Semiparametric Research Articles

Hierarchical Bayesian semi-parametric models for measurement error correction in determining optimal fertilizer application levels

Measurement errors present a substantial challenge in accurately determining optimal fertilizer application levels, directly impacting agricultural efficiency and cost-effectiveness. This study examines the use of Hierarchical Bayesian Semi-Parametric (HBS) models to correct these errors, thereby improving precision in agricultural decision-making. By applying these models to a decade of data from Uasin Gishu County, Kenya, we evaluated key variables including maize yield, land size, and fertilizer levels. The results indicate that the HBS models effectively mitigate both systematic and random errors, leading to more accurate fertilizer recommendations. This advancement supports better resource management and higher crop yields. Our findings underscore the value of Bayesian methods in agricultural data analysis and highlight the critical role of accurate measurement and correction in achieving optimal outcomes. The implications of this research extend to improved decision-making processes and more sustainable agricultural practices.

Preservation of the aging intensity order and the preservation of the monotonic aging intensity classes under distorted distributions

This paper presents preservation property of the aging intensity order and the preservation property of the monotonic aging intensity classes under distorted distributions. Several sufficient conditions are given to get the preservation properties. It is shown that the imposed conditions are achievable as we examine in some examples. The preservation of the aging intensity order and the preservation of the decreasing aging intensity class under the structure of a parallel system with independent and identically distributed components’ lifetime are made. A lower bound for the aging intensity function of a random lifetime with an increasing failure rate in average distribution is derived. The results are applied to some semiparametric model as a particular standard family of distorted distribution.

Statistical downscaling model for future projection of daily IDF relationship by Markov chain and kernel density estimator

ABSTRACT Changes in climate might have a significant impact on rainfall characteristics, including extreme rainfall. This study aims to project the future daily rainfall, preserving most of the rainfall characteristics, including extreme rainfall incorporating climate changes. This paper presents two hybrid semi-parametric statistical downscaling models for future projection of IDF curves. The precipitation flux from seven scenarios of ten GCMs and observed daily rainfall data are considered as predictors and predictand variables, respectively. At site, daily rainfall occurrence is modeled using a two-state first-order Markov chain. Rainfall amounts on each wet day are modelled using a univariate nonparametric kernel density estimator. Two types of amount generation models are presented in this study. The bounded model (KDE-SP) is developed, considering the support for the kernel distribution as positive. In the unbounded model (KDE-Ext), the wet days are reclassified as extreme and non-extreme rainy days. A significant increasing trend can be observed in the future projected intensity–duration–frequency relationships. The maximum increment using empirical distribution is observed as 93.21 and 80.93% on a 5-year return period in the far future for the SSP5-8.5 scenario, using KDE-Ext and KDE-SP models, respectively. Although both methods show similar results, the KDE-Ext model performs better in simulating extreme rainfall.

Model-averaging-based semiparametric modeling for conditional quantile prediction

Model-averaging-based semiparametric modeling for conditional quantile prediction

Integration of Modified Heston Model (Jump and Sentiment Factor) with Artificial Neural Networks (ANN) for Volatility Forecasting in Financial Markets

This study examined a new methodology for predicting volatility in financial markets by combining the modified Heston model (JSF) with Artificial Neural Networks (ANN). Forecasting volatility is essential in various financial contexts, such as risk management, option pricing and portfolio optimization. Although the traditional Heston model is effective but it does not account for additional elements like jumps and investor sentiment factors, which impact asset price dynamics. To address this limitation, the extended Heston model with jump and sentiment variables helped in enhancing its ability to capture complex market trends. We utilized ANN's predictive capabilities on the modified Heston model to improve volatility predictions using historical random market data from NASDAQ Composite. The ANN was trained on simulated data from the modified Heston model and assessed on its effectiveness in forecasting volatility. This approach helped in developing a forecasting hybrid semi-parametric model known as the modified-Heston-ANN model. Our findings revealed that this integrated approach outperformed the individual model, offering more precise and robust volatility predictions

Wildfire Loss Modeling: A Flexible Semiparametric Approach

Wildfires have emerged as one of the most devastating natural disasters worldwide, which, in addition to a loss of life, results in billions of dollars in annual losses. This study aims to identify factors that contribute to the frequency and severity of wildfire losses. We propose a semiparametric modeling approach to analyze wildfire property losses in the United States from 1995 to 2020. Our methodology incorporates flexible generalized additive models for location, scale, and shape (GAMLSS), which integrate climate, socioeconomic, and geographical variables into the modeling process. Through empirical analysis, we demonstrate the critical role of spatial effects in modeling wildfire-related property losses. Additionally, we identify key variables, including house index, state population, wildfire duration, and climate index, that significantly contribute to explaining the average property loss. These findings offer valuable insights for enhancing catastrophe risk management for insurance companies and government agencies. Furthermore, the proposed approach can be easily applied to diverse regions grappling with spatially varying catastrophe risks.

Robust estimation for semiparametric spatial autoregressive models via weighted composite quantile regression

In this article, the robust estimation for a class of semiparametric spatial autoregressive models has been investigated. By combining the QR decomposition technique for matrix and the weighted composite quantile regression method, we propose a robust estimation procedure for the parametric and non parametric components. Under certain regularity conditions, asymptotic properties of the resulting estimators are proved. Several simulation analyses have been conducted for further illustrating the performance of the proposed method, and the simulation results demonstrate that the proposed method improve the robustness of the models.

Effects of Alcohol Use on Patient Retention in HIV Care in East Africa.

We sought to investigate the association between hazardous alcohol use and gaps in care for people living with HIV over a long-term follow-up period. Adults who had participated in our previously published Phase I study of hazardous alcohol use at HIV programs in Kenya and Uganda were eligible at their 42 to 48month follow-up visit. Those who re-enrolled were followed for an additional ~ 12months. Hazardous alcohol use behavior was measured using the Alcohol Use Disorders Identification Test (AUDIT) tool. Deidentified clinical data were used to assess gaps in care (defined as failure to return to clinic within 60days after a missed visit). The proportion of patients experiencing a gap in care at a specific time point was based on a nonparametric moment-based estimator. A semiparametric Cox proportional hazard model was used to determine the association between hazardous alcohol use at enrollment in Phase I (AUDIT score ≥ 8) and gaps in care. Of the 731 study-eligible participants from Phase I, 5.5% had died, 10.1% were lost to follow-up, 39.5% transferred, 7.5% declined/not approached, and 37.3% were enrolled. Phase II participants were older, had less hazardous drinking and had a lower WHO clinical stage than those not re-enrolled. Hazardous drinking in the re-enrolled was associated with a Hazard Ratio (HR) of 1.88 [p-value = 0.016] for a gap in care. Thus, hazardous alcohol use at baseline was associated with an increased risk of experiencing a gap in care and presents an early target for intervention.

Multiperiod Dynamic Portfolio Choice: When High Dimensionality Meets Return Predictability

Multiperiod portfolio choice is the central problem in active asset management. Multiperiod dynamic portfolios are notoriously difficult to solve, especially when there are hundreds of tradable assets as well as a large number of state variables. In this article, we develop a novel two-step methodology to solve the multiperiod dynamic portfolio choice problem with high dimensional assets in the presence of return predictability conditional on a large number of state predictors. Specifically, in the first step, we propose the new Risk-Premium Projected-PCA (RP-PPCA) method to reduce the dimension of tradable assets. This method achieves Dimension Reduction (DR) by estimating latent factors with explanatory power in both time series variation and expected return in high-dimension-low-sample-size data. In the second step, we use dynamic programming to solve the multiperiod portfolio choice problem, and in each recursive step, we adopt an Adjusted semiparametric Model Averaging (AMA) method to avoid the curse of dimensionality associated with a large set of state variables while remaining computationally efficient. Thus, we name this two-step approach DRAMA, which stands for a combination of a new dimension reduction method and an adjusted semiparametric model averaging method. Analytically, we show that the portfolios constructed by the DRAMA are approximately optimal under mild assumptions. Moreover, our numerical results based on empirical data from US stock markets show that the proposed portfolios have excellent out-of-sample performances.

Minimum Profile Hellinger Distance Estimation of General Covariate Models

Covariate models, such as polynomial regression models, generalized linear models, and heteroscedastic models, are widely used in statistical applications. The importance of such models in statistical analysis is abundantly clear by the ever-increasing rate at which articles on covariate models are appearing in the statistical literature. Because of their flexibility, covariate models are increasingly being exploited as a convenient way to model data that consist of both a response variable and one or more covariate variables that affect the outcome of the response variable. Efficient and robust estimates for broadly defined semiparametric covariate models are investigated, and for this purpose the minimum distance approach is employed. In general, minimum distance estimators are automatically robust with respect to the stability of the quantity being estimated. In particular, minimum Hellinger distance estimation for parametric models produces estimators that are asymptotically efficient at the model density and simultaneously possess excellent robustness properties. For semiparametric covariate models, the minimum Hellinger distance method is extended and a minimum profile Hellinger distance estimator is proposed. Its asymptotic properties such as consistency are studied, and its finite-sample performance and robustness are examined by using Monte Carlo simulations and three real data analyses. Additionally, a computing algorithm is developed to ease the computation of the estimator.

측정오차 공변량을 고려한 포아송 소지역 모델의 준모수적 계층적 베이즈 추정

In the sample survey, it is impossible to obtain sufficient samples in relatively small areas, which makes it difficult to estimate accurate statistics for small areas. For this reason, various studies on small area estimation are constantly being conducted, and in particular, the Fay-Herriot (1979) model has been expanded to various forms as one of the most widely used small area estimation models. In this study, we developed a semiparametric regression model based on a radial basis function that can consider nonlinear relationships between the resulting variables and covariates, extended from the model considering the measurement error of the covariate in the Poisson distribution proposed by Nam, Hwang(2021). Also, parameter estimation and model fit were performed through hierarchical Bayesian estimation using Gibbs sampling and Metropolis-Hastings algorithm among Markov chain Monte Carlo techniques. Simulations were conducted under various conditions to confirm the suitability of the model developed in this study, and the excellence of the developed model was further verified through empirical analysis using data from the 8th Korean longitudinal study of aging.

Trajectories of maternal parenting stress and adolescent behavioral symptoms in unmarried families: The role of family immigration status

BackgroundMaternal parenting stress during childhood may have important influences on offspring internalizing and externalizing behaviors during adolescence in unmarried households, but it is unclear whether effects differ across different trajectory patterns of maternal parenting stress and for native-born vs. immigrant families. MethodsUsing data from the Future of Families and Child Wellbeing Study, we identified trajectory patterns of maternal parenting stress from ages 1–9 years using semi-parametric group-based trajectory modeling. We used negative binomial regression models to estimate associations between maternal parenting stress trajectories and adolescent behavioral symptoms at age fifteen. ResultsFive maternal parenting stress trajectory groups were identified among the 1982 unmarried families included in this study, representing consistently low (9.2 %), consistently mild (54.2 %), moderate and decreasing (14.4 %), moderate and increasing (16.0 %) and consistently high (6.2 %) levels of maternal parenting stress. For adolescent internalizing symptoms, all maternal parenting stress trajectory groups exhibited higher symptoms compared to the consistently low group: IRR for consistently mild: 1.21 (95 % CI: 0.98–1.56); IRR for moderate/decreasing: 1.34 (95 % CI: 1.04–1.74); IRR for moderate/increasing: 1.62 (95 % CI: 1.28–2.13); and IRR for consistently high: 1.74 (95 % CI = 1.29–2.41). Similar results were observed for adolescent externalizing symptoms. Stronger effects of maternal parenting stress trajectories on adolescent externalizing symptoms were observed among native-born vs. immigrant families. LimitationsDifferential attrition and same-source bias may lead to under- or over-estimation of the associations of interest. ConclusionsInterventions targeting unmarried families with elevated maternal parenting stress during childhood may reduce behavioral symptoms in adolescence.

Exploring shared frailty models for cluster-specific risk estimation: A study on diabetes patients with a history of acute coronary syndrome

This study proposes the use of semiparametric log-normal shared frailty models to analyze time-to-event data for individuals with similar features referred to as clusters. Shared frailty models are useful for modeling and estimating common risk in the lifetimes of individuals in these clusters. While various methods have been proposed for estimating shared frailty models, few studies have explored the use of the pseudo-full-likelihood method. In this study, the pseudo-full-likelihood and hierarchical likelihood approaches were used to construct and estimate parameter estimates and check for asymptotic properties via simulations. Log-normal semiparametric frailty model was used to obtain cluster-specific frailty based on the semiparametric log-normal shared frailty distribution. The results of both methods were compared, and prediction intervals for a random effect were obtained. To further investigate the existence of shared frailty in diabetes patients and a history of acute coronary syndrome (STEMI and NSTEMI), data from UK Biobank was used. The results suggest the presence of frailty within the clusters and indicate cluster time dependence in the study population. Overall, this study highlights the potential benefits of using the pseudo-full-likelihood method in shared frailty modeling and provides insights into the impact of observed variabilities on hazards within clusters.

Application of Semi-parametric Spatial Lag Model in the Study of the Relationship Between Innovation Efficiency and Industrial Development in China’s Pharmaceutical Manufacturing Industry

Application of Semi-parametric Spatial Lag Model in the Study of the Relationship Between Innovation Efficiency and Industrial Development in China’s Pharmaceutical Manufacturing Industry

Copula-based analysis of dependent current status data with semiparametric linear transformation model.

This paper discusses regression analysis of current status data with dependent censoring, a problem that often occurs in many areas such as cross-sectional studies, epidemiological investigations and tumorigenicity experiments. Copula model-based methods are commonly employed to tackle this issue. However, these methods often face challenges in terms of model and parameter identification. The primary aim of this paper is to propose a copula-based analysis for dependent current status data, where the association parameter is left unspecified. Our method is based on a general class of semiparametric linear transformation models and parametric copulas. We demonstrate that the proposed semiparametric model is identifiable under certain regularity conditions from the distribution of the observed data. For inference, we develop a sieve maximum likelihood estimation method, using Bernstein polynomials to approximate the nonparametric functions involved. The asymptotic consistency and normality of the proposed estimators are established. Finally, to demonstrate the effectiveness and practical applicability of our method, we conduct an extensive simulation study and apply the proposed method to a real data example.

A general semi-parametric elliptical distribution model for semi-supervised learning

ABSTRACT This research proposes a novel semi-parametric elliptical distribution model for application in semi-supervised learning tasks. We use labelled and unlabelled data to develop a pseudo maximum likelihood method for estimation and classification. The proposed estimator outperforms the estimator based solely on labelled data and achieves the semi-parametric efficiency bound with a suitable size of unlabelled data. We efficiently maximise the objective function by utilising low-dimensional groupwise pseudo-likelihood functions in a block coordinate descent manner while ensuring numerical stability and convergence through appropriate bandwidth selectors and initial parameter estimates. Additionally, the study comprehensively investigates the impact of labelled and unlabelled data on the pseudo maximum likelihood estimator and classifier. Simulation studies and empirical data applications illustrate the superiority of our methodology.

Optimal subsampling for semi-parametric accelerated failure time models with massive survival data using a rank-based approach.

Subsampling is a practical strategy for analyzing vast survival data, which are progressively encountered across diverse research domains. While the optimal subsampling method has been applied to inferences for Cox models and parametric accelerated failure time (AFT) models, its application to semi-parametric AFT models with rank-based estimation have received limited attention. The challenges arise from the non-smooth estimating function for regression coefficients and the seemingly zero contribution from censored observations in estimating functions in the commonly seen form. To address these challenges, we develop optimal subsampling probabilities for both event and censored observations by expressing the estimating functions through a well-defined stochastic process. Meanwhile, we apply an induced smoothing procedure to the non-smooth estimating functions. As the optimal subsampling probabilities depend on the unknown regression coefficients, we employ a two-step procedure to obtain a feasible estimation method. An additional benefit of the method is its ability to resolve the issue of underestimation of the variance when the subsample size approaches the full sample size. We validate the performance of our estimators through a simulation study and apply the methods to analyze the survival time of lymphoma patients in the surveillance, epidemiology, and end results program.

Semiparametric Additive Modeling of the Restricted Mean Survival Time.

Analysis of the restricted mean survival time (RMST) has become increasingly common in biomedical studies during the last decade as a means of estimating treatment or covariate effects on survival. Advantages of RMST over the hazard ratio (HR) include increased interpretability and lack of reliance on the often tenuous proportional hazards assumption. Some authors have argued that RMST regression should generally be the frontline analysis as opposed to methods based on counting process increments. However, in order for the use of the RMST to be more mainstream, it is necessary to broaden the range of data structures to which pertinent methods can be applied. In this report, we address this issue from two angles. First, most of existing methodological development for directly modeling RMST has focused on multiplicative models. An additive model may be preferred due to goodness of fit and/or parameter interpretation. Second, many settings encountered nowadays feature high-dimensional categorical (nuisance) covariates, for which parameter estimation is best avoided. Motivated by these considerations, we propose stratified additive models for direct RMST analysis. The proposed methods feature additive covariate effects. Moreover, nuisance factors can be factored out of the estimation, akin to stratification in Cox regression, such that focus can be appropriately awarded to the parameters of chief interest. Large-sample properties of the proposed estimators are derived, and a simulation study is performed to assess finite-sample performance. In addition, we provide techniques for evaluating a fitted model with respect to risk discrimination and predictive accuracy. The proposed methods are then applied to liver transplant data to estimate the effects of donor characteristics on posttransplant survivaltime.

Hybrid Modeling for On-Line Fermentation Optimization and Scale-Up: A Review

Modeling is a crucial tool in the biomanufacturing industry, namely in fermentation processes. This work discusses both mechanistic and data-driven models, each with unique benefits and application potential. It discusses semi-parametric hybrid modeling, a growing field that combines these two types of models for more accurate and easy result extrapolation. The characteristics and structure of such hybrid models will be examined. Moreover, its versatility will be highlighted, showing its usefulness in various stages of process development, including real-time monitoring and optimization. Scale-up remains one of the most relevant topics in fermentation processes, as it is important to have reproducible critical quality attributes, such as titer and yield, on larger scales. Furthermore, the process still relies on empirical correlations and iterative optimization. For these reasons, it is important to improve scale-up predictions, through e.g., the use of digital tools. Perspectives will be presented on the potential that hybrid modeling has by predicting performance across different process scales. This could provide more efficient and reliable biomanufacturing processes that require less resource consumption through experimentation.

Resolving the Stellar-Collapse and Hierarchical-Merger Origins of the Coalescing Black Holes.

Spin and mass properties provide essential clues in distinguishing the origins of coalescing black holes (BHs). With a dedicated semiparametric population model for the coalescing binary black holes (BBHs), we identify two distinct categories of BHs among the GWTC-3 events, which is favored over the one population scenario by a logarithmic Bayes factor (lnB) of 7.5. One category, with a mass ranging from ∼25M_{⊙} to ∼80M_{⊙}, is distinguished by the high spin magnitudes (∼0.75) and consistent with the hierarchical merger origin. The other category, characterized by low spins, has a sharp mass cutoff at ∼40M_{⊙}, which is natural for the stellar-collapse origin and in particular the pair-instability explosion of massive stars. We infer the local hierarchical merger rate density as 0.46_{-0.24}^{+0.61} Gpc^{-3} yr^{-1}. Additionally, we find that a fraction of the BBHs has a cosine-spin-tilt-angle distribution concentrated preferentially around 1, and the fully isotropic distribution for spin orientation is disfavored by a lnB of -6.3, suggesting that the isolated field evolution channels are contributing to the total population.