Semiparametric Regression Model Research Articles

Forecasting Coke's Price by Combination Semi-Parametric Regression Model

According to the characteristics of consumption of coke, the article utilized combination semi-parametric regression method rather than usual regression analysis to forecast the coke's price. Coke' price was divided into two parts. Parameter part was analyzed through error correction model and BP artificial neural network. Nonlinear part was fitted by core function. Nonparametric sector was described by coke yield. The article utilized cross validation method to select optimum bandwidth, choosing the Parabola kernel for the kernel function. The least squares estimation was selected in new model estimation. The estimation results of real case demonstrate that error correction-semi-parametric regression model and BP artificial neural network-semi-parametric regression model not only reduced boundary estimation error but also strengthen economical interpretation. It is an effective method to forecast coke's price, which can largely raise the estimation precision of coke's price.

Incidence of Opportunistic Infections among HIV-infected Children on ART at Gondar University Specialized Hospital, Ethiopia

Objective: To assess the prevalence and determinants of opportunistic infections among HIV-infected children receiving antiretroviral medication (ART) at the University of Gondar’s comprehensive specialty hospital. Methods: A retrospective cohort research was undertaken at the University of Gondar comprehensive specialized hospital between January 11, 2017, and January 10, 2022. The study included 389 HIV-positive children on antiretroviral therapy. A data extraction form derived from the ART entry and follow-up forms was used to collect data from the charts of HIV-infected persons. The data was entered using Epi-dataTM Version 4.5, and the data was analyzed using StataTM Version 16. ofBivariate and multivariate semi-parametric and parametric regression models were fitted to identify risk factors of opportunistic infections. The Kaplan Meier survival curve was used to calculate the time gap between opportunistic infections. Result: Between January 11, 2017, and January 10, 2022, 389 HIV-infected children started antiretroviral therapy (ART). The overall Incidence of opportunistic infections was 4.2 (95 percent CI 3.75 to 4.46) per 100 person-year observation during the follow-up period, which lasted a median of 67 months (IQR=58-76 months). The most common OI at follow-up was bacterial pneumonia, which was 13.5 (95 percent CI: 10.29,17.7). Children with baseline CD4 200 cells/l counts (AHR= 2.05 (1.40, 3.00), baseline Hgb level of 10 g/dl (AHR=5.87 (3.97,8.69), ever taking IPT (AHR=1.71(0.94, 3.11), and HIVinfected children with fair/poor ART adherence (AHR=1.41(0.90, 2.19) were all significantly linked to the development of opportunistic infections. Conclusion: In this study, the rate of opportunistic infections among HIV-positive children was quite low. OIs were significantly associated with characteristics such as baseline CD4 count, Hgb level, adherence to ART, and use of IPT. We feel that early screening and treatment of OIs should be explored as a result of dueto the aforesaid findings above. Additionally, adherence assistance through phone calls and case managers might be increased. Furthermore, at each follow-up examination, children with severe immunodeficiency and advanced disease stages at the time of ART initiation should be closely monitored and extensively screened for the occurrence of OIs. Furthermore, at each followup examination, children with severe immunodeficiency and advanced disease stages at the time of ART initiation should be closely monitored and extensively screened for the occurrence of OIs. Additionally, adherence assistance through phone calls and case managers might be increased. Keywords: Children; Antiretroviral therapy; HIV; Opportunistic infections; Ethiopia

Determinantal Point Processes Implicitly Regularize Semiparametric Regression Problems

Semiparametric regression models are used in several applications which require comprehensibility without sacrificing accuracy. Typical examples are spline interpolation in geophysics and nonlinear time series problems, where the system includes a linear and nonlinear component. We discuss here the use of a finite determinantal point process (DPP) for approximating semiparametric models. Recently, Barthelmé, Tremblay, Usevich, and Amblard introduced a novel representation of finite DPPs. These authors formulated extended L-ensembles that can conveniently represent partial-projection DPPs and suggest their use for optimal interpolation. With the help of this formalism, we derive a key identity illustrating the implicit regularization effect of determinantal sampling for semiparametric regression and interpolation. Also, a novel projected Nyström approximation is defined and used to derive a bound on the expected in-sample prediction error for the corresponding approximation of semiparametric regression. This work naturally extends similar results obtained for kernel ridge regression.

Intrinsic semi-parametric regression model on Grassmannian manifolds with applications

Statistics on manifolds attracts more and more attentions because a variety of observations or responses have manifold structures. In this paper, we address the regression task on Grassmannian manifolds, which is arisen from the statistical shape analysis. Particularly, we develop an intrinsically semi-parametric model instead of the geodesic regression for better approximating shapes. Concretely, first we introduce a nonparametric term on Grassmannian manifolds to further depict the relationships that cannot be well described by the geodesic model on Grassmannian manifolds. Second, we utilize an alternatively iterative strategy to update parametric and nonparametric parts to form a solution. Finally, we testify the effectiveness of our regression model on synthetic data on with multiple combinations of n and m, as well as a real data of corpus callosum shapes. The experimental results validate that our proposed model outperforms the conventional geodesic regression model and shows the advantage of approaches driven doubly by model and data.

MODEL SURVIVAL SEMIPARAMETRIK DAN PARAMETRIK UNTUK DATA DEMAM BERDARAH DENGUE (DBD) DI RSUD KABUPATEN CIAMIS TAHUN 2020

The research is about the best survival analysis model used for patients with Dengue Hemorrhagic Fever (DHF) at Ciamis Hospital in 2020. The purpose of this research is to identify the factors that influence the rate of cure for DHF at Ciamis Hospital in 2020. The method used is survival analysis. Furthermore, the parametric model used Weibull PH, whereas the semiparametric model used Cox PH regression. The result of the analysis showed that the best model obtained was the semiparametric Cox PH regression model, with significant factors including age, Pack Cell Volume (PCV), hemoglobin, White Blood Cell (WBC), and body temperature.

A binational analysis of infant mortality among crisis-driven diasporas and those who remain: a population-based study in Colombia and Venezuela

ObjectiveTo assess the health cost (or benefit) of crisis-driven migration by focusing on the infant mortality rate (IMR) of the Colombian diaspora in Venezuela and the Venezuelan diaspora in Colombia.MethodsWe...

ESTIMATION OF SEMIPARAMETRIC REGRESSION CURVE WITH MIXED ESTIMATOR OF MULTIVARIABLE LINEAR TRUNCATED SPLINE AND MULTIVARIABLE KERNEL

The response variable of the regression analysis has a linear relationship with one of the variable predictors, however the unknown relationship pattern with the other predictor variables. Consequently, it can be approached by using semiparametric regression model. The predictor variable that has a linear relationship with the response variable can be approached by using linear parametric curve called parametric component. Meanwhile, the unknown relationship between the response variable with another predictor variable can be approached by using nonparametric curve called nonparametric component. If the predictor variable in nonparametric component is more than one, then it can be approached by using a different nonparametric curve named combined or mixed estimator. In this research, nonparametric component is approached using mixed estimator of multivariable linear truncated spline and multivariable kernel. The objective of this research is to estimate the model of semiparametric regression curve with mixed estimator of multivariable truncated spline and multivariable kernel. Estimation of this mixed model using ordinary least square method.

Semiparametric regression analysis of doubly-censored data with applications to incubation period estimation.

The incubation period is a key characteristic of an infectious disease. In the outbreak of a novel infectious disease, accurate evaluation of the incubation period distribution is critical for designing effective prevention and control measures . Estimation of the incubation period distribution based on limited information from retrospective inspection of infected cases is highly challenging due to censoring and truncation. In this paper, we consider a semiparametric regression model for the incubation period and propose a sieve maximum likelihood approach for estimation based on the symptom onset time, travel history, and basic demographics of reported cases. The approach properly accounts for the pandemic growth and selection bias in data collection. We also develop an efficient computation method and establish the asymptotic properties of the proposed estimators. We demonstrate the feasibility and advantages of the proposed methods through extensive simulation studies and provide an application to a dataset on the outbreak of COVID-19.

Robust estimates of regional treatment effects in multiregional randomized clinical trials with ordinal responses

ABSTRACT Global clinical trials involving multiple regions are common in current drug development processes. Determining the regional treatment effects of a new therapy over an existing therapy is important to both the sponsors and the regulatory agencies in the regions. Existing methods are mainly for continuous primary endpoints and use subjectively specified models, which may deviate from the true model. Here, we consider trials that have ordinal responses as the primary endpoint. This article extends the recently developed robust semiparametric ordinal regression model to estimate regional treatment effects, in which the regression coefficients and regional effects are modeled parametrically for ease of interpretation, and the regression link function is specified nonparametrically for robustness. The model parameters are estimated by semiparametric maximum likelihood estimation, and the null hypothesis of no regional effect is tested by the Wald test. Simulation studies are conducted to evaluate the performance of the proposed method and compare it with the commonly used parametric model. The results of the former show an improved overall performance over the latter. In particular, the model yields much higher precision in estimation and prediction than the fixed-link model. This result is especially appealing since our interest is to estimate the treatment effect more efficiently and the estimand is of particular interest in multiregional clinical trials. We then apply the method by analyzing real multiregional clinical trials with ordinal responses as their primary endpoint.

Interpretability of SurvivalBoost upon Shapley Additive Explanation value on medical data

Machine learning methods have been extensively used in survival analysis. SurvivalBoost - a machine-learning-based survival regression algorithm, focused on Elastic-net-Type penalized semiparametric Cox regression model on XGBoost and random survival forests, has been verified its superior prediction performance on real and simulated datasets. Whereas the interpretability is remain undiscovered. This paper discusses the interpretability of this algorithm upon using the Shapley Additive Explanation (SHAP) value. It is illustrated that the algorithm can be more effective to guide the diagnosis and practice of survival analysis.

Comparing Some of Robust the Non-Parametric Methods for Semi-Parametric Regression Models Estimation

In this research, some robust non-parametric methods were used to estimate the semi-parametric regression model, and then these methods were compared using the MSE comparison criterion, different sample sizes, levels of variance, pollution rates, and three different models were used. These methods are S-LLS S-Estimation -local smoothing, (M-LLS)M- Estimation -local smoothing, (S-NW) S-Estimation-NadaryaWatson Smoothing, and (M-NW) M-Estimation-Nadarya-Watson Smoothing.
 The results in the first model proved that the (S-LLS) method was the best in the case of large sample sizes, and small sample sizes showed that the (M-LLS) method was the best, while the second model showed in general that the S-LLS method was the best in addition to the method M-LLS was the best in some cases of sample sizes and at different levels of variance. As for the third model, it was shown through the results that in most cases the S-LLS method was the best in addition to the M-LLS method which was better in some cases of sample sizes and at different levels of variance.

Model selection among Dimension-Reduced generalized Cox models.

Conventional semiparametric hazards regression models rely on the specification of particular model formulations, such as proportional-hazards feature and single-index structures. Instead of checking these modeling assumptions one-by-one, we proposed a class of dimension-reduced generalized Cox models, and then a consistent model selection procedure among this class to select covariates with proportional-hazards feature and a proper model formulation for non-proportional-hazards covariates. In this class, the non-proportional-hazards covariates are treated in a nonparametric manner, and a partial sufficient dimension reduction is introduced to reduce the curse of dimensionality. A semiparametric efficient estimation is proposed to estimate these models. Based on the proposed estimation, we further constructed a cross-validation type criterion to consistently select the correct model among this class. Most importantly, this class of hazards regression models contains the fully nonparametric hazards regression model as the most saturated submodel, and hence no further model diagnosis is required. Overall speaking, this model selection approach is more effective than performing a sequence of conventional model checking. The proposed method is illustrated by simulation studies and a data example.

Estimating returns to education: The control function approach

This study aims to evaluate the effect of schooling years on wage level in Turkey by using the Mincer wage equation. This function is used as the baseline for the investigation of earnings determinants. For this purpose, the relationship between wage and education level of people is estimated by using a semiparametric regression model considering the control function approach. Various variables such as education level, experience, gender and marital status are estimated separately in the wage model by utilizing the Household Budget Statistics micro data set of 2017 in Turkey obtained by the Turkish Statistical Institute. The parametric model does not clarify the model clearly when functional form of relation is not known. To overcome this drawback, semiparametric regression model, which contains parametric and nonparametric variables, can be successfully applied. This model is extended by adding the 1997 education reform as a control variable. The achieved semiparametric test results from this study showed that there is a positive relationship between schooling years and wage level. On the other hand, control function approach results indicate the existence of fluctuant progress for the effect of schooling years on the wage level along the period.

Analysis of Pathogen Characteristics and Nursing Factors of Tonsil Infection Based on Regression Equation.

In order to meet the needs of the analysis and application of regression equation in clinical medicine of tonsil infection, this paper focuses on the semiparametric regression model method, cross-validation method, empirical method, and multiple regression equation analysis of atypical data using regression equation. The general method of analyzing this kind of data is given, and the parameter estimation and hypothesis testing of the model are systematically studied. The experimental results showed that among the 90 paraffin-embedded tissue specimens of chronic tonsillitis and adenoid hypertrophy in this study, 26 out of 49 male children were EBERs positive, accounting for 53.06% of male children (26/49 cases). 28 of the 41 female children were positive, accounting for 68.29 of the female children (28/41 cases). There were 14 cases in infant group, 20 cases in preschool age group, 25 cases in school-age group, and 31 cases in adolescence group; the EBERs-positive rate was 42.86% (6/14 cases) in early childhood and 55.00% in early school-age (11/20 cases), and the EBERs-positive rate was 60.00% in school-age group (15/20 cases) and 70.97% in adolescent group. The results showed that the latent infection rate of adenoid hypertrophy EBV in children with chronic tonsillitis showed no significant difference between genders. It is proved that the regression equation method can meet the needs of clinical analysis and application of tonsil infection.

Variable selection in semiparametric regression models for longitudinal data with informative observation times.

A common issue in longitudinal studies is that subjects' visits are irregular and may depend on observed outcome values which is known as longitudinal data with informative observation times (follow-up). Semiparametric regression modeling for this type of data has received much attention as it provides more flexibility in studying the association between regression factors and a longitudinal outcome. An important problem here is how to select relevant variables and estimate their coefficients in semiparametric regression models when the number of covariates at baseline is large. The current penalization procedures in semiparametric regression models for longitudinal data do not account for informative observation times. We propose a variable selection procedure that is suitable for the estimation methods based on pseudo-score functions. We investigate the asymptotic properties of penalized estimators and conduct simulation studies to illustrate the theoretical results. We also use the procedure for variable selection in semiparametric regression models for the STAR*D dataset from a multistage randomized clinical trial for treating major depressive disorder.

Association between Acute Exposure to PM2.5 Chemical Species and Mortality in Megacity Delhi, India.

The association between daily all-cause mortality and short-term fine particulate matter (PM2.5) exposure is well established in the literature. However, association between acute exposure to PM2.5 chemical species and mortality is not well known, especially in developing countries like India. Here we examined associations between mortality and acute exposure to PM2.5 mass concentration and their 15 chemical components using data from 2013 to 2016 in megacity Delhi using a semiparametric quasi-Poisson regression model, adjusting for mean temperature, relative humidity, and long-term time trend as the major potential confounders. Mortality estimates were further checked for effect modification by sex, age group, and season. The subspecies of NO3-, NH4NO3, Cr, NH4+, EC, and OC showed a higher mortality impact than the total PM2.5 mass. Males were at higher risk from NO3-, SO42-, and their NH4+ compounds along with carcinogen Cr, whereas female group was at higher risk from EC and OC. Among all age groups, the elderly above 65 years were the most vulnerable group prone to mortality effects from maximum species. The major mortality risk from all hazardous species arose from their winter exposures. Our study provides the first evidence of association between acute exposure to PM2.5 chemical species and mortality anywhere in India and recommends similar studies in other regions so that sectoral mitigation emitting the most toxic species can be prioritized to maximize the health benefits.

A semiparametric approach to estimating reference price effects in sales response models

It is well known that store-level brand sales may not only depend on contemporaneous influencing factors like current own and competitive prices or other marketing activities, but also on past prices representing customer response to price dynamics. On the other hand, non- or semiparametric regression models have been proposed in order to accommodate potential nonlinearities in price response, and related empirical findings for frequently purchased consumer goods indicate that price effects may show complex nonlinearities, which are difficult to capture with parametric models. In this contribution, we combine nonparametric price response modeling and behavioral pricing theory. In particular, we propose a semiparametric approach to flexibly estimating price-change or reference price effects based on store-level sales data. We compare different representations for capturing symmetric vs. asymmetric and proportional vs. disproportionate price-change effects following adaptation-level and prospect theory, and further compare our flexible autoregressive model specifications to parametric benchmark models. Functional flexibility is accommodated via P-splines, and all models are estimated within a fully Bayesian framework. In an empirical study, we demonstrate that our semiparametric dynamic models provide more accurate sales forecasts for most brands considered compared to competing benchmark models that either ignore price dynamics or just include them in a parametric way.

An empirical comparison of parametric and semiparametric time series regression models for overdispersed count data

Count time series regression is of interest in diverse applications. Count data may be marginally, as well as conditionally overdispersed, in addition to being serially dependent. We propose a fully parametric approach and a semiparametric approach (in the Godambeinformation sense), and compare model performance in simulation studies. Estimators from both approaches exhibit large relative bias, but their variability was similar. Our empirical study shows that while our semiparametric approach method is promising as a robust alternative to fully parametric count time series regression modelling, bias correction is needed.

Consistency of semi-parametric maximum likelihood estimator under identifiability conditions for the linear regression model with type I right censoring data

The consistency of the semi-parametric maximum likelihood estimator (SMLE) under the semi-parametric linear regression model with right-censoring data (SPLRRC model) has not been studied under the necessary and sufficient condition for the identifiability of the parameters. In this paper, we discuss the necessary and sufficient condition for the consistency of SMLE under type I right censoring.

Semiparametric Regression Models and Applicability in Agriculture

Parametric regression models assume that the dependent variable is a linear relationship with the independent variables and the form of the relationship is known. Nonparametric regression methods are applied in cases where the relationship type is not known or assumptions cannot be provided. However, when there is more than one independent variable, some of the independent variables may be in a linear relationship with the dependent variable, while some may be in a nonlinear relationship. In order to model these variables, semiparametric regression models, which are a combination of parametric and nonparametric regression methods, are used. In this study parametric, nonparametric and semiparametric regression models, parametric estimates, fit statistical values of the models, confidence intervals and standard error values were calculated. As a result of the analysis, the parameters of the milking unit and the quarantine area among the parametric variables, the operation area, the ventilation area, the number of ventilation, the quarantine area, the infirmary area, the manure pit and the distance to the center among the non-parametric variables were found to be statistically very important (P