Additional information and borrowing strength from the related sites and other sources will improve estimation in small areas. Generalized linear mixed-effects models (GLMMs) have been frequently used in small area estimation; however, the relationsh...
This paper proposes a semiparametric spatial lag model and develops a Bayesian estimation method for this model. In the estimation of the model, the paper combines Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm, random walk Metropolis sa...
The GARCH model is one of the most influential models for characterizing and predicting fluctuations in economic and financial studies. However, most traditional GARCH models commonly use daily frequency data to predict the return, correlation, and r...
Multivariate “Bayesian” regression via a shared component model has gained popularity in recent years, particularly in modeling and mapping the risks associated with multiple diseases. This method integrates joint outcomes, fixed effects...
This paper proposes a variable selection method for a semiparametric varying coefficient spatial autoregressive panel model with fixed effects based on a penalized profile quasi-likelihood method, which can simultaneously select significant variables...
23 December 2022
Based on a sample of 92 listed renewable energy enterprises in China from 2007–2017, this paper empirically examines the nonlinear effect of environmental policies on renewable energy investments using a semiparametric regression model. Environ...
In this paper, we introduce two semiparametric single-index models for spatially and temporally correlated data. Our first model has spatially and temporally correlated random effects that are additive to the nonparametric function, which we refer to...
The mixture of generalised linear models (MGLM) requires knowledge about each mixture component’s specific exponential family (EF) distribution. This assumption is relaxed and a mixture of semi-parametric generalised linear models (MSPGLM) appr...
Many semiparametric spatial autoregressive (SSAR) models have been used to analyze spatial data in a variety of applications; however, it is a common phenomenon that heteroscedasticity often occurs in spatial data analysis. Therefore, when considerin...
This paper introduces stochastic disturbances into a semi-parametric SEIR model with infectivity in an incubation period. The model combines the randomness of disease transmission and the nonlinearity of transmission rate, providing a flexible framew...
This paper investigates a stochastic semi-parametric SEIR model characterized by infectivity during the incubation period and influenced by white noise perturbations. First, based on the theory of stochastic persistence, we derive the conditions requ...
Practitioners often face the situation of comparing any set of k distributions, which may follow neither normality nor equality of variances. We propose a semiparametric model to compare those distributions using an exponential tilt method. This exte...
A semi-parametric mixture model, combining kernel density estimation (KDE) and the generalized Pareto distribution (GPD), is applied to analyze the statistical characteristics of earthquake magnitudes. Data below a threshold are fitted using KDE, whi...
This paper investigates the incentive of credit rating agencies (CRAs) to bias ratings using a semiparametric, ordered-response model. The proposed model explicitly takes conflicts of interest into account and allows the ratings to depend flexibly on...
To describe the stylized features of volatility comprehensively, this paper embeds the time-varying leverage effect of volatility into the Realized Generalized AutoRegressive Conditional Heteroskedasticity (RG) model and proposes a new volatility mod...
A new sparse semiparametric functional model is proposed, which tries to incorporate the influence of two functional variables in a scalar response in a quite simple and interpretable way. One of the functional variables is included trough a single-i...
This paper introduces a parsimonious and yet flexible semiparametric model to forecast financial volatility. The new model extends a related linear nonnegative autoregressive model previously used in the volatility literature by way of a power transf...
This paper proposes a semiparametric realized stochastic volatility model by integrating the parametric stochastic volatility model utilizing realized volatility information and the Bayesian nonparametric framework. The flexible framework offered by...
In clinical and epidemiological studies, when the time-to-event(s) and the longitudinal outcomes are associated, modelling them separately may give biased estimates. A joint modelling approach is required to obtain unbiased results and to evaluate th...
Network vector AutoRegressive models play a vital role in multivariate time series analysis. However, previous research in the classic Network vector AutoRegressive (NAR) model is limited to strict assumptions of linearity and time-invariance of node...
Semi- and non-parametric mixture of normal regression models are a flexible class of mixture of regression models. These models assume that the component mixing proportions, regression functions and/or variances are non-parametric functions of the co...
In the past few decades, model averaging has received extensive attention, and has been regarded as a feasible alternative to model selection. However, this work is mainly based on parametric model framework and complete dataset. This paper develops...
Battery model identification is very important for reliable battery management as well as for battery system design process. The common problem in identifying battery models is how to determine the most appropriate mathematical model structure and pa...
This paper proposes a semiparametric local polynomial estimator for modelling agricultural time-series. We consider the modelling of the crop yield variable according to determined financial risk factors in Turkey. The derivation of a semiparametric...
Despite the fact that growth theories suggest that natural disasters should have an impact on economic growth, parametric empirical studies have provided little to no evidence supporting that prediction. On the other hand, pure nonparametric regressi...
This paper focuses on studying a random effects semiparametric regression model (RESPRM) with separable space-time filters. The model cannot only capture the linearity and nonlinearity existing in a space-time dataset, but also avoid the inefficient...
A multiresponse multipredictor semiparametric regression (MMSR) model is a combination of parametric and nonparametric regressions models with more than one predictor and response variables where there is correlation between responses. Due to this co...
Semiparametric panel data models are powerful tools for analyzing data with complex characteristics such as linearity and nonlinearity of covariates. This study aims to investigate the estimation and testing of a random effects semiparametric model (...
Airborne laser scanning (ALS) acquisitions provide piecemeal coverage across the western US, as collections are organized by local managers of individual project areas. In this study, we analyze different factors that can contribute to developing a r...
This work introduces a straightforward framework for semiparametric non-linear models as an alternative to existing non-linear parametric models, whose interpretation primarily depends on biological or physical aspects that are not always available i...
In survival analysis, interval-censored data and panel count data represent two prevalent types of incomplete data. Given that, within certain research contexts, the events of interest may simultaneously involve both data types, it is imperative to p...
When handling longitudinal data in regression models, we often encounter problems involving two interrelated response variables. These response variables may display an unknown curve shape in their relationship with one predictor variable, referred t...
Growing interest in genomics research has called for new semiparametric models based on kernel machine regression for modeling health outcomes. Models containing redundant predictors often show unsatisfactory prediction performance. Thus, our task is...
This note revisits the ideas of the so-called semiparametric methods that we consider to be very useful when applying machine learning in insurance. To this aim, we first recall the main essence of semiparametrics like the mixing of global and local...
There is a causal interaction between urban rail passenger flow and the station-built environment. Analyzing the implicit relationship can help clarify rail transit operations or improve the land use planning of the station. However, to characterize...
Management of financial risks and sound decision making rely on the accurate information and predictive models. Drawing useful information efficiently from big data with complex structures and building accurate models are therefore crucial tasks. Mos...
This paper compares two statistical methods for parameter reconstruction (random drift and diffusion coefficients of the Itô stochastic differential equation, SDE) in the problem of stochastic modeling of air–sea heat flux increment evolu...
By drawing on the concept of sustainable economic development, this study advances the research on debt sustainability in the economic literature. We explore the correlation between local government debt and regional economic growth in 30 provinces i...
Chilean seismic activity is one of the strongest in the world. As already shown in previous papers, seismic activity can be usefully described by a space–time branching process, such as the ETAS (Epidemic Type Aftershock Sequences) model, which is a...
There is a difficulty in finding an estimate of the standard error (SE) of the profile likelihood estimator in the joint model of longitudinal and survival data. The difficulty is on the differentiation of an implicit function that appear in the prof...
Climate change has several negative effects on health, including cardiovascular disease. Many studies have considered the effect of temperature on cardiovascular disease and found that there is an association between extreme levels of temperature, co...
We study the non-parametric estimation of partially linear generalized single-index functional models, where the systematic component of the model has a flexible functional semi-parametric form with a general link function. We suggest an efficient an...
This paper experiments an artificial neural networks model with Bayesian approach on a small real estate sample. The output distribution has been calculated operating a numerical integration on the weights space with the Markov Chain Hybrid Monte Car...
A Bayesian semiparametric model framework is proposed to analyze multivariate longitudinal data. The new framework leads to simple explicit posterior distributions of model parameters. It results in easy implementation of the MCMC algorithm for estim...
23 March 2011
A semi-parametric econometric model is used to study the relationship between malaria cases and climatic factors in 25 African countries. Results show that a marginal change in temperature and precipitation levels would lead to a significant change i...
We consider a regression analysis of multivariate interval-censored failure time data where the censoring may be informative. To address this, an approximated maximum likelihood estimation approach is proposed under a general class of semiparametric...
We consider a stochastic frontier model in which a deviation of output from the production frontier consists of two components, a one-sided technical inefficiency and a two-sided random noise. In such a situation, we develop a semiparametric regressi...
Regression models for continuous outcomes frequently require a transformation of the outcome, which is often specified a priori or estimated from a parametric family. Cumulative probability models (CPMs) nonparametrically estimate the transformation...
This paper examines the world wheat market leadership using price discovery occurring in wheat futures markets of the United States (U.S.) and Europe. An error correction model (ECM) generalized autoregressive conditional heteroskedasticity (GARCH),...
Research has examined the impact of green payment programs on agricultural and economic outcomes such as agricultural productivity and farm income. However, it is unclear whether these policies are capitalized into farmland prices. This paper provide...
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