Coefficient Estimates Research Articles

Towards achieving net zero emission targets and sustainable development goals, can long-term material footprint strategies be a useful tool?

This study analyzes material footprint (MF), which can be essential in achieving net zero emission targets and sustainable development goals for EURO-26 countries. Increasing the efficiency of MF rather than domestic material consumption is more effective in reducing emissions. Therefore, this study examines the relationship between MF, economic growth, and CO2 emissions for EURO-26 countries. For empirical analysis, second-generation panel cointegration tests and long-term coefficient estimators, which consider the cross-sectional dependence, are employed. The empirical results indicate that (i) there is a long-term relationship between the variables and (ii) MF increases the CO2 emissions. However, the positive relationship between economic growth and CO2 emissions is statistically insignificant. According to the individual results, while the impact of MF on CO2 emissions is negative in developed countries, MF increases CO2 emissions in developing countries in general. Overall findings reveal that long-term material footprint strategies should be implemented in EURO-26 countries and material footprint policies can be used as a strategic tool to achieve net zero emission targets and sustainable development goals (SDGs).

Two-Way Truncated Linear Regression Models with Extremely Thresholding Penalization

This article introduces a new type of linear regression model with regularization. Each predictor is conditionally truncated through the presence of unknown thresholds. The new model, called the two-way truncated linear regression model (TWT-LR), is not only viewed as a nonlinear generalization of a linear model but is also a much more flexible model with greatly enhanced interpretability and applicability. The TWT-LR model performs classifications through thresholds similar to the tree-based methods and conducts inferences that are the same as the classical linear model on different segments. In addition, the innovative penalization, called the extremely thresholding penalty (ETP), is applied to thresholds. The ETP is independent of the values of regression coefficients and does not require any normalizations of regressors. The TWT-LR-ETP model detects thresholds at a wide range, including the two extreme ends where data are sparse. Under suitable conditions, both the estimators for coefficients and thresholds are consistent, with the convergence rate for threshold estimators being faster than n . Furthermore, the estimators for coefficients are asymptotically normal for fixed dimension p. It is demonstrated in simulations and real data analyses that the TWT-LR-ETP model illustrates various threshold features and provides better estimation and prediction results than existing models. Supplementary materials for this article are available online.

Global-Scale Evaluation of XCO2 Products from GOSAT, OCO-2 and CarbonTracker Using Direct Comparison and Triple Collocation Method

Triple collocation (TC) shows potential in estimating the errors of various geographical data in the absence of the truth. In this study, the TC techniques are first applied to evaluate the performances of multiple column-averaged dry air CO2 mole fraction (XCO2) estimates derived from the Greenhouse Gases Observing Satellite (GOSAT), the Orbiting Carbon Observatory 2 (OCO-2) and the CarbonTracker model (CT2019B) at a global scale. A direct evaluation with the Total Carbon Column Observing Network (TCCON) measurements is also employed for comparison. Generally, the TC-based evaluation results are consistent with the direct evaluation results on the overall performances of three XCO2 products, in which the CT2019B performs best, followed by OCO-2 and GOSAT. Correlation coefficient estimates of the TC show higher consistency and stronger robustness than root mean square error estimates. TC-based error estimates show that most of the terrestrial areas have larger error than the marine areas overall, especially for the GOSAT and CT2019B datasets. The OCO-2 performs well in areas where CT2019B or GOSAT have large errors, such as most of China except the northwest, and Russia. This study provides a reference for characterizing the performances of multiple CO2 products from another perspective.

Non-Expert Markings of Active Chronic Graft-Versus-Host Disease Photographs: Optimal Metrics of Training Effects.

Lack of reliable measures of cutaneous chronic graft-versus-host disease (cGVHD) remains a significant challenge. Non-expert assistance in marking photographs of active disease could aid the development of automated segmentation algorithms, but validated metrics to evaluate training effects are lacking. We studied absolute and relative error of marked body surface area (BSA), redness, and the Dice index as potential metrics of non-expert improvement. Three non-experts underwent an extensive training program led by a board-certified dermatologist to mark cGVHD in photographs. At the end of the 4-month training, the dermatologist confirmed that each trainee had learned to accurately mark cGVHD. The trainees' inter- and intra-rater intraclass correlation coefficient estimates were "substantial" to "almost perfect" for both BSA and total redness. For fifteen 3D photos of patients with cGVHD, the trainees' median absolute (relative) BSA error compared to expert marking dropped from 20 cm2 (29%) pre-training to 14 cm2 (24%) post-training. Total redness error decreased from 122 a*·cm2 (26%) to 95 a*·cm2 (21%). By contrast, median Dice index did not reflect improvement (0.76 to 0.75). Both absolute and relative BSA and redness errors similarly and stably reflected improvements from this training program, which the Dice index failed to capture.

Hankel and Symmetric Toeplitz Determinants for a New Subclass of q-Starlike Functions

This paper considers the basic concepts of q-calculus and the principle of subordination. We define a new subclass of q-starlike functions related to the Salagean q-differential operator. For this class, we investigate initial coefficient estimates, Hankel determinants, Toeplitz matrices, and Fekete-Szegö problem. Moreover, we consider the q-Bernardi integral operator to discuss some applications in the form of some results.

A new test for market efficiency and uncovered interest parity

We suggest a new single-equation test for Uncovered Interest Parity () based on a dynamic regression approach. The method provides consistent and asymptotically efficient parameter estimates, and is not dependent on assumptions of strict exogeneity. This new approach is asymptotically more efficient than the common approach of using OLS with HAC robust standard errors in the static forward premium regression. The coefficient estimates when spot return changes are regressed on the forward premium are all positive and remarkably stable across currencies. These estimates are considerably larger than those of previous studies, which frequently find negative coefficients. The method also has the advantage of showing dynamic effects of risk premia, or other events that may lead to rejection of UIP or the efficient markets hypothesis.

ODP089 Familial correlation and heritability of muscle strength in Korean adults (KNHANES 2014-2019)

Abstract Objective The onset and progression of sarcopenia are highly variable between individuals due to genetic and environmental factors. However, studies that have measured muscle strength directly in large numbers of matched parent-adult offspring pairs are limited. In this study, we investigated familial correlation and heritability of handgrip strength (HGS) in Korean adults. Methods This was a family-based cohort study from the Korea National Health and Nutrition Examination Survey (2014-2019) including 5,004 Koreans aged ≥19 years from 1,527 families. HGS was measured using a digital grip strength dynamometer. Familial correlations for HGS were calculated in different types of relative pairs. Variance component methods were used to obtain heritability estimates. Results Mean HGS of parents were 39.3 ± 7.5 kg in fathers and 24.4 ± 5. 0 kg in mothers and those of offspring were 41.8 ± 8. 0 kg in sons and 24. 0 ± 4.8 kg in daughters. Correlation coefficient estimates between parent-offspring pairs, sibling pairs, and spouse pairs for HGS were significant as 0. 07, 0.10, and 0.23 (p <0. 001, p = 0. 041, and p <0. 001), respectively. The total variance of the phenotype under study was explained by additive genetic (15.4%), common shared environmental (11. 0%), and unique environmental (73.6%) influences. The risk of low HGS conferred by parents’ low HGS were found to be significant for individuals whose parents had low HGS (OR 3.10, 95%CI 1.14-8.45). Conclusion Muscle strength showed a familial correlation in Korean adults. A significant heritable tendency of muscle strength from parents to their adult offspring and common shared environmental influence can explain this correlation. Presentation: No date and time listed

Uniformly convex and starlike functions related with Liu-Owa integral operator

In this present paper, we introduce and explore certain new classes of uniformly convex and starlike functions related with Liu-Owa integral operator. We study explore various properties and characteristics, such as coefficient estimates, growth rate function property and partial sums. It is important to mention that our results are generalization of number of existing results in literature.

Applications of the q-Derivative Operator to New Families of Bi-Univalent Functions Related to the Legendre Polynomials

By using the q-derivative operator and the Legendre polynomials, some new subclasses of q-starlike functions and bi-univalent functions are introduced. Several coefficient estimates and Fekete–Szegö-type inequalities for functions in each of these subclasses are obtained. The results derived in this article are shown to extend and generalize those in some earlier works.

Estimation and Classification of NLFM Signals Based on the Time-Chirp Representation.

A new approach to the estimation and classification of nonlinear frequency modulated (NLFM) signals is presented in the paper. These problems are crucial in electronic reconnaissance systems whose role is to indicate what signals are being received and recognized by the intercepting receiver. NLFM signals offer a variety of useful properties not available for signals with linear frequency modulation (LFM). In particular, NLFM signals can ensure the desired reduction of sidelobes of an autocorrelation (AC) function and desired power spectral density (PSD); therefore, such signals are more frequently used in modern radar and echolocation systems. Due to their nonlinear properties, the discussed signals are difficult to recognize and therefore require sophisticated methods of analysis, estimation and classification. NLFM signals with frequency content varying with time are mainly analyzed by time-frequency algorithms. However, the methods presented in the paper belong to time-chirp domain, which is relatively rarely cited in the literature. It is proposed to use polynomial approximations of nonlinear frequency and phase functions describing signals. This allows for applying the cubic phase function (CPF) as an estimator of phase polynomial coefficients. Originally, the CPF involved only third-order nonlinearities of the phase function. The extension of the CPF using nonuniform sampling is used to analyse the higher order polynomial phase. In this paper, a sixth order polynomial is considered. It is proposed to estimate the instantaneous frequency using a polynomial with coefficients calculated from the coefficients of the phase polynomial obtained by CPF. The determined coefficients also constitute the set of distinctive features for a classification task. The proposed CPF-based classification method was examined for three common NLFM signals and one LFM signal. Two types of neural network classifiers: learning vector quantization (LVQ) and multilayer perceptron (MLP) are considered for such defined classification problem. The performance of both the estimation and classification processes was analyzed using Monte Carlo simulation studies for different SNRs. The results of the simulation research revealed good estimation performance and error-free classification for the SNR range encountered in practical applications.

Adaptive control of dual-motor autonomous steering system for intelligent vehicles via Bi-LSTM and fuzzy methods

In order to achieve better trajectory tracking of intelligent vehicle on different road surfaces, which include underling the weather of rain and snow, an adaptive tire cornering stiffness strategy (ACS) and a trajectory tracking autonomous steering control strategy were proposed in this paper. Firstly, the tire–road friction coefficient estimator was designed to obtain the tire–road friction coefficient based on Bi-LSTM neural network, then combining the tire cornering stiffness coefficient by fuzzy controller to obtain the online tire cornering stiffness. Secondly, considering the uncertainty of tire cornering stiffness, a trajectory tracking upper-level controller based on model predictive control (MPC) algorithm was proposed to calculate the optimal front wheel angle. Thirdly, in order to better adapt to various road conditions, a dynamic equivalent stiffness model is proposed to replace the self-aligning torque model during vehicle steering, and a lower-level controller for pinion gear target position tracking was designed based on the sliding mode control (SMC) algorithm. In addition, this paper also designed a dual-motor control strategy, which cooperates with the pinion gear target position tracking lower-level controller to quickly and accurately achieve the target front wheel angle. Finally, the simulation and HiL test results show that the proposed control algorithm greatly improves the trajectory tracking accuracy and stability of the intelligent vehicle on roads with different tire–road friction coefficients.

Identifying brain hierarchical structures associated with Alzheimer's disease using a regularized regression method with tree predictors.

Brain segmentation at different levels is generally represented as hierarchical trees. Brain regional atrophy at specific levels was found to be marginally associated with Alzheimer's disease outcomes. In this study, we propose an ℓ1 -type regularization for predictors that follow a hierarchical tree structure. Considering a tree as a directed acyclic graph, we interpret the model parameters from a path analysis perspective. Under this concept, the proposed penalty regulates the total effect of each predictor on the outcome. With regularity conditions, it is shown that under the proposed regularization, the estimator of the model coefficient is consistent in ℓ2 -norm and the model selection is also consistent. When applied to a brain sMRI dataset acquired from the Alzheimer's Disease Neuroimaging Initiative (ADNI), the proposed approach identifies brain regions where atrophy in these regions demonstrates the declination in memory. With regularization on the total effects, the findings suggest that the impact of atrophy on memory deficits is localized from small brain regions, but at various levels of brain segmentation. Data used in preparation of this paper were obtained from the ADNIdatabase.

Quantifying Total and Out-of-Pocket Costs Associated With Head and Neck Cancer Survivorship

Oncologic treatment is costly to the health care system and to individuals, but patients with head and neck cancer (HNC) also have long-term care needs after treatment. Survivors of HNC require specific consideration given their rapidly growing numbers. This subpopulation of cancer survivors often experiences long-term treatment-associated morbidity. To describe the total and out-of-pocket (OOP) costs associated with HNC survivorship and the risk factors for financial toxicity among this population. This was a retrospective review and economic evaluation of a cohort of US adults with a diagnosis of HNC from 2006 to 2018. The study used data the from IBM MarketScan Commercial Claims Database. Data were analyzed from November 2020 to June 2022. Treatment for HNC. Total and OOP medical costs were assessed monthly and reported relative to the date of HNC diagnosis. The primary outcome was the difference between a patient's mean monthly survivorship costs (13-60 months after diagnosis) and mean monthly baseline costs (7-12 months before diagnosis). Univariate and multivariable linear regression models were created for total and OOP costs to generate coefficient estimates with 95% CIs. The study cohort of this economic evaluation included 19 098 patients with HNC (median [range] age, 56 [18-64] years; 14 144 [74.1%] men and 4954 [25.9%] women; race and ethnicity were not considered). Throughout the survivorship period, median total and OOP costs were $372 per month and $31 per month higher than baseline costs, respectively, with variation in expenses by demographic information, health plan type, and oncologic variables. In the multivariable model, greater total and OOP excess survivorship costs were associated with female sex ($343/mo; 95% CI, $126 to $560 and $9/mo; 95% CI, $4 to $14). Highest and lowest total excess survivorship costs associated with cancer site were seen for hypopharyngeal ($1908/mo; 95% CI, $1102 to $2714) and oropharyngeal cancers (-$703/mo; 95% CI, -$967 to -$439) vs oral cavity cancers. Compared with surgery or radiation therapy alone, multimodal treatment was generally associated with excess OOP survivorship costs. The findings of this retrospective economic evaluation review suggest that the costs of HNC survivorship remain persistently elevated above baseline costs for at least 5 years after diagnosis. High survivorship costs were associated with female sex, hypopharyngeal tumors, and treatment with multimodal therapy. Practitioners should seek to minimize costs for these patients at higher-risk of financial toxicity after treatment and work to provide directed supportive services.

Evaluating spatial and temporal variations in sub-field level crop water demands

Variable rate irrigation (VRI) requires accurate knowledge of crop water demands at the sub-field level. Existing VRI practices commonly use one or more variables like soil electrical conductivity, historical yields, and topographic maps to delineate variable rate zones. However, these data sets do not quantify within season variability in crop water demands. Crop coefficients are widely used to help estimate evapotranspiration (ET) at different stages of a crop’s growth cycle, and past research has shown how remotely sensed data can identify differences in crop coefficients at regional and field levels. However, the amount of spatial and temporal variation in crop coefficients at the sub-field level (i.e. within a single center pivot system) has not been widely researched. This study aims to compare sub-field ET estimates from two remote sensing platforms and quantify spatial and temporal variations in aggregated sub-field level ET. Vegetation indices and reference ET data were collected at Kansas State University’s Southwest Research Extension Center (SWREC) and two Water Technology Farms during the 2020 corn growing season. Weekly maps of the Normalized Difference Vegetation Index (NDVI) and the Soil Adjusted Vegetation Index (SAVI) from aerial imagery are combined with empirical equations from existing literature to estimate both basal and combined crop coefficients at a 1-meter resolution. These ET estimates are aggregated to a 30 m resolution and compared to the Landsat Provisional Actual ET dataset. Finally, actual ET estimates from aerial images were aggregated using k-means clustering and stationary variable speed zones to determine if there is enough variation in actual ET at the sub-field level to build variable rate irrigation schedules. An equivalence test demonstrated that the aerial imagery and Landsat data sources produce significantly different crop coefficient estimates. However, the two datasets were moderately correlated with Pearson’s product-moment correlation coefficients ranging from -0.95 to 0.86. Both the aerial imaging and Landsat datasets showed high variability in crop coefficients during the first 5-6 weeks after emergence, with these coefficients becoming more spatially uniform later in the growing season. These crop coefficients may help irrigators make more informed irrigation management decisions during the growing season. However, more research is needed to validate these remotely sensed ET estimates and integrate them into an irrigation decision support system.

BRICS-T Ülkelerinde Ekonomik Büyüme Üzerinde Yenilenebilir Enerji Tüketiminin Etkisi

In this study, it is aimed to investigate the effect of renewable energy consumption on economic growth in BRICS countries and Turkey. In this context, panel data analysis in the form of coefficient estimates and causality analysis was applied using annual data for the period 1990-2018. Within the scope of the determinants of economic growth, besides the renewable energy consumption variable which forms the basis of the study, the variables of capital accumulation, population, trade openness, foreign direct investments and non-renewable energy consumption are also used. According to the coefficient estimation results made with the fixed effects model, it has been determined that the increase in renewable energy consumption has negative and significant effects on economic growth. On the other hand, it has been determined that the increase in non-renewable energy consumption has a positive effect. In addition, it is among the findings that the increase in capital accumulation has a positive effect on economic growth and the increase in trade openness has a negative effect. Finally, Dumitrescu-Hurlin (2012) panel causality test results revealed that there is a unidirectional causality relationship from renewable energy consumption to economic growth. Accordingly, it was seen that the growth hypothesis was valid among the variables.

Composite quantile regression for heteroscedastic partially linear varying-coefficient models with missing censoring indicators

This paper is concerned with composite quantile regression for partially linear varying-coefficient models with heteroscedasticity when data are right censored and censoring indicators are missing at random. We construct estimators of parametric regression coefficients and nonparametric varying-coefficient functions in the proposed models based on regression calibration, imputation and inverse probability weighted approaches. The asymptotic normality of the proposed estimators is proved. Meanwhile, an adaptive LASSO penalized variable selection method and its oracle property are considered. We also demonstrate the performance of the proposed estimation method and variable selection procedure through comprehensive simulations and a real-data application.

Restricted Stein-rule estimation in ultrastructural linear measurement error models

We consider a multivariate ultrastructural measurement error model when some information on regression coefficients is available in the form of linear restriction. We propose Stein-rule estimation for the regression coefficients. Without any assumption about the distribution for the random components, the asymptotic risk under a specific quadratic loss function is studied and a sufficient condition for the dominance of the proposed estimator over a consistent estimator. We also carry out a simulation study to demonstrate the finite sample properties of the estimators.

Nearly optimal Bayesian shrinkage for high-dimensional regression

During the past decade, shrinkage priors have received much attention in Bayesian analysis of high-dimensional data. This paper establishes the posterior consistency for high-dimensional linear regression with a class of shrinkage priors, which has a heavy and flat tail and allocates a sufficiently large probability mass in a very small neighborhood of zero. While enjoying its efficiency in posterior simulations, the shrinkage prior can lead to a nearly optimal posterior contraction rate and the variable selection consistency as the spike-and-slab prior. Our numerical results show that under the posterior consistency, Bayesian methods can yield much better results in variable selection than the regularization methods such as LASSO and SCAD. This paper also establishes a BvM-type result, which leads to a convenient way of uncertainty quantification for regression coefficient estimates.

Bi-univalent Function Subclasses Subordinate to Horadam Polynomials

The object of this article is to explore two subclasses of regular and bi-univalent functions subordinate to Horadam polynomials in the disk $\{\varsigma\in\mathbb{C}:|\varsigma| <1\}$. We originate upper bounds for the initial Taylor-Maclaurin coefficient estimates of functions in these subclasses. Fekete-Szeg\"o functional problem is also established. Furthermore, we present some new observations and investigate relevant connections to existing results.

Influencing factors on instrumental activities of daily living functioning in people with mild cognitive disorder – a secondary investigation of cross-sectional data

BackgroundFinding a strategy to reduce the impact of cognitive decline on everyday functioning in persons suffering from cognitive impairment is a public health priority. Instrumental activities of daily living (IADL) are key to everyday functioning. Hence, it is essential to understand the influencing factors on IADL to develop specific interventions to improve everyday functioning in persons with mild cognitive disorder. Therefore, this study aimed to 1) explore different influencing factors on IADL functioning considering all domains of the International Classification of Functioning, disability, and health and 2) rank these factors.MethodsWe performed a secondary analysis of a cohort including participants with amnestic mild cognitive impairment (a-MCI) or mild Alzheimer’s Dementia (mild AD). The IADL functioning model was used as a starting point to estimate the effects of cognitive and physical function factors and personal and environmental factors on IADL functioning using multiple linear regression analysis, including subgroup analysis in persons with a-MCI. We used standardized coefficient estimates to relate the size of the predictor effects in the final model.ResultsWe included 105 participants (64 a-MCI, 41 mild AD); the mean age was 81.9 years (SD 4.9), with 70% females. Based on a multi-step approach and model fit, the final model included IADL functioning as the response variable and memory, attention, executive function, vision and hearing, mobility, balance, education, and social support as predictors. The final model explained 75% of the variability. The significant predictors in the model were mobility, balance, attention, and education, and were the predictors with the most considerable effects based on standardized coefficient estimates. The subgroup analysis, including only a-MCI participants, revealed a similar pattern.ConclusionOur results confirm that IADL functioning in people with mild cognitive disorder is influenced by cognitive and physical function and personal factors. The study provides further insight into understanding IADL functioning impairments in persons with mild impaired cognition and may be used to develop specific non-pharmacological interventions.