Partially Linear Models Research Articles

Pharmacometabolomics of sulfonylureas in patients with type 2 diabetes: a cross-sectional study.

Sulfonylureas have been a longstanding pharmacotherapy in the management of type 2 diabetes, with potential benefits beyond glycemic control. Although sulfonylureas are effective, interindividual variability exists in drug response. Pharmacometabolomics is a potent method for elucidating variations in individual drug response. Identifying unique metabolites associated with treatment response can improve our ability to predict outcomes and optimize treatment strategies for individual patients. Our objective is to identify metabolic signatures associated with good and poor response to sulfonylureas, which could enhance our capability to anticipate treatment outcome. In this cross-sectional study, clinical and metabolomics data for 137 patients with type 2 diabetes who are taking sulfonylurea as a monotherapy or a combination therapy were obtained from Qatar Biobank. Patients were empirically categorized according to their glycosylated hemoglobin levels into poor and good responders to sulfonylureas. To examine variations in metabolic signatures between the two distinct groups, we have employed orthogonal partial least squares discriminant analysis and linear models while correcting for demographic confounders and metformin usage. Good responders showed increased levels of acylcholines, gamma glutamyl amino acids, sphingomyelins, methionine, and a novel metabolite 6-bromotryptophan. Conversely, poor responders showed increased levels of metabolites of glucose metabolism and branched chain amino acid metabolites. The results of this study have the potential to empower our knowledge of variability in patient response to sulfonylureas, and carry significant implications for advancing precision medicine in type 2 diabetes management.

Integrative Data Analysis Where Partial Covariates Have Complex Nonlinear Effects by Using Summary Information from an External Data

A full parametric and linear specification may be insufficient to capture complicated patterns in studies exploring complex features, such as those investigating age-related changes in brain functional abilities. Alternatively, a partially linear model (PLM) consisting of both parametric and nonparametric elements may have a better fit. This model has been widely applied in economics, environmental science, and biomedical studies. In this article, we introduce a novel statistical inference framework that equips PLM with high estimation efficiency by effectively synthesizing summary information from external data into the main analysis. Such an integrative scheme is versatile in assimilating various types of reduced models from the external study. The proposed method is shown to be theoretically valid and numerically convenient, and it ensures a high-efficiency gain compared to classic methods in PLM. Our method is further validated using two data applications by evaluating the risk factors of brain imaging measures and blood pressure.

Projection tests for regression coefficients in high-dimensional partial linear models

. To check the significance of the regression coefficients in the linear component of high-dimensional partial linear models, we proposed some projection-based test statistics. These test statistics are connected with U-statistics of order two and they are applicable for diverging dimensions and heteroscedastic model errors. By using the martingale central limit theorem, we show the asymptotic normalities of the proposed test statistics under the null hypothesis and local alternative hypotheses. The performance of test statistics are evaluated by simulation studies. The simulation results show that the proposed test statistics are powerful and have the correct type-I error asymptotically under the null hypothesis.

Double penalized variable selection for high-dimensional partial linear mixed effects models

In this study, we address the selection of both fixed and random effects in partial linear mixed effects models. By combining B-spline and QR decomposition techniques, we propose a double-penalized likelihood procedure for both estimating and selecting these effects. Furthermore, we introduce an orthogonality-based method to estimate the non-parametric component, ensuring that the fixed and random effects are separated without any mutual interference. The asymptotic properties of the resulting estimators are investigated under mild conditions. Simulation studies are conducted to evaluate the finite sample performance of the proposed method. Finally, we demonstrate the practical applicability of our methodology by analyzing a real data.

Additive partial linear models with autoregressive symmetric errors and its application to the hospitalizations for respiratory diseases

Additive partial linear models with autoregressive symmetric errors and its application to the hospitalizations for respiratory diseases

Comparison of Some Robust Methods of Difference Based Ridge Estimator in Semiparametric Regression Model

The semiparametric regression models have received a lot of attention from researchers recently because it combines parametric and nonparametric methods, it is one of the advanced topics in data analysis for various studies, which aims to find the best capabilities and a high level of efficiency. One of the most important semiparametric regression models is the partial linear regression model (PLM), which consists of a parametric component and a nonparametric component, for the purpose of estimating the parametric component, the difference method will be used to remove the nonparametric component. When the analysis hypotheses of the parametric component are not fulfilled, it will suffer from several problems, the most important of which is the problem of complete multicollinearity, besides the multicollinearity, there are also outliers in the data. In this research, the problems of multicollinearity and outliers of the semiparametric regression model were addressed, where simulation was used to generate data with different sample sizes and for different correlations and outlier ratios and for different methods such as [Difference Ridge based M robust with Nadaraya – Watson (DRMNW), Difference Ridge based S robust with Nadaraya – Watson (DRSNW), Difference Ridge based M robust with Smoothing spline (DRMSP), Difference Ridge based S robust with Smoothing spline (DRSSP)], the results showed that method Difference Ridge based M robust with Smoothing spline (DRMSP) is the best estimator.

Elevated plasma neurofilament light was associated with multi-modal neuroimaging features in Alzheimer’s disease signature regions and predicted future tau deposition

BackgroundNeurofilament Light (NfL) is a biomarker for early neurodegeneration in Alzheimer’s disease (AD). This study aims to examine the association between plasma NfL and multi-modal neuroimaging features across the AD spectrum and whether NfL predicts future tau deposition.MethodsThe present study recruited 517 participants comprising Aβ negative cognitively normal (CN-) participants (n = 135), Aβ positive cognitively normal (CN +) participants (n = 64), individuals with amnestic mild cognitive impairment (aMCI) (n = 212), and those diagnosed with AD dementia (n = 106). All the participants underwent multi-modal neuroimaging examinations. Cross-sectional and longitudinal associations between plasma NfL and multi-modal neuro-imaging features were evaluated using partial correlation analysis and linear mixed effects models. We also used linear regression analysis to investigate the association of baseline plasma NfL with future PET tau load. Mediation analysis was used to explore whether the effect of NfL on cognition was mediated by these imaging biomarkers.ResultsThe results showed that baseline NfL levels and the rate of change were associated with Aβ deposition, brain atrophy, brain connectome, glucose metabolism, and brain perfusion in AD signature regions (P<0.05). In both Aβ positive CN and MCI participants, baseline NfL showed a significant predictive value of elevating tau burden in the left medial orbitofrontal cortex and para-hippocampus (β = 0.336, P = 0.032; β = 0.313, P = 0.047). Lastly, the multi-modal neuroimaging features mediated the association between plasma NfL and cognitive performance.ConclusionsThe study supports the association between plasma NfL and multi-modal neuroimaging features in AD-vulnerable regions and its predictive value for future tau deposition.

Nonlinearity, Heterogeneity and Indirect Effects in the CO2 Emissions-Financial Development Relation From Partial Linear Additive Panel Model

Nonlinearity, Heterogeneity and Indirect Effects in the CO2 Emissions-Financial Development Relation From Partial Linear Additive Panel Model

Unraveling patterns, causes, and nature-based remediation strategy for non-grain production on farmland in hilly regions

The surge in non-grain production on farmland (NGPF) poses significant threats to food security and land sustainability, particularly in hilly regions. However, there remains a lack of clarity on how to effectively balance grain and non-grain production in relation to land remediation. Using Wannian County as a case study, we investigate the evolution of this by leveraging high-precision land surveys and satellite imagery. Through the application of bootstrapped partial linear regression models, we identify key influencers behind each type of NGPF. In proposing land remediation solutions, we integrate the results of NGPF and land quality evaluations to identify mismatches between non-grain production and land attributes (i.e., topography, geology, soil, and land use). Our findings reveal a substantial growth in NGPF, expanding from 3838.72 ha to 5659.64 ha (2010–2020), and predominantly occurring on farmland with favorable natural conditions and connected locations such as proximity to roads, town centers, and industrial plants. Surprisingly, the basic farmland protection policy shows limited effectiveness in curbing NGPF, except for garden operations. We identify 1674 NGPF patches suitable for conversion to grain production and provide land remediation suggestions tailored to low-quality farmland with specific natural barriers, thus complementing the demand for regional non-grain production. This study thereby innovatively proposes nature-based land remediation strategies to address the non-grain production dilemma by tailoring NGPF and land quality, offering valuable insights for sustainable farmland management in China and beyond.

Local linear-$k$NN smoothing for semi-functional partial linear regression

The aim of this paper is to study a semi-functional partial linear regression model. The estimators are constructed by $k$-nearest neighbors local linear method. Some asymptotic results are established for an i.i.d sample under certain conditions, including asymptotic normality of the parametric component and the almost certain convergence (with rate) of the non-parametric component. Lastly, using cross-validation, the performances of our estimation method are presented on simulated data and on real data by comparing them with other known approaches for semi-functional partial linear regression models.

Semi-Functional Partial Linear Quantile Regression Model with Randomly Censored Responses

Semi-Functional Partial Linear Quantile Regression Model with Randomly Censored Responses

Modelling of cup anemometry and dynamic overspeeding in average wind speed measurements

Abstract. Cup anemometers measure average wind speed in the atmosphere and have been used for one and a half centuries by meteorologists. Within the last half century, cup anemometers have been used extensively in wind energy to measure wind resources and performance of wind turbines. Meteorologists researched cup anemometer behaviour and found dynamic overspeeding to be an inherent and significant systematic error. The wind energy community has strong accuracy requirements for power performance measurements on wind turbines, and this led in the last 2 decades to new research on cup anemometer characteristics, which was taken to a new level with the development of improved calibration procedures, cup anemometer calculation models and classification methods. Research projects in wind energy demonstrated, by field and wind tunnel measurements, that angular response was a significant contributor to uncertainty and that dynamic overspeeding was a significant but less important contributor. Earlier research was mainly made on cup anemometers with hemispherical cups on long arms, ​​​​​​​and dynamic overspeeding was considered an inherent and high uncertainty error for cup anemometers. Research on conical cups on short arms has now shown that zero or low overspeeding is present on a well-designed cup anemometer, providing a much lower overspeeding uncertainty error. Different cup anemometer calculation models were investigated in order to find derived overspeeding characteristics. The general and often used parabolic torque coefficient model showed that zero overspeeding is present when the speed ratio roots of the torque coefficient curve go through the equilibrium speed ratio and zero. The two-cup drag model is a special case of the parabolic torque coefficient model but with the second root being reciprocal to the equilibrium speed ratio. The drag model always results in a positive maximum overspeeding of the order of 1.1 times the turbulence intensity squared. A linear torque coefficient results in maximum overspeeding levels equal to the turbulence intensity squared. Torque characteristics of a cup anemometer with hemispherical cups fit slightly well to the drag model, but a cup anemometer with conical cups does not fit to the drag model nor the parabolic model; it fits better to a partial linear model and even better to an optimized torque model. The most accurate modelling of cup anemometer characteristics is at present made with the ACCUWIND model (Dahlberg et al., 2006). This model uses tabulated torque coefficient and angular response data measured in a wind tunnel. The ACCUWIND model is found in International Electrotechnical Commission (IEC) wind turbine power performance standards, where it is used in a classification system for estimation of operational uncertainties. For an actual comparison of two cup anemometers, with respectively hemispherical and conical cups, the influence of dynamic overspeeding was found to be relatively low compared to angular response, but for conical cups it was specifically low.

Nursing workforce plays a significant role in reducing COVID-19 deaths worldwide: A cross-sectional analysis of data from 178 countries.

Worldwide, the role of nursing workforce in reducing COVID-19 case fatality ratio (CFR) is analyzed with scatter plots, Pearson's r and nonparametric, partial correlation and multiple linear regression models. The potential confounders, median age, health expenditure, physician density, and urbanization were incorporated for calculating the independent role of nursing workforce in protecting against COVID-19 CFR. The study findings suggested that (1) the nursing workforce inversely and significantly correlates with COVID-19 CFR; (2) this relationship remained independent of the confounding effects of each individual confounder or their combination; (3) Nursing workforce was the only variable identified as a significant contributor for reducing COVID-19 CFR, when it was incorporated into stepwise regression model with health expenditure, median age, physician density, and urbanization for analyzing their individual predicting effects on COVID-19 CFR. A strong message for the health authorities is that, although in shortage, nursing workforce showed their significant role in reducing COVID-19 deaths worldwide. This study highlights that the role of nursing workforce should be incorporated into population health research.

A multi-scale approach to study palm-weevils in a tropical agroecosystem

Abstract Context The recent phytosanitary crisis caused by the stem borer weevils Dynamis borassi and Rhynchophorus palmarum in peach palms (Bactris gasipaes) seriously affects the economy of rural families in Colombia. To understand the possible ecological mechanisms favoring the pest, the weevil-palm system needs to be studied from a multi-scale perspective. Objectives We evaluated the combined effects of agricultural management, landscape, topography, soil, and climate variables on infestation levels of peach palm caused by the weevils D. borassi and R. palmarum in the Colombian southwest to understand underlying ecological drivers of the current phytosanitary crisis. Methods The palm infestation levels (response variable) and local agronomic practices were recorded in 32 sites across Colombia's Pacific, Andes, and Amazon natural regions. For each peach palm crop, landscape variables were estimated from classified Sentinel-2 images, soil variables were extracted from the Soil Grids 250 m database, and climate variables were extracted from the CHIRPS and CHIRTS databases. Explanatory variables were analyzed using Generalized Linear Models and Partial Least Squares-Generalized Linear Models. Results Landscape variables, specifically the number and shape of non-forest patches, had a greater influence on peach palm infestation levels than other environmental variables, possibly related to the decrease in the density of Oenocarpus batatua, the natural palm host for D. borassi, leading it to switch to the peach palm. Conclusions A multi-scale approach of the weevil-palm interaction enabled us to identify an ecological mechanism that has not been proposed before. Our recommendation for families depending on peach palm is to design palm management with a landscape perspective that considers the conservation of alternative hosts for the weevils in agroforestal plots.

Pengaruh Kompetensi terhadap Kualitas Pelayanan Administrasi Kelurahan Setiamanah Kota Cimahi

Community services are various government organizations engaged in services whose job is to provide services for public. Public services in question are various forms of service-related activities carried out by an organization or individual. Every organization is formed to achieve certain goals, and if these goals are achieved, the organization can be said to be successful. Competence is a strong foundation for achieving success. Employee competence is needed when carrying out services. The services provided to the community are part of the identity of the organization. If in providing services, there is a good impression from its employees, then the organization looks good in the eyes of the public. This study aims to determine how competence influences the quality of administrative services in the Setiamanah Village, Cimahi City. The method in this study used quantitative methods with a population and sample of 21 employees. The data analysis technique used in this study is a simple linear regression analysis technique. Based on the partial linear regression test summary model table above it proves that the magnitude of the R Square correlation value is 0.600 or 60%. This means that the influence of competence on the quality of administrative services is 60%. And 40% is influenced by other factors that are not present in this study.

Partial Linear Model Averaging Prediction for Longitudinal Data

Partial Linear Model Averaging Prediction for Longitudinal Data

Can the digital economy boost rural residents’ income? Evidence from China based on the spatial Durbin model

The digital economy, as a new economic form, has the potential to be a considerable driving force for promoting agricultural modernisation, accelerating the flow of information between urban and rural areas and increasing the income of rural residents, thereby contributing to rural revitalisation. However, it may also inevitably lead to issues such as widening the digital divide between urban and rural areas, which can impact the extent to which rural residents effectively benefit from the digital economy. This study employs provincial panel data from 2011 to 2020 and conducts a temporal global principal component analysis to measure the level of China's digital economy development. Based on this, the spatial Durbin model is employed for empirical testing and regional analysis of the impact and mechanisms of digital economy development on the per capita disposable income of rural residents. The partial linear function coefficient model is used for supplementary analysis. The results indicate the following: (1) there are regional disparities in China's digital economy development, with the highest levels in the eastern region, followed by the western region and the lowest levels in the central region, forming a U-shaped spatial structure; (2) the development of the digital economy has a suppressing effect on the per capita disposable income of rural residents in both the home province and neighbouring provinces, indicating that the digital economy has not become a growth booster for rural revitalisation; (3) regional results show that the negative spatial effects of the digital economy are in the eastern region, whereas the western region has considerable positive spatial spillover effects. In contrast, no notable spatial effects exist in the central region. Therefore, under the backdrop of the digital economy, it is necessary to promote agricultural transformation and increase rural residents’ income by tailoring strategies to regional characteristics.

Empirical likelihood in a partially linear single-index model with censored response data

An empirical likelihood (EL) approach for a partial linear single-index model with censored response data is studied. A bias-corrected EL ratio is proposed, and the asymptotic chi-squared distribution of this ratio is obtained. The result can be directly used to construct the confidence regions of the regression parameters. The estimators of regression parameters and link function are constructed, and their asymptotic distributions are obtained. Also, a confidence band of the link function is constructed. The proposed method has two main features: The first feature is that the EL ratio is calibrated directly from within, instead of multiplying an adjustment factor by an EL ratio, which reflects the nature of EL. The second feature is avoiding undersmoothing of nonparametric functions, thus ensuring that the n-consistency of the parameter estimator. As a byproduct, the EL and estimation of a single-index model with censored response data are studied. The performance of the bias-corrected EL is evaluated by the simulation studies. The proposed method is illustrated with an example of a real data analysis.

Quantile Model Averaging Based on Longitudinal Partial Linear Model

Quantile Model Averaging Based on Longitudinal Partial Linear Model

Semiparametric estimation in generalized additive partial linear models with nonignorable nonresponse data

Semiparametric estimation in generalized additive partial linear models with nonignorable nonresponse data