Parameters Of Regression Model Research Articles

Effects of esketamine nasal spray on depressive symptom severity in adults with treatment-resistant depression and associations between the Montgomery-Åsberg Depression Rating Scale and the 9-item Patient Health Questionnaire.

To examine the effect of esketamine nasal spray (ESK) plus newly initiated oral antidepressant (OAD) versus OAD plus placebo nasal spray (PBO) on the association between Montgomery-Åsberg Depression Rating Scale (MADRS) and 9-item Patient Health Questionnaire (PHQ-9) scores in adults with treatment-resistant depression (TRD). Data from TRANSFORM-1 and TRANSFORM-2 (two similarly designed, randomized, active-controlled TRD studies) and SUSTAIN-1 (relapse prevention study) were analyzed. Group differences for mean changes in PHQ-9 total score from baseline were compared using analysis of covariance. Associations between MADRS and PHQ-9 total scores from TRANSFORM-1/TRANSFORM-2 were assessed using simple parametric, nonparametric, and multiple regression models. In TRANSFORM-1/TRANSFORM-2 (ESK+OAD, n=343; OAD+PBO, n=222), baseline PHQ-9 mean scores were 20.4 for ESK+OAD and 20.6 for OAD+PBO (severe depression). At day 28, significant group differences were observed in least squares mean change (SE) in PHQ-9 scores from baseline (-12.8 [0.46] vs -10.3 [0.53], P<.001) and in clinically substantial change in PHQ-9 scores (≥6 points; 77.1% vs 64%, P<.001) in ESK+OAD and OAD+PBO groups, respectively. A nonlinear relationship between MADRS and PHQ-9 was observed; total scores demonstrated increased correlation over time. In SUSTAIN-1, 57.3% of patients receiving ESK+OAD (n=89) versus 44.2% receiving OAD+PBO (n=86) retained remission status (PHQ-9 score ≤4) at maintenance treatment end point (P=.044). In adults with TRD, ESK+OAD significantly improved severity of depressive symptoms, and more patients achieved clinically meaningful changes in depressive symptoms based on PHQ-9, versus OAD+PBO. PHQ-9 outcomes were consistent with those of clinician-rated MADRS. ClinicalTrials.gov: NCT02417064, NCT02418585, NCT02493868.

Building energy efficiency assessment base on predict-center criterion under diversified conditions

With the rapid expanding of urban economy and population, how to reduce the energy consumption and improve energy efficiency of public buildings has become a key issue for energy conservation and urban development. However, due to the diversity of building usage condition, energy consumption data often has problems with nonlinearity, outliers, and diversity, which greatly reduce the accuracy of energy consumption prediction and energy conservation assessment. To overcome the above drawbacks, a regression prediction model of building energy consumption is designed, which cluster the same type of data characteristics to form a subset of equipment through Gaussian mixture clustering, and design the specific normalized adaptive loss function to determine the optimal control parameters of regression model. Furthermore, a novel predict-center energy efficiency evaluation criterion is proposed, and the cumulative distribution pattern is further statistically analyzed. Finally, the energy benchmark evaluation framework is proposed to determine the energy efficiency evaluation grade. Through comparative experimental analysis, it can be seen that the proposed prediction model can adapt to different types of building data, and have better prediction accuracy and convergence speed. The consistency and robustness of the benchmark evaluation framework and method are optimal.

Estimation Parameters in the fuzzy class Poisson mixture regression model

In the Poisson mixture regression for the fuzzy class model, observations originate from distinct sub-sources or classes due to the problem of heteroscedasticity. The underlying assumption posits that the observed data stem from a finite mixture of fuzzy classes. The primary challenge lies in achieving the optimal assignment of observations to their respective categories, necessitating the utilization of sophisticated methods for parameter estimation within the model. This research paper explores the estimation of parameters in a Poisson mixture regression model designed for the fuzzy class. Leveraging the FCML (Fuzzy Classification Maximum Likelihood algorithm) and genetic algorithm methodologies, we conducted simulations to assess the accuracy and efficacy of parameter estimation. Our findings reveal a clear superiority of the genetic algorithm over the FCML algorithm, as evidenced by the Mean Square Error (MSE) criterion. The genetic algorithm consistently produced lower MSE values, indicating more precise and reliable parameter estimates compared to the FCML algorithm. This research underscores the potential of the genetic algorithm as a robust and effective tool for parameter estimation in complex statistical models, offering researchers an alternative approach for tackling challenges in the estimation of parameters within the context of the fuzzy class

Detection of rice (with husk) moisture content based on hyperspectral imaging technology combined with MSLPP–ESMA–SVR model

AbstractMoisture content detection has guiding significance for the storage and quality detection of rice. To detect moisture content rapidly and non‐destructively, hyperspectral imaging technology (400‐1000 nm) was employed to analyze rice with different moisture content, and Savitzky–Golay mixed standard normalized variable algorithm (SG‐SNV) was used for spectral data pretreatment. Furthermore, a modified supervised locality preserving projections (MSLPP) method was proposed to extract spectral features. The modeling results showed that MSLPP had better spectral feature extraction performance. Finally, to improve prediction accuracy, the equilibrium slime mold algorithm (ESMA) was introduced to obtain the optimal parameters (c, g) of the support vector regression (SVR) model. And MSLPP–ESMA–SVR model had higher prediction accuracy and stronger robustness, with R2p reaching 0.9755 and root mean square error of prediction reaching 0.8597%. Therefore, hyperspectral imaging technology combined with MSLPP–ESMA–SVR model is feasible to detect rice moisture content.

A comparison between the estimation of the exponential and Weibull survival regression models using the Bayesian method (Simulation)

Parametric regression models are one of the most important regression models used in the medical field. They are the tool through which the response variable is modeled when the values ​​of that variable are survival times with a known probability distribution such as the exponential distribution and the Weibull distribution, I propose in this article to estimate the parameters of parametric survival regression models using the Bayesian method, these models are: the exponential survival regression model, the Weibull survival regression model, The simulation was used to generate data that follows the parametric survival regression models depending on various factors such as sample size, and three different parameter models, The simulation results showed that the exponential survival regression model outperformed the Weibull survival regression model in order to obtain the lowest value according to the Bayesian Information Criterion (BIC), The larger the sample size, the more accurate and reliable the analysis estimators performed by the Bayes Information Criterion

Health and growth in China: Greater investment in both soft- and hard-ware are needed

The purpose is to study the relationship between health investment and economic growth in China. The research design involves empirical research using semi parametric regression models and instrumental variable methods. The results indicate that on the one hand, Chinese health investment can promote economic growth, and its promoting effect on economic growth is non-linear; On the other hand, healthcare investment in the form of human capital is more conducive to promoting regional economic growth than healthcare investment in the form of material capital. The conclusion is that it is necessary to increase health investment in the form of human capital such as the number of health personnel in the eastern region, as well as in the form of material capital such as the number of beds in the central and western regions, in order to better promote regional economic growth.

Complete convergence for arrays of rowwise m n -extended negatively dependent random variables and its application

. In this article, under some proper and sufficient conditions, we gave complete convergence for weighted sums and maximal weighted sums of arrays of rowwise m n -extended negatively dependent (rowwise m n -END) random variables, which is a new dependent structure. In addition, a relationship between { m n , n ≥ 1 } and moment condition for convergence is revealed in a sense. The results obtained in the article generalize some corresponding ones for independent and some dependent random variables. As an application, the strong consistency for the weighted estimator in a non parametric regression model is established.

Dynamic monitoring of aboveground biomass in inner Mongolia grasslands over the past 23 Years using GEE and analysis of its driving forces

Aboveground biomass (AGB) in grasslands directly reflects the net primary productivity, making it a sensitive indicator of grassland resource quality and ecological degradation. Accurately estimating AGB over large regions to reveal long-term AGB evolution trends remains a formidable challenge. In this study, we divided Inner Mongolia Autonomous Region (IMAR) grasslands into three study regions based on their spatial distribution of grassland types. We combined remote sensing data with ground-based sample data collected over the past 19 years from 6114 field plots using the Google Earth Engine platform. We constructed random forest (RF) and traditional regression AGB inversion models for each region and selected the best-performing model through accuracy assessment to estimate IMAR grassland AGB for the period 2000–2022. We also examined the trends in AGB changes and identified the driving forces affecting IMAR grasslands through the application of Theil-Sen estimation, Mann-Kendall trend analysis, and the Geodetector model. The main findings are as follows: (1) Compared with the univariate parametric traditional regression model, the AGB monitoring accuracy of the multivariate non-parametric RF model in the three study regions increased by 5.94%, 5.08% and 19.14%, respectively. (2) The average AGB per unit area of IMAR grasslands from 2000 to 2022 was 731.41 kg/hm2, with alpine meadow having the highest average AGB (1271.70 kg/hm2) and temperate grassland desertification having the lowest (469.06 kg/hm2). IMAR grasslands exhibited an overall increasing trend in AGB over the past 23 years (6.01 kg/hm2•yr), with the increasing trend covering 83.52% of the grassland area and the decreasing trend covering 16.48%. (3) Spatially, IMAR grassland AGB showed a gradual decline from northeast to southwest and exhibited an increasing trend with increasing longitude (45.423 kg/hm2 per degree) and latitude (71.9 kg/hm2 per degree). (4) Meteorological factors were the most significant factors affecting IMAR grassland AGB, with precipitation (five-year average q value of 0.61) being the most prominent. In the western part of IMAR, where precipitation is consistently limited throughout the year, the primary drivers of influence were human activities, with particular emphasis on the number of livestock (with a five-year average q value of 0.44). It is evident that reducing human activity disturbance and pressure in fragile grassland areas or implementing near-natural restoration measures will be beneficial for the sustainable development of grassland ecosystems. The results of this research hold substantial reference importance for the protection and restoration of grasslands, the supervision and administration of grassland resources, as well as the development of policies related to grassland management.

Parametric Quantile Beta Regression Model

SummaryIn this paper, we develop a fully parametric quantile regression model based on the generalised three‐parameter beta (GB3) distribution. Beta regression models are primarily used to model rates and proportions. However, these models are usually specified in terms of a conditional mean. Therefore, they may be inadequate if the observed response variable follows an asymmetrical distribution. In addition, beta regression models do not consider the effect of the covariates across the spectrum of the dependent variable, which is possible through the conditional quantile approach. In order to introduce the proposed GB3 regression model, we first reparameterise the GB3 distribution by inserting a quantile parameter, and then we develop the new proposed quantile model. We also propose a simple interpretation of the predictor–response relationship in terms of percentage increases/decreases of the quantile. A Monte Carlo study is carried out for evaluating the performance of the maximum likelihood estimates and the choice of the link functions. Finally, a real COVID‐19 dataset from Chile is analysed and discussed to illustrate the proposed approach.

Impact of zinc supplementation on phenotypic antimicrobial resistance of fecal commensal bacteria from pre-weaned dairy calves.

The objective of this study was to evaluate the impact of dietary zinc supplementation in pre-weaned dairy calves on thephenotypic antimicrobial resistance (AMR) of fecal commensal bacteria. A repository of fecal specimens from a random sample of calves block-randomized into placebo (n = 39) and zinc sulfate (n = 28) groups collected over a zinc supplementation clinical trial at the onset of calf diarrhea, calf diarrheal cure, and the last day of 14 cumulative days of zinc or placebo treatment were analyzed. Antimicrobial susceptibility testing was conducted for Enterococcus spp. (n = 167) and E. coli (n = 44), with one representative isolate of each commensal bacteria tested per sample. Parametric survival interval regression models were constructed to evaluate the association between zinc treatment and phenotypic AMR, with exponentiated accelerated failuretime (AFT) coefficients adapted for MIC instead of time representing the degree of change in AMR (MIC Ratio, MR). Findings from our study indicated that zinc supplementation did not significantly alter the MIC in Enterococcus spp. for 13 drugs: gentamicin, vancomycin, ciprofloxacin, erythromycin, penicillin, nitrofurantoin, linezolid, quinupristin/dalfopristin, tylosin tartrate, streptomycin, daptomycin, chloramphenicol, and tigecycline (MR = 0.96-2.94, p > 0.05). In E. coli, zinc supplementation was not associated with resistance to azithromycin (MR = 0.80, p > 0.05) and ceftriaxone (MR = 0.95, p > 0.05). However, a significant reduction in E. coli MIC values was observed for ciprofloxacin (MR = 0.17, 95% CI 0.03-0.97) and nalidixic acid (MR = 0.28, 95% CI 0.15-0.53) for zinc-treated compared to placebo-treated calves. Alongside predictions of MIC values generated from these 17 AFT models, findings from this study corroborate the influence of age and antimicrobial exposure on phenotypic AMR.

On the rate of asymptotic normality of integral weighted kernel estimator in a non parametric regression model for φ-mixing random variables

. In this article, we study the convergence rate of asymptotic normality for the estimator in a non parametric regression model under φ-mixing random variables by using the blockwise technique. With different choices of the parameters, the rates are shown as O ( n − 1 / 9 ) and O ( n − 1 / 6 ) . We also carry out some simulation studies and a real data analysis to support the theoretical results established here.

The effect of the working correlation on fitting models to longitudinal data

AbstractWe present a detailed discussion of the theoretical properties of quadratic inference function estimators of the parameters in marginal linear regression models. We consider the effect of the choice of working correlation on fundamental questions including the existence of quadratic inference function estimators, their relationship with generalized estimating equations estimators, and the robustness and asymptotic relative efficiency of quadratic inference function and generalized estimating equations estimators. We show that the quadratic inference function estimators do not always exist and propose a way to handle this. We then show that they have unbounded influence functions and can be more or less asymptotically efficient than generalized estimating equations estimators. We also present empirical evidence to demonstrate these results. We conclude that the choice of working correlation can have surprisingly large effects.

Absolute treatment effects of novel antidiabetic drugs on a composite renal outcome: meta-analysis of digitalized individual patient data

BackgroundAbsolute treatment benefits—expressed as numbers needed to treat—of the glucose lowering and cardiovascular drugs, glucagon-like peptide-1 (GLP-1) receptor agonists and sodium-glucose transporter 2 (SGLT2) inhibitors on renal outcomes remain uncertain. With the present meta-analysis of digitalized individual patient data, we aimed to display and compare numbers needed to treat of both drugs on a composite renal outcome.MethodsFrom Kaplan–Meier plots of major cardiovascular outcome trials of GLP-1 receptor agonists and SGLT2 inhibitors vs. placebo, we digitalized individual patient time-to-event information on composite renal outcomes with WebPlotDigitizer 4.2; numbers needed to treat from individual cardiovascular outcome trials were estimated using parametric Weibull regression models and compared to original data. Random-effects meta-analysis generated meta-numbers needed to treat with 95% confidence intervals (CI).ResultsTwelve cardiovascular outcome trials (three for GLP-1 receptor agonists, nine for SGLT2 inhibitors) comprising 90,865 participants were included. Eight trials were conducted in primary type 2 diabetes populations, two in a primary heart failure and two in a primary chronic kidney disease population. Mean estimated glomerular filtration rate at baseline ranged between 37.3 and 85.3 ml/min/1.73 m2. Meta-analyses estimated meta-numbers needed to treat of 85 (95% CI 60; 145) for GLP-1 receptor agonists and 104 (95% CI 81; 147) for SGLT2 inhibitors for the composite renal outcome at the overall median follow-up time of 36 months.ConclusionThe present meta-analysis of digitalized individual patient data revealed moderate and similar absolute treatment benefits of GLP-1 receptor agonists and SGLT2 inhibitors compared to placebo for a composite renal outcome.Graphical

Using Nonparametric Procedure to Develop an OCMT Estimator for Big Data Linear Regression Model with Application Chemical Pollution in the Tigris River

Chemical pollution is a very important issue that people suffer from and it often affects the nature of health of society and the future of the health of future generations. Consequently, it must be considered in order to discover suitable models and find descriptions to predict the performance of it in the forthcoming years. Chemical pollution data in Iraq take a great scope and manifold sources and kinds, which brands it as Big Data that need to be studied using novel statistical methods. The research object on using Proposed Nonparametric Procedure NP Method to develop an (OCMT) test procedure to estimate parameters of linear regression model with large size of data (Big Data) which comprises many indicators associated with chemical pollution and profoundly have an effect on the life of the Iraqi people. The SICA estimator were chosen to analyze data and the MSE were used to make a comparison between the two methods and we determine that NP estimator is more effective than the other estimators under Big Data circumstances.

Childhood physical activity and pubertal timing: findings from the LEGACY girls study.

There is limited research on whether physical activity (PA) in early childhood is associated with the timing of pubertal events in girls. We used data collected over 2011-16 from the LEGACY Girls Study (n = 984; primarily aged 6-13 years at study enrolment), a multicentre North American cohort enriched for girls with a breast cancer family history (BCFH), to evaluate if PA is associated with age at thelarche, pubarche and menarche. Maternal-reported questionnaire data measured puberty outcomes, PA in early childhood (ages 3-5 years) and total metabolic equivalents of organized PA in middle childhood (ages 7-9 years). We used interval-censored Weibull parametric survival regression models with age as the time scale and adjusted for sociodemographic factors, and we tested for effect modification by BCFH. We used inverse odds weighting to test for mediation by body mass index-for-age z-score (BMIZ) measured at study enrolment. Being highly active vs inactive in early childhood was associated with later thelarche in girls with a BCFH [adjusted hazard ratio (aHR) = 0.39, 95% CI = 0.26-0.59), but not in girls without a BCFH. In all girls, irrespective of BCFH, being in the highest vs lowest quartile of organized PA in middle childhood was associated with later menarche (aHR = 0.70, 95% CI = 0.50-0.97). These associations remained after accounting for potential mediation by BMIZ. This study provides new data that PA in early childhood may be associated with later thelarche in girls with a BCFH, also further supporting an overall association between PA in middle childhood and later menarche.

Causal Artificial Intelligence Models of Food Quality Data.

Causal Artificial Intelligence Models of Food Quality Data.

Confidence sets for a level set in linear regression.

Regression modeling is the workhorse of statistics and there is a vast literature on estimation of the regression function. It has been realized in recent years that in regression analysis the ultimate aim may be the estimation of a level set of the regression function, ie, the set of covariate values for which the regression function exceeds a predefined level, instead of the estimation of the regression function itself. The published work on estimation of the level set has thus far focused mainly on nonparametric regression, especially on point estimation. In this article, the construction of confidence sets for the level set of linear regression is considered. In particular, level upper, lower and two-sided confidence sets are constructed for the normal-error linear regression. It is shown that these confidence sets can be easily constructed from the corresponding level simultaneous confidence bands. It is also pointed out that the construction method is readily applicable to other parametric regression models where the mean response depends on a linear predictor through a monotonic link function, which include generalized linear models, linear mixed models and generalized linear mixed models. Therefore, the method proposed in this article is widely applicable. Simulation studies with both linear and generalized linear models are conducted to assess the method and real examples are used to illustrate the method.

Detecting potential outliers in longitudinal data with time-dependent covariates.

Outliers can influence regression model parameters and change the direction of the estimated effect, over-estimating or under-estimating the strength of the association between a response variable and an exposure of interest. Identifying visit-level outliers from longitudinal data with continuous time-dependent covariates is important when the distribution of such variable is highly skewed. The primary objective was to identify potential outliers at follow-up visits using interquartile range (IQR) statistic and assess their influence on estimated Cox regression parameters. Study was motivated by a large TEDDY dietary longitudinal and time-to-event data with a continuous time-varying vitamin B12 intake as the exposure of interest and development of Islet Autoimmunity (IA) as the response variable. An IQR algorithm was applied to the TEDDY dataset to detect potential outliers at each visit. To assess the impact of detected outliers, data were analyzed using the extended time-dependent Cox model with robust sandwich estimator. Partial residual diagnostic plots were examined for highly influential outliers. Extreme vitamin B12 observations that were cases of IA had a stronger influence on the Cox regression model than non-cases. Identified outliers changed the direction of hazard ratios, standard errors, or the strength of association with the risk of developing IA. At the exploratory data analysis stage, the IQR algorithm can be used as a data quality control tool to identify potential outliers at the visit level, which can be further investigated.

Parametric modal regression with error in covariates.

An inference procedure is proposed to provide consistent estimators of parameters in a modal regression model with a covariate prone to measurement error. A score-based diagnostic tool exploiting parametric bootstrap is developed to assess adequacy of parametric assumptions imposed on the regression model. The proposed estimation method and diagnostic tool are applied to synthetic data generated from simulation experiments and data from real-world applications to demonstrate their implementation and performance. These empirical examples illustrate the importance of adequately accounting for measurement error in the error-prone covariate when inferring the association between a response and covariates based on a modal regression model that is especially suitable for skewed and heavy-tailed responsedata.

Analysis of the role and influence of sight-singing and ear training on vocal learning skills

Abstract Sight-singing and ear training is one of the essential introductory courses for music majors, and the effectiveness of sight-singing and ear training teaching directly affects students’ growth and progress in music. In this paper, we have combined relevant contents of sight-singing, ear training, and vocal learning to construct a flipped classroom teaching model for sight-singing and ear training using the flipped classroom teaching process. Secondly, taking the students of L Conservatory of Music as the research object, two different questionnaires were designed for students and teachers, and the teaching experiment comparison method was used to test the role of sight-singing and ear-training flipped classroom on vocal music learning skills. Finally, the parameters of the multiple linear regression model were estimated using the least squares method, and the model was used to analyze the specific effects of sight-singing and ear training on vocal learning skills. It was found that after the flipped classroom teaching of sight-singing and ear-training, the ear-training scores of students in the experimental class were 90.43±9.26 points, which were 17.06 points higher than those of students in the control class, and there was no difference in the pre- and post-test scores of sight-singing and ear-training. The regression coefficient of students’ musical expression was 0.306. Every 1 percentage point increase in musical expression would enhance vocal learning skills by 0.306 percentage points. Sight-singing and ear-training can help students better master the basic skills of vocal music, cultivate their sense of rhythm, and enhance their vocal music learning skills.