Academic literature on the topic 'Semiparameteric'

We develop a new model of household sorting in a system of residential neighborhoods. We show that this model is partially identified without imposing parametric restrictions on the distribution of unobserved tastes for neighborhood quality and the shape of the indirect utility function. The proof of identification is constructive and can be used to derive a new semiparameteric estimator. Our empirical application focuses on residential choices in the Pittsburgh metropolitan area. We find that sorting of households with children exhibit more stratification by income than sorting of households without children. (JEL C51, D12, H41, J12, R21, R23)

Panel count data often occur in a long-term recurrent event study, where the exact occurrence time of the recurrent events is unknown, but only the occurrence count between any two adjacent observation time points is recorded. Most traditional methods only handle panel count data for a single type of event. In this paper, we propose a Bayesian semiparameteric approach to analyze panel count data for multiple types of events. For each type of recurrent event, the proportional mean model is adopted to model the mean count of the event, where its baseline mean function is approximated by monotone I-splines. The correlation between multiple types of events is modeled by common frailty terms and scale parameters. Unlike many frequentist estimating equation methods, our approach is based on the observed likelihood and makes no assumption on the relationship between the recurrent process and the observation process. Under the Poisson counting process assumption, we develop an efficient Gibbs sampler based on novel data augmentation for the Markov chain Monte Carlo sampling. Simulation studies show good estimation performance of the baseline mean functions and the regression coefficients; meanwhile, the importance of including the scale parameter to flexibly accommodate the correlation between events is also demonstrated. Finally, a skin cancer data example is fully analyzed to illustrate the proposed methods.

A weighted semiparameter estimate model is proposed. The parameter components and nonparameter components are weighted. The weights are determined by the characters of different data. Simulation data and real GPS data are both processed by the new model and least square estimate, ridge estimate, and semiparameter estimate. The main research method is to combine qualitative analysis and quantitative analysis. The deviation between estimated values and the true value and the estimated residuals fluctuation of different methods are used for qualitative analysis. The mean square error is used for quantitative analysis. The results of experiment show that the model has the smallest residual error and the minimum mean square error. The weighted semiparameter estimate model has effectiveness and high precision.

This paper proposed a panel data clustering model based on D-vine and C-vine and supported a semiparametric estimation for parameters. These models include a two-step inference function for margins, two-step semiparameter estimation, and stepwise semiparametric estimation. In similarity measurement, similarity coefficients are constructed by a multivariate Hierarchical Nested Archimedean Copula (HNAC) model and compound PCC models, which are HNAC and D-vine compound model and HNAC and C-vine compound model. Estimation solutions and models evaluation are given for these models. In the case study, the clustering results of HNAC and D-vine compound model and HNAC and C-vine compound model are given, and the effect of different copula families on clustering results is also discussed. The result shows the models are effective and useful.

Based on electrical measurements from electrodes placed around the boundary of a body, electrical impedance tomography (EIT) is an imaging procedure that recovers the spatial distribution of the conductivities in the interior of a body. Recent studies have shown promising results in reconstructing EIT images using heuristic algorithms. This work presents a study of the applicability of six heuristic algorithms – firefly algorithm (FA), novel bat algorithm (NBA), genetic algorithm with new multi-parent crossover (GA-MPC), success history-based adaptive differential evolution with linear population size reduction with semiparameter adaptation hybrid with covariance matrix adaptation evolutionary strategy (LSHADE-SPACMA), ensemble sinusoidal differential covariance matrix adaptation (LSHADE-cnEpSin), and effective butterfly optimizer with covariance matrix adapted retreat phase (EBOwithCMAR) – for the EIT image reconstruction problem. These algorithms have never been employed to solve the EIT inverse problem. Series of numerical tests were carried out to compare the performance of the selected algorithms.