Regression Parameters Research Articles

Mixture Cure Semiparametric Accelerated Failure Time Models With Partly Interval-Censored Data.

In practical survival analysis, the situation of no event for a patient can arise even after a long period of waiting time, which means a portion of the population may never experience the event of interest. Under this circumstance, one remedy is to adopt a mixture cure Cox model to analyze the survival data. However, if there clearly exhibits an acceleration (or deceleration) factor among their survival times, then an accelerated failure time (AFT) model will be preferred, leading to a mixture cure AFT model. In this paper, we consider a penalized likelihood method to estimate the mixture cure semiparametric AFT models, where the unknown baseline hazard is approximated using Gaussian basis functions. We allow partly interval-censored survival data which can include event times and left-, right-, and interval-censoring times. The penalty function helps to achieve a smooth estimate of the baseline hazard function. We will also provide asymptotic properties to the estimates so that inferences can be made on regression parameters and hazard-related quantities. Simulation studies are conducted to evaluate the model performance, which includes a comparative study with an existing method from the smcure R package. The results show that our proposed penalized likelihood method has acceptable performance in general and produces less bias when faced with the identifiability issue compared to smcure. To illustrate the application of our method, a real case study involving melanoma recurrence is conducted and reported. Our model is implemented in our R package aftQnp which is available from https://github.com/Isabellee4555/aftQnP.

FHLight: A novel method of indoor scene illumination estimation using improved loss function

In augmented reality tasks, especially in indoor scenes, achieving illumination consistency between virtual objects and real environments is a critical challenge. Currently, mainstream methods are illumination parameters regression and illumination map generation. Among these two categories of methods, few works can effectively recover both high-frequency and low-frequency illumination information within indoor scenes. In this work, we argue that effective restoration of low-frequency illumination information forms the foundation for capturing high-frequency illumination details. In this way, we propose a novel illumination estimation method called FHLight. Technically, we use a low-frequency spherical harmonic irradiance map (LFSHIM) restored by the low-frequency illumination regression network (LFIRN) as prior information to guide the high-frequency illumination generator (HFIG) to restore the illumination map. Furthermore, we suggest an improved loss function to optimize the network training procedure, ensuring that the model accurately restores both low-frequency and high-frequency illumination information within the scene. We compare FHLight with several competitive methods, and the results demonstrate significant improvements in metrics such as RMSE, si-RMSE, and Angular error. In addition, visual experiments further confirm that FHLight is capable of generating scene illumination maps with genuine frequencies, effectively resolving the illumination consistency issue between virtual objects and real scenes. The code is available at https://github.com/WA-tyro/FHLight.git.

Defective regression models for cure rate data with competing risks

ABSTRACT In this paper, we propose a novel method for the analysis of cure rate data with competing risks using defective distributions. We develop two defective regression models for the analysis of competing risk data subjected to random right censoring. The proposed models enable us to estimate the cure fraction directly from the model. Simultaneously, we also estimate the regression parameters corresponding to each cause of failure using the method of maximum likelihood. We conduct a simulation study to evaluate the finite sample performance of the proposed estimators. The practical usefulness of the procedures is illustrated using two real-life data sets.

Research on Forecasting Sales of Pure Electric Vehicles in China Based on the Seasonal Autoregressive Integrated Moving Average–Gray Relational Analysis–Support Vector Regression Model

Aiming to address the complexity and challenges of predicting pure electric vehicle (EV) sales, this paper integrates a time series model, support vector machine and combined model to forecast EV sales in China. Firstly, a seasonal autoregressive integrated moving average (SARIMA) model was constructed using historical EV sales data, and the model was trained on sales statistics to obtain forecasting results. Secondly, variables that were highly correlated with sales were analyzed using gray relational analysis (GRA) and utilized as input parameters for the support vector regression (SVR) model, which was constructed to optimize sales predictions for EVs. Finally, a combined model incorporating different algorithms was verified against market sales data to explore the optimal sales prediction approach. The results indicate that the SARIMA-GRA-SVR model with the squared prediction error and inverse method achieved the best predictive performance, with MAPE, MAE and RMSE values of 12%, 1.45 and 2.08, respectively. This empirical study validates the effectiveness and superiority of the SARIMA-GRA-SVR model in forecasting EV sales.

Using a Stopping Rule to Optimize Cost-Quality Tradeoffs in a Large, Mixed-Mode Survey: A Simulation Study

Rising nonresponse and the increasing costs of conducting surveys are creating pressure on survey organizations to efficiently allocate resources. The challenge is to produce the highest quality data possible within a fixed budget. We use a stopping rule designed to minimize a function of cost and errors. The rule is based on the product of predicted costs and the predicted mean squared error of a survey estimate. We simulate the impact of implementing the stopping rule on the 2019 US Survey of Doctorate Recipients (SDR), which is a longitudinal survey conducted every two years in the US using web, mail, and CATI. We vary the types of models used to generate the input predictions (parametric regression vs. nonparametric tree models) and the timing of the implementation of the rule. We found that the modeling approach made less difference, while the timing of the implementation of the stopping rule made a large difference in outcomes. The rule is multivariate and optimizes outcomes for two variables. It performed better for one variable, leading to reduced costs and only small increases in errors. The other variable had larger error increases.

Impact of sociodemographic disparities on sarcopenia, telomere length, and mortality in patients with liver disease in the US population.

Sarcopenia is common in patients with liver disease and both sarcopenia and short telomeres are associated with mortality, however their relationship in patients with liver disease remains unknown. A cohort of 16,072 adults from the National Health and Nutrition Examination Survey from 1999 to 2006 was analyzed. Liver disease was defined by aminotransferases and classified into etiology-based categories. Sarcopenia was defined by dual-energy x-ray absorptiometry. All analyses were conducted separately on each multiple imputation data set and combined via Rubin's rules. P-values for group comparisons were calculated by testing logistic regression parameter estimates. Cox proportional hazards regression was used for mortality analysis with mortality data available until 2015. Sarcopenia was present in 9.5% of patients with liver disease. Age, race, income, education, physical inactivity, and certain medical comorbidities were associated with sarcopenia. Patients with liver disease and sarcopenia had significantly shorter telomeres than patients with liver disease without sarcopenia when unadjusted for age. The interaction between telomere length and sarcopenia was significantly associated with all-cause mortality. The implications of telomere length on all-cause mortality in patients with liver disease varied by age and sarcopenia status. Shorter telomeres appear to be more highly associated with increased mortality in older patients without sarcopenia.

A new parametric quantile regression model based on an Owen distribution

Quantile regression is a versatile statistical tool for modeling conditional quantiles, offering a comprehensive view of the relationship between explanatory and response variables. This technique is widely used in fields like economics, finance, environmental science, and psychology. This paper introduces a new quantile regression model based on the Owen distribution, which provides flexibility in capturing quantile-specific relationships. The model parameters are estimated using maximum likelihood estimation, with a detailed discussion on the estimation process. Diagnostic measures, including a pseudo- R 2 statistic, quantile residuals, and generalized Cook’s distance, are proposed to assess model adequacy and identify influential data points. Monte Carlo simulations are employed to evaluate the estimators' performance. The model’s effectiveness is illustrated through an application to Chilean household income data, highlighting its advantages over conventional quantile regression models. Implemented in the R programming language, this model is accessible for practical use, offering a valuable addition to the quantile regression field and suggesting promising directions for further research and application across disciplines.

APPLIED MIXED KERNEL AND FOURIER SERIES MODELLING IN NONPARAMETRIC REGRESSION

There are three nonparametric regression approaches, namely, parametric, nonparametric and semi-parametric regression. Nonparametric regression allows the response variable to follow a different curve from one predictor variable to another. In paired data, the components of predictor variable and the response variable are assumed to follow unknown data patterns, so that they can be approximated by a kernel based regression model and Fourier series. The base components are approximated by kernel functions and Fourier series functions. Error is assumed to be normally distributed with zero mean and constant variance. The aim of this research is to make a kernel-based nonparametric regression model and Fourier series on poverty data in the Province of Bali. The research phase method begins with the introduction of the kernel based nonparametric regression model and the Fourier series. The next step is to study the estimation of the regression curve. The function estimation results are highly dependent on the bandwidth, smoothing and oscillation parameters. The resulting model gives a value of R2=0.6278, means that the used variables can explain the model by 62.78 percent. From the obtained modeling re-sults, the Open Unemployment Rate has a positive effect on the percentage of poor people in Bali. Keywords: Nonparametric Regression; Modeling; Kernel; Fourier Series.

Integrated analysis of single-cell and bulk RNA-sequencing identifies a metastasis-related gene signature for predicting prognosis in lung adenocarcinoma.

Metastasis has been documented as an independent and significant prognostic feature of lung adenocarcinoma (LUAD) patients. However, the underlying genetic and molecular mechanisms responsible for LUAD metastasis and their prognostic significance are not exactly defined. The single-cell transcriptomic profiles of primary and metastatic LUAD samples were integrated as a whole dataset. Enrichment analysis and pseudotime trajectory analysis were performed to illustrate the cellular origins and changes during the metastatic process. The LUAD metastasis-related genes (LMRGs) molecular cluster and signature was constructed through unsupervised consensus clustering and ten machine-learning algorithms in The Cancer Genome Atlas (TCGA) LUAD cohort using ten machine-learning algorithms. Validation of the signature was conducted using four independent cohorts from the Gene Expression Omnibus (GEO) database. Kaplan-Meier, ROC, univariate and multivariate Cox-regression analyses were performed to test the stability of the signature. The gene CCT6A was subjected to knockdown, followed by validation through western blot analysis, flow cytometry, wound healing and transwell-migration assays to determine its potential significance. First, the signaling pathway networks remodeling and metabolic reprogramming were demonstrated to be involved in the metastasis of malignant LUAD cells, which facilitate their extravasation and adaptation to other organs. Furthermore, distinct subtypes of malignant LUAD cells exhibit tissue-specific patterns. Then, two distinct molecular patterns of LMRGs were established, which showed diverse prognoses. A LUAD metastasis-related gene signature (LMRGS) was constructed via a multiple machine-learning-based integrative procedure, which possesses distinctly superior accuracy than most common clinical features and 69 published prognostic signatures. The patients stratified by the signature into high-risk group had a significantly poorer prognosis compared to those in the low-risk group, and this was well validated across different clinical subgroups. In addition, the risk score calculated by LMRGS remained an independent prognostic parameter in both univariate and multivariate Cox regression. Notably, knockdown of CCT6A gene promoted cell apoptosis and decelerated the cell migration obviously. LMRGS could serve as a novel and promising tool to improve clinical outcomes for individual LUAD patients.

Hybrid Learning Model for intrusion detection system: A combination of parametric and non-parametric classifiers

The growing digital transformation has increased the need for effective intrusion detection systems. Traditional intrusion detection systems face challenges in accurately classifying complex patterns. To address this issue, this study proposed a Hybrid Learning Model (HLM) that combines both parametric and non-parametric classifiers. The proposed HLM consist of two stages: the first stage employs a non-parametric Base Learner (np-BL) to analyze the data patterns and the second stage involves meta-modelling to generalize the overall performance of the model, named the Parametric Meta-Learning (PML) model. The proposed HLM blends the outcomes of np-BL and PML models using a stacking ensemble. As a base learning model K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Gradient Boosting Machine (GBM), and Support Vector Classification with Radial Basis Function (SVC-RBF), are adopted from a non-parametric classifier group. The parametric classifiers Logistic Regression (LR), Naïve Bayes Classifier (NBC), Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA) and Support Vector Machine with linear kernel (Linear SVM) were used as meta-models. The HLM, as proposed, enhances the adaptability and robustness of the model by combining non-parametric and parametric models. To evaluate the competence of the proposed HLM, a performance analysis was conducted using the NSL-KDD, UNSW-NB15, and CICIDS2017 datasets. The effectiveness was assessed using various metrics, including classification accuracy, precision, recall, F1-Score (F1), Receiver Operating Characteristic (ROC) curve, Detection Rate (DR), and False Alarm Rate (FAR). The proposed HLM achieves a better accuracy rate across different datasets when compared with the existing models. The achieved accuracies are 99.02 %, 99.98 % and 99.63 % for the NSL-KDD, UNSW-NB15, and CICIDS2017 datasets respectively. Furthermore, the HLM gave a significant reduction in FAR, with values of 0.0126, 0.0001, and 0.0016 for the above-mentioned datasets.

Cox regression model with doubly truncated and interval-censored data

Interval sampling is an efficient sampling scheme used in epidemiological studies. Doubly truncated (DT) data arise under this sampling scheme when the failure time can be observed exactly. In practice, the failure time may not be observed and might be recorded only within time intervals, leading to doubly truncated and interval censored (DTIC) data. This article considers regression analysis of DTIC data under the Cox proportional hazards (PH) model and develops the conditional maximum likelihood estimators (cMLEs) for the regression parameters and baseline cumulative hazard function of models. The cMLEs are shown to be consistent and asymptotically normal. Simulation results indicate that the cMLEs perform well for samples of moderate size.

Comparison between estimates of beta regression model using MSE

The multiple linear regression analysis shows the effects of explanatory variables on the dependent variable. Its function is to represent data to understand the shape and nature of the relationship between explanatory variables and the response variable. One of the main problems faced by linear regression is the nature of the data, as sometimes it does not follow a normal distribution. This has led to the emergence of different types of linear regression models such as the beta regression model, where the data of the dependent variable is less than one and confined within the period (0,1), and the data is relative and decimal. Non-traditional methods will be used to estimate beta regression parameters because some traditional methods do not provide suitable and accurate results when dealing with one of the main problems encountered in regression, which is multicollinearity. To address this issue, we used some methods include The Maximum Likelihood Estimation (MLE), ridge regression, modified Liu, and Ozkale and Kaciranlar method. Shrinking parameters are proposed for each method, and a comparison between the methods is conducted using the Mean Squared Error comparison criterion through a simulation experiment with two approaches, the simulation results showed that the ridge regression method is the best for the first and second methods of simulation. Paper type Research Paper

Asymptotic normality of a relative error functional regression estimator under left truncation and right censoring

. In this article, we introduce a non parametric regression estimator based on a relative error criterion, for left truncated and right censored data with functional predictors. Left truncation and right censoring are common forms of incomplete data in survival analysis, where the event time of interest is only observed within a certain interval which adds some challenges to the regression task. We establish the asymptotic normality of our estimator and develop a procedure for constructing confidence intervals for the predicted responses. The theoretical results obtained are supported using simulation studies and a real data example from the energy field.

Confidence ellipses for simple regression parameters with Quasi-associated random errors

The work presented in this paper introduces a novel methodology for constructing confidence regions for linear regression parameters using ellipses, particularly in cases where the random errors are quasi-associated. Linear regression is widely employed in statistical analysis to predict a dependent variable using one or more independent variables. Traditionally, it is assumed that random errors in regression models follow a normal distribution. However, this assumption may not hold in many practical applications where errors exhibit dependencies. Thus, this study focuses on quasi-associated random variables, which include both positively and negatively associated variables.We derive exponential inequalities that allow the construction of confidence ellipses for least squares estimates of model parameters. This approach is applied to a Keynesian consumption function, which models the relationship between consumption and national income. Using data on monthly per capita income in Africa over 12 years, we demonstrate the effectiveness of our method in estimating regression parameters when the random errors are quasi-associated. The findings show that this technique can improve the accuracy of statistical models used in fields such as economics and engineering, especially in cases where the traditional assumptions of regression analysis are not met.

Do digitalization and green innovation limit carbon emissions? Evidences from BRICS economies

Rapidly evolving innovation and digitalization have captured the focus of policymakers and scholars regarding their potent role in influencing environmental quality. The present research analyzes the impact of these variables on the carbon emissions of Brazil, Russia, India, China, and South Africa economies from 1990 to 2021. This research also explores the impact of economic growth, quadratic green innovation, and green energy on carbon emissions. Using several panel diagnostic tests, this research validates heterogeneous slopes, the presence of cross-sectional dependence, and significant cointegration. Due to the mixed integration order, this research uses a cross-sectional augmented autoregressive distributed lag model, and the results show that economic expansion and green innovation are significant drivers of emissions in both the short and long run. However, digitalization, quadratic green innovation, environmental policy stringency, and green energy are significant in improving environmental quality and sustainability. The long-term results are tested by employing a series of parametric and nonparametric regressions. This research recommends further investment in environmental research and development, digital technologies, green innovation, and the strengthening of environmental policies to attain sustainable development.

Study on a Pseudo-Elastic Model for High-Damping Rubber.

With advancements in seismic isolation and damping technology, high-damping rubber (HDR) bearings are now widely used. However, significant gaps remain in HDR-analysis model research, with few studies integrating multiple factors, the Mullins effect, and stiffness hardening for more accurate practical predictions. This study classifies the effective behavior of HDR and examines the stress-strain relationships of different behavioral types using more appropriate equations. Mathematical models were established based on pseudo-elasticity theory, which is an extension of continuum mechanics. Subsequently, parameter functions were developed through parameter determination tests and regression analysis, leading to the completion of the pseudo-elastic model for HDR. Finally, the model's effectiveness was validated through validation tests. This study finds that behavior classification effectively examines phenomenological-based HDR stress-strain relationships, as distinct behavioral patterns are not adequately captured by a single approach. Incorporating tests to functionalize material parameters complements theoretical models. Additionally, accurately explaining HDR behavior requires considering the Mullins effect and stiffness hardening, influenced by the coupled effects of temperature, strain amplitude, and compressive stress. Consequently, this HDR pseudo-elastic model offers a comprehensive explanation of HDR behavior, including the Mullins effect and stiffness hardening, under various influencing factors based on clear mechanical principles and explicit computational procedures.

Potential hematopoietic effects of SGLT2 inhibitors in patients with cardiac amyloidosis

Abstract Introduction Sodium–glucose cotransporter 2 inhibitors (SGLT2i) have been revealed to have potential hematopoietic effects in patients with heart failure (HF), leading to an improvement in clinical outcome. However, these benefits have not been studied in patients with cardiac amyloidosis (CA), despite CA often causing symptoms of heart failure (HF) and contributing to an iron-deficient state, which further complicates by limiting erythropoiesis and lowering haemoglobin and haemotocrit levels. We aimed to determine the potential of SGLT2i in improving haematological parameters and functional capacity (FC) in amyloidosis patients. Methods A prospective analysis was conducted to compare the effects of SGLT2i in a cohort of patients who received the best medical therapy (BMT) alongside SGLT2i (n=20), as opposed to patients receiving only BMT without SGLT2i (n=20). All of patients underwent blood testing and cardiopulmonary exercise testing (CPET) at baseline and after 6 months (IQR:5.0 – 6.0). Results The SGLT2i-based therapy resulted in a significant improvement and difference in hematological parameters upon follow-up assessment compared to the control group. In the SGLT2i group, the mean haemoglobin level increased from 13.3 g/dL to 14.5 g/dL, whereas in the control group, it decreased from 12.7 g/dL to 10.9 g/dL (P < 0.001). Furthermore, the haematocrit difference showed a significant increase in the SGLT2i group (+3.8%) compared to a decrease in the control group (-4.4%) (P < 0.001). Additionally, the iron status improved in the SGLT2i-treated group (+6.7 µg/dL) compared to a decrease in the control group (-14.9 µg/dL), although the difference was significant only with a p-value of 0.034. However, neither group demonstrated significant improvement nor regression in CPET parameters. The peak oxygen consumption (peak VO2, (ml/min)/kg) showed no significant change in either group (P = 0.286), as well as VE/VCO2 slope (P = 0.295). Conclusions Treatment with SGLT2i could be utilized in the treatment of patients with amyloidosis. This potential benefit in improving hematological parameters warrants further investigation and consideration in clinical practice.

Integrating Environmental Covariates into Adaptability and Stability Analyses: A Structural Equation Modeling Approach for Cotton Breeding

Breeding programs rely on genotype-by-environment interaction (GEI) to recommend cultivars for specific locations. GEI describes how different genotypes perform under varying environmental conditions. Several methods were proposed to assess adaptability and stability across environments. These methods utilize various statistical approaches like parametric and non-parametric regression, multivariate analysis techniques, and even Bayesian frameworks and artificial intelligence. The accessibility of environmental data through platforms like NASA POWER allows breeders to integrate this information into a breeding process. It has been done by using multi-omics integration models that combine data across various biological levels to create accurate predictive models. In the context of phenotypic adaptability and stability analysis, structural equation modeling (SEM) offers an interesting approach to integrating environmental covariates. This work aimed to propose a novel approach that integrates weather information into adaptability and stability analysis, combining SEM with the established Eberhart and Russell model. Additionally, a user-friendly applet, denoted ECERSEM-AdaptStab, was made available to perform the analysis. This approach utilized data from 12 cotton cultivar trials conducted across two growing seasons at 19 sites. This approach successfully integrated environmental covariates into a phenotypic adaptability and stability analysis of cotton cultivars. Specifically, the genotypes TMG 41 WS, IMA CV 690, DP 555 BGRR, BRS 286 and BRS 369 RF were recommended for favorable environments, while the genotypes TMG 43 WS, IMA 5675 B2RF, IMA 08 WS, NUOPAL, DELTA OPAL, BRS 335, and BRS 368 RF are more suitable for unfavorable environments.

Bayesian Empirical Likelihood Regression for Semiparametric Estimation of Optimal Dynamic Treatment Regimes.

We propose a semiparametric approach to Bayesian modeling of dynamic treatment regimes that is built on a Bayesian likelihood-based regression estimation framework. Methods based on this framework exhibit a probabilistic coherence property that leads to accurate estimation of the optimal dynamic treatment regime. Unlike most Bayesian estimation methods, our proposed method avoids strong distributional assumptions for the intermediate and final outcomes by utilizing empirical likelihoods. Our proposed method allows for either linear, or more flexible forms of mean functions for the stagewise outcomes. A variational Bayes approximation is used for computation to avoid common pitfalls associated with Markov Chain Monte Carlo approaches coupled with empirical likelihood. Through simulations and analysis of the STAR*D sequential randomized trial data, our proposed method demonstrates superior accuracy over Q-learning and parametric Bayesian likelihood-based regression estimation, particularly when the parametric assumptions of regression error distributions may be potentially violated.

Age, Growth, Sex Composition, and Diet of the Burbot, Lota lota, the Only Freshwater Species of the Family Lotidae in the Amur (Heilongjiang) River, Northeast China

Information about the population structure, including the age and sex composition, growth characteristics, and diet of fish, is essential for the conservation and sustainable exploitation of fish stock. The burbot, Lota lota, is the only freshwater species of the family Lotidae in the Amur (Heilongjiang) River catchment located in northeastern China. Information on the biological characteristics and data on the population structure of this fish from the Amur River are scarce. To study these factors in burbot, Lota lota, in the Amur River of China, 749 specimens from four sampling areas were taken and analyzed in October 2022, January 2023, and May 2023. The ages of the sampled fish ranged from 1+ to 7+ years. The female/male ratio was 1.04:1, and body length and mass varied from 175 to 595 mm and 73.5 to 1958.7 g, respectively. The length–weight regression parameter b value was estimated as 2.80. The parameters of the von Bertalanffy growth model were L∞ = 596, K = 0.221, and t0 = −0.771 for all sampled fish; L∞ = 625, K = 0.208, and t0 = −0.756 for females; and L∞ = 584, K = 0.219, and t0 = −0.980 for males. The analysis of the stomach contents showed fish to be the major source of nutrition across all areas and sampling periods. In January, the secondary prey of burbots in the Huma reach of the upper Amur River was aquatic insect larvae, while, in the Tongjiang and Fuyuan reaches of the middle Amur River, the secondary prey was shrimp. The mean stomach fullness index and rate of empty stomachs differed with the sampling area, with the greatest proportion of empty stomachs observed in the Luobei reach of the middle Amur River, and the lowest in the Tongjiang and Fuyuan reaches. The mean stomach fullness index showed the opposite trend. In the Huma reach, the contributions of fish to the diet and the mean stomach fullness index were significantly higher in May than in October and January. The study provides the first detailed information on the population age and sex structure, growth patterns, and feeding ecology of burbots from the Amur River, China. The results will aid in formulating management strategies and regulations for local populations of burbots in the Amur River, China.