of fit of lifetime distributions when dealing with Type II censored data. The test statistic is derived from a local linear regression-based estimator of the Kullback–Leibler information. Extensive analysis is conducted to examine the properties of this test statistic, highlighting its nonnegative nature, as for KL information. The proposed test statistic is applied to various distributions, namely exponential, Weibull, log-normal, and Pareto. Critical values and Type I error rates for the tests are determined, demonstrating their exceptional accuracy and reliability. To further assess their performance, the proposed tests are subjected to Monte Carlo simulations, comparing their power values against alternative tests currently in use. Finally, the proposed method is applied to three real data sets from the engineering reliability aspect to prove their practical versatility.

Effect of Dietary Protein on Fat-Free Mass in Energy Restricted, Resistance-Trained Individuals: An Updated Systematic Review With Meta-Regression

ABSTRACT Individuals often restrict energy intake to lose fat mass (and body mass [BM]) while performing resistance training (RT) to retain fat-free mass (FFM). Therefore, the aim of the present systematic review with meta-regression was to explore (a) the pattern and strength of the dose-response relationship between daily dietary protein intake and FFM change, and (b) whether intervention duration, energy deficit magnitude, baseline body fat percentage (BF%), and participant sex influence this relationship. Studies were included if they involved a standardized RT protocol with nonobese, energy-restricted (experiencing fat mass loss) individuals with a minimum of 3 months RT experience. Of 916 retrieved studies, data were extracted from a total of 29 studies. Bayesian methods were used to fit linear and nonlinear meta-regression models and estimate effect sizes, highest density credible intervals, and probabilities. Results suggest a >97% probability of a linear dose-response relationship between daily protein intake [g/kgBM: β = 0.07 (95% highest density interval [HDI]: −0.01 to 0.14), and g/kg/FFM: β = 0.06 (95% HDI: 0.01 to 0.12)] and favorable FFM changes. The relationship is stronger when protein intake is expressed relative to FFM, in interventions longer than 4 weeks, in men, and when BF% is lower. Overall, the heterogeneity between studies renders our findings exploratory.

Uncovering Dynamical Equations of Stochastic Decision Models Using Data-Driven SINDy Algorithm.

Decision formation in perceptual decision making involves sensory evidence accumulation instantiated by the temporal integration of an internal decision variable toward some decision criterion or threshold, as described by sequential sampling theoretical models. The decision variable can be represented in the form of experimentally observable neural activities. Hence, elucidating the appropriate theoretical model becomes crucial to understanding the mechanisms underlying perceptual decision formation. Existing computational methods are limited to either fitting of choice behavioral data or linear model estimation from neural activity data. In this work, we made use of sparse identification of nonlinear dynamics (SINDy), a data-driven approach, to elucidate the deterministic linear and nonlinear components of often-used stochastic decision models within reaction time task paradigms. Based on the simulated decision variable activities of the models and assuming the noise coefficient term is known beforehand, SINDy, enhanced with approaches using multiple trials, could readily estimate the deterministic terms in the dynamical equations, choice accuracy, and decision time of the models across a range of signal-to-noise ratio values. In particular, SINDy performed the best using the more memory-intensive multitrial approach while trial averaging of parameters performed more moderately. The single-trial approach, although expectedly not performing as well, may be useful for real-time modeling. Taken together, our work offers alternative approaches for SINDy to uncover the dynamics in perceptual decision making and, more generally, for first-passage time problems.

Multifidelity uncertainty quantification for ice sheet simulations

Ice sheet simulations suffer from vast parametric uncertainties, such as the basal sliding boundary condition or geothermal heat flux. Quantifying the resulting uncertainties in predictions is of utmost importance to support judicious decision-making, but high-fidelity simulations are too expensive to embed within uncertainty quantification (UQ) computations. UQ methods typically employ Monte Carlo simulation to estimate statistics of interest, which requires hundreds (or more) of ice sheet simulations. Cheaper low-fidelity models are readily available (e.g., approximated physics, coarser meshes), but replacing the high-fidelity model with a lower fidelity surrogate introduces bias, which means that UQ results generated with a low-fidelity model cannot be rigorously trusted. Multifidelity UQ retains the high-fidelity model but expands the estimator to shift computations to low-fidelity models, while still guaranteeing an unbiased estimate. Through this exploitation of multiple models, multifidelity estimators guarantee a target accuracy at reduced computational cost. This paper presents a comprehensive multifidelity UQ framework for ice sheet simulations. We present three multifidelity UQ approaches—Multifidelity Monte Carlo, Multilevel Monte Carlo, and the Best Linear Unbiased Estimator—that enable tractable UQ for continental-scale ice sheet simulations. We demonstrate the techniques on a model of the Greenland ice sheet to estimate the 2015-2050 ice mass loss, verify their estimates through comparison with Monte Carlo simulations, and give a comparative performance analysis. For a target accuracy equivalent to 1 mm sea level rise contribution at 95 % confidence, the multifidelity estimators achieve computational speedups of two orders of magnitude.

A comparison of shrinkage estimators of the cosmological precision matrix

Abstract The determination of the covariance matrix and its inverse, the precision matrix, is critical in the statistical analysis of cosmological measurements. The covariance matrix is typically estimated with a limited number of simulations at great computational cost before inversion into the precision matrix; therefore, it can be ill-conditioned and overly noisy when the sample size n used for estimation is not much larger than the data vector dimension. In this work, we consider a class of methods known as shrinkage estimation for the precision matrix, which combines an empirical estimate with a target that is either analytical or stochastic. These methods include linear and non-linear shrinkage applied to the covariance matrix (the latter represented by the so-called NERCOME estimator), and the direct linear shrinkage estimation of the precision matrix which we introduce in a cosmological setting. By performing Bayesian parameter inference and using metrics like matrix loss functions, the Kullback–Leibler divergence and the eigenvalue spectrum, we compare their performance against the standard sample estimator with varying sample size n. We have found the shrinkage estimators to significantly improve the posterior distribution at low n, especially for the linear shrinkage estimators either inverted from the covariance matrix or applied directly to the precision matrix, with an empirical target constructed from the sample estimate. Our results are particularly relevant to the analyses of Stage-IV spectroscopic galaxy surveys such as the Dark Energy Spectroscopic Instrument (DESI) and Euclid, whose statistical power can be limited by the computational cost of obtaining an accurate precision matrix estimate.

Association between age at loss of ambulation and cardiac function in adults with Duchenne muscular dystrophy.

Cardiomyopathy is a common co-morbidity in individuals with Duchenne muscular dystrophy (DMD). This retrospective single centre study investigated the relationship between age at loss of ambulation (LOA) and late stage left ventricular ejection fraction (LVEF) in 84 individuals (> 16 years old) with DMD taking glucocorticoid and ACE inhibitors treatment. Regression analyses showed a positive correlation between later age at LOA and higher LVEF in adulthood (linear regression estimate 1.49, 95 % CI: 0.13-2.84, p = 0.03). Each additional year of ambulation increased the odds of displaying a higher LVEF category (LVEF 40 %, 40 - 50 % or 50 %) by 35 % (p = 0.003). Sensitivity models excluding cardioprotective genotypes (absence of Dp116 isoform) and mild motor phenotypes (out of frame deletions amenable to skip exon 44 and 45) confirmed this association while models including age at respiratory impairment did not improve the model. Individuals who lost ambulation before age 11.92 (ROC AUC 0.73, 95 % CI: 0.60-0.85) reached a LVEF <40 % 5.21 years earlier than those who lost ambulation after that age (adjusted restricted mean survival time 19.08 vs 24.29 years, p < 0.001). These findings may suggest that prolonging ambulation does not impact cardiac function adversely in advance stages of DMD.

Short-term wind forecasting via surface pressure measurements: Stochastic modeling and sensor placement

We propose a short-term wind forecasting framework for predicting real-time variations in atmospheric turbulence based on nacelle-mounted anemometer and ground-level air-pressure measurements. Our approach combines linear stochastic estimation and Kalman filtering algorithms to assimilate and process real-time field measurements with the predictions of a stochastic reduced-order model that is confined to a two-dimensional plane at the hub height of turbines. We bridge the vertical gap between the computational plane of the model at hub height and the measurement plane on the ground using a projection technique that allows us to infer the pressure in one plane from the other. Depending on the quality of this inference, we show that customized variants of the extended and ensemble Kalman filters can be tuned to balance estimation quality and computational speed 1–1.5 diameters ahead and behind leading turbines. In particular, we show how synchronizing the sign of estimates with that of velocity fluctuations recorded at the nacelle can significantly improve the ability to follow temporal variations upwind of the leading turbine. We also propose a convex optimization-based framework for selecting a subset of pressure sensors that achieve a desired level of accuracy relative to the optimal Kalman filter that uses all sensing capabilities.

Chromosome 1 QTLs associated with response to bacterial leaf spot in Beta vulgaris

AbstractBacterial leaf spot (BLS), caused by Pseudomonas syringae pathovar aptata (Psa), is a seedborne, foliar disease affecting members of the Amaranthaceae and Cucurbitaceae families, including table beet and Swiss chard crops. There is no known resistance to BLS in beet or chard. A diversity panel, modified from the Wisconsin Beta Diversity Panel (WBDP) and comprised of 219 accessions from the Beta vulgaris crop complex, was assembled and genotyped for single nucleotide polymorphism data. These accessions were screened by foliar inoculation of Psa and visually evaluated for percentage of diseased leaf tissue. Overall, sugar beet and Beta vulgaris subsp. maritima accessions had the lowest BLS response, whereas table beet accessions had the largest range of responses. Phenotypic means were adjusted using best linear unbiased estimates, and two different software programs, GWASpoly and GAPIT3, were utilized to conduct a genome‐wide association study (GWAS). Leaf color was found to be significantly associated with and correlated with BLS response scores, and was used as a covariate in GWAS analysis. An association with BLS response was detected on chromosome 1 in the full WBDP, explaining upward of 21% of the variation in the phenotype. The marker associated with this quantitative trait locus (QTL), Chr1_61344476, showed an additive relationship between dosage and BLS response. Eleven candidate genes, described and annotated in sugar beet, were associated with this QTL. Some of these include F Box domains, RNA‐binding proteins, and calcium‐dependent kinases, all of which have roles in plant defense responses. Marker Chr1_61344476 may be useful in breeding for BLS resistance in members of the Beta vulgaris crop complex.

Introduction of curvature index and improvement of calibration curves used in ion chromatography (IC), atomic absorption spectrometry (AAS), UV-Vis spectrophotometry and catalytic combustion

The calibration curve represents the relationship between known CRM and the response of the instrument (e.g absorbance). Most of the time, in analytical chemistry standards, the linear function (of the 1st degree) is used to characterize the calibration curves. This, at first glance, has some major advantages: simple equation, simple linearity estimation criterion, and easy way of calculation. However, from a quality point of view, the grade 1 calibration curve does not characterize a working domain very well, because it does not take into account the curvature and has larger residuals. Here we show that, most of the time, the non-linear secondary function fits the calibration curves much better (argument valid for UV-Vis spectrophotometry, atomic absorption spectrometry (FAAS and ETAAS), catalytic combustion and ion chromatography (IC)), having lower residuals, but also a higher coefficient of determination, R2. The limitation of the secondary non-linear function use is given by the insufficiency of data related to its qualitative characterization. Through this work, we have introduced new characterization criteria for non-linear secondary functions such as curvature angle and curvature index. Although in the specialized literature, the term linearity of the calibration curve has existed for a long time, in reality it has not been fully interpreted. The curvature index developed in this work allows the controlled implementation of non-linear curves, but also with increasing the precision of the results. The curvature index characterizes the degree of curvature of the 1st or 2nd degree calibration functions. Using the 2nd degree calibration function allows the improvement of the obtained results, and curvature parameters (curvature angle and curvature index) allow qualitative characterization of many data sets used for calibration. The degree of curvature of a calibration curve is a new term not found in the literature, which will allow their characterization from a qualitative point of view.

Building Detection and Outlining in Multi-Modal Remote Sensor Data: A Stratified Approach

Exploitation of aerial sensor data for vectorized representation of urban structures is of great importance for urban modeling. Even if elevation data is available, extracting complex and irregular building shapes would become challenging. In this paper, we present a stratified approach for the extraction of building outlines consisting of two steps: Classification and vectorization. For classification, we use training data transfer to evaluate a dataset with no or little labeled data. Since the available reference data is also flawed, we use a self-developed interactive tool to adjust and improve the building shape before contrasting it with the classification results. Initial building polygons are slightly generalized and refined considering building shape characteristics. Hereby, Least-Squares adjustment is implemented to solve the best-fit problem of building edges with the input data by applying the Gauss-Helmert and Gauss-Markov Models. On the raster level, the resulting polygons achieved an accuracy of over 99% in the Potsdam dataset and almost 98% in the Munich dataset while on the vector level, the median building-wise deviations lie in sub-meter range. Although there were few mis-detections and, sometimes, considerable peak deviations for the Hausdorff distance, the compelling qualitative results attest to the robustness and validity of the proposed procedure.

Overconfidence, managerial accounting competence and dividend smoothing: evidence from emerging markets

PurposeThe purpose of this research is to review the dividend smoothing effectiveness from the perspective of managers' overconfidence and accounting competence. Accounting competence is considered as an important factor in recognizing management’s ability to override internal controls as an opportunity to distort financial reports.Design/methodology/approachThe current study applies multivariable linear regression method estimator to investigate the relationship between Overconfidence, Managerial Accounting Competence and Dividend Smoothing of 1,320 firm-year observations in Iran for the period of 2012–2022.FindingsThe result show that manager overconfidence leads to dividend smoothing. Moreover, this relation is stronger in low information quality and not driven by high information quality or by others measures. This research show that accounting competence has led to positive change in the efficiency of the manager performance and reduce the self-interesting motives of manager.Practical implicationsIn the present study, the weaknesses caused by the ambiguity of capital market efficiency in market performance-based statistical models are compensated and partially covered by classifying the relationships and implementing models in each group. Results obtained from this study will aid market practitioners to evaluate the firms’ dividend smoothing. The results provide evidence and information for policymakers and investors about the theoretical gap and the factors affecting to it. It also informs policymakers to the dividend smoothing associated with the manager characteristics.Originality/valueThe previous researches emphasize on limiting agency costs by creating limits for the optimistic actions of managers, while framing and standardizing a major part of management behaviors is not possible. In this research, the manager’s executive ability has been examined in the form of accounting competence at the same time as excessive self-confidence, in order to control part of the inherent limitations caused by the managers' behavior. This study also considers the positive aspect of managers' ability in the form of accounting competence.

Laser Phase Noise Compensation Method Based on Dual Reference Channels in Inverse Synthetic Aperture Lidar

Laser phase noise is a critical factor that limits the range and performance of coherent lidar systems, especially in high-resolution applications such as inverse synthetic aperture lidar (ISAL), which demands stringent coherence. The effective suppression of laser phase noise is essential to enable high-resolution imaging over long distances. This paper presents a phase noise compensation technique utilizing dual reference channels (DRCs) based on concatenated generated phase (CGP) principles. The proposed method uses two reference channels with different delay lengths: a long-delay channel for coarse phase noise compensation and a short-delay channel for fine adjustments. We performed ISAL imaging experiments on stationary and rotating targets using a seed laser with a 3.41 MHz linewidth, achieving round-trip distances exceeding 110 times the laser coherence length. Imaging quality closely matched a 100 Hz narrow linewidth laser, approaching theoretical resolution limits. Compared to prior methods based on residual error linear estimation, the DRC method enhances compensation speed tenfold while maintaining accuracy. These results highlight the efficacy of the proposed DRC method in mitigating laser phase noise, significantly improving ISAL imaging performance.

Artificial Neural Network Regression Modelling of Poverty Index in Nigeria Artificial Neural Network Regression Modelling

Due to the benefits of Artificial Neural Network (ANN) regression modelling over classical linear regression estimator with respect to faulty tolerance and generalization ability, the study adopted (ANN) regression modelling in order to investigate the impacts of economic variables indices on the poverty index of Nigeria in the years 2018/2019, artificial neural network regression modelling was adopted. This study examined poverty modelling in the realm of (ANN) regression and showcased the contribution of the weight of each predictor variable towards the nodes that determine the Multidimensional Poverty Index (MPI). Most literatures do not interpret the weights and bias of ANN regression, they only described the architecture of the procedures to obtain it. This is the gap this study filled. The study observed that Food insecurity has the highest relative importance with 0.085 magnitude to (MPI) while sanitation has lowest relative importance with magnitude of 0.045 to (MPI).

A higher-order quadratic NLS equation on the half-line

The well-posedness of the initial-boundary value problem for higher-order quadratic nonlinear Schrödinger equations on the half-line is studied by utilizing the Fokas solution formula for the corresponding linear problem. Using this formula, linear estimates are derived in Bourgain spaces for initial data in spatial Sobolev spaces on the half-line and boundary data in temporal Sobolev spaces suggested by the time regularity of the linear initial value problem. Then, the needed bilinear estimates are derived and used for showing that the iteration map defined via the Fokas solution formula is a contraction in appropriate solution spaces. Finally, well-posedness is established for optimal Sobolev exponents in a way analogous to the case of the initial value problem on the whole line with solutions in classical Bourgain spaces.

On The Pros and Cons of Using Excel for Regression Analysis

Excel is a useful and powerful software for simple regression analysis without any programming skills and that is why, it is widely preferred by the undergraduate students from various areas such as chemistry, biology and agriculture as well as many engineering disciplines such as chemical engineering, food engineering and bioengineering. Parameter values and coefficient of determination (R2) can be easily obtained together with the graphical representation for those models exist in Excel such as linear and exponential models. It is also possible to visually examine the model fit and experimental data together on the same graph. For linear models (linear in parameters) Excel Add-In Data Analysis-Regression tool creates a summary output, and parameter estimates, parameter uncertainties, adjusted R2 (R2adj) and root mean square error (RMSE) values can be found even for the models that do not exist in Excel. For nonlinear models (nonlinear in parameters) Excel Add-In Solver tool can be used to obtain parameter estimates (but not uncertainties), and R2, R2adj and RMSE can be calculated manually. Despite these advantages, there are some shortcomings of Excel for regression analysis. For linear models with no-intercept Excel reports the incorrect and overoptimistic R2 and also reports incorrect and overpessimistic R2adj. Excel has also some nonlinear models such as exponential and power models in it; however, Excel computes linear parameter estimates for those nonlinear models and again optimistic R2 is calculated. This paper aims (i) to show these inaccuracies with their reasons by using published data, (ii) how to obtain the correct results, (iii) to warn the instructors who would use Excel for regression analysis in class. Excel users (instructors, students, professionals in any field) should be aware of the pitfalls when using Excel for regression.

Estimation for time-varying coefficient smoothed quantile regression

Time-varying coefficient regression is commonly used in the modeling of nonstationary stochastic processes. In this paper, we consider a time-varying coefficient convolution-type smoothed quantile regression (conquer). The covariates and errors are assumed to belong to a general class of locally stationary processes. We propose a local linear conquer estimator for the varying-coefficient function, and obtain the global Bahadur–Kiefer representation, which yields the asymptotic normality. Furthermore, statistical inference on simultaneous confidence bands is also studied. We investigate the finite-sample performance of the conquer estimator and confirm the validity of our asymptotic theory by conducting extensive simulation studies. We also consider financial volatility data as an example of a real-world application.

Comparison of household work intensity in Slovakia and Czechia through least squares means analysis based on GLM

The work intensity (WI) of a household is primarily monitored in order to identify (quasi-)jobless (QJ) households. QJ households are those households whose members use less than 20% of their work potential. Individuals in such households, together with income-poor and severely materially and socially deprived persons are included in the Europe 2030 Strategy as socially excluded who need to be targeted by social policies. The aim of the paper is to assess the impact of relevant factors and their interactions on the WI of households in Slovakia and Czechia. For this purpose, general linear models, contrast analysis and estimates of marginal means are employed. The presented analyses are based on the EU-SILC 2021 survey and carried out separately for Slovakia and Czechia. The paper reveals the common and different features of these countries in terms of the WI of households. Particular attention is devoted to the identification of the profiles of persons at high risk of living in QJ households

Stress-Strength Reliability of Two-Parameter Exponential Distribution Based on Progres- sively Type-II Censored Data

Introduction: Stress-strength models has achieved considerable attention in recent years due to its applicability in various areas like engineering, quality control, biology, genetics, medicine etc. This paper investigates estimation of the stress-strength reliability parameter R=P(Y&lt;X) in two-parameter exponential distributions under progressively type-II censored samples. Methods: The maximum likelihood and the best linear unbiased estimates of P(Y&lt;X) are obtained, and the Bayes estimates of P(Y&lt;X) are computed under the squared error, linear-exponential, and Stein loss functions. Also, confidence intervals of stress-strength reliability such as the bootstrap confidence intervals, highest posterior density credible interval, and confidence interval based on the generalized pivotal quantity are obtained. Results: Using a simulation study, the point estimators and confidence intervals are evaluated and compared. A set of real data is presented for better clarification of the issue. Conclusion: The results demonstrated that with increasing the sample size, in almost cases the estimated risk of all the estimators decrease. Also, in almost all cases the Bayes estimator under the linear-exponential loss function has smaller estimated risk than the other estimators. Based on our simulation, the expected lengths of all intervals tend to decrease when the sample size increases. Moreover, the highest posterior density confidence intervals are shorter than the others intervals for all the values of P(Y&lt;X).