Semi-varying coefficient multinomial logistic regression for disease progression risk prediction

This paper proposes a risk prediction model using semi-varying coefficient multinomial logistic regression. We use a penalized local likelihood method to do the model selection and estimate both functional and constant coefficients in the selected model. The model can be used to improve predictive modelling when non-linear interactions between predictors are present. We conduct a simulation study to assess our method's performance, and the results show that the model selection procedure works well with small average numbers of wrong-selection or missing-selection. We illustrate the use of our method by applying it to classify the patients with early rheumatoid arthritis at baseline into different risk groups in future disease progression. We use a leave-one-out cross-validation method to assess its correct prediction rate and propose a recalibration framework to evaluate how reliable are the predicted risks

Structure Identification in Panel Data Analysis

Panel data analysis is an important topic in statistics and econometrics. In such analysis, it is very common to assume the impact of a covariate on the response variable remains constant across all individuals. While the modelling based on this assumption is reasonable when only the global effect is of interest, in general, it may overlook some individual/subgroup attributes of the true covariate impact. In this paper, we propose a data driven approach to identify the groups in panel data with interactive effects induced by latent variables. It is assumed that the impact of a covariate is the same within each group, but different between the groups. An EM based algorithm is proposed to estimate the unknown parameters, and a binary segmentation based algorithm is proposed to detect the grouping. We then establish asymptotic theories to justify the proposed estimation, grouping method, and the modelling idea. Simulation studies are also conducted to compare the proposed method with the existing approaches, and the results obtained favour our method. Finally, the proposed method is applied to analyse a data set about income dynamics, which leads to some interesting findings

Model selection and structure specification in ultra-high dimensional generalised semi-varying coefficient models

In this paper, we study the model selection and structure specification for the generalised semi-varying coefficient models (GSVCMs), where the number of potential covariates is allowed to be larger than the sample size.We first propose a penalised likelihood method with the LASSO penalty function to obtain the preliminary estimates of the functional coefficients. Then, using the quadratic approximation for the local log-likelihood function and the adaptive group LASSO penalty (or the local linear approximation of the group SCAD penalty) with the help of the preliminary estimation of the functional coefficients, we introduce a novel penalised weighted least squares procedure to select the significant covariates and identify the constant coefficients among the coefficients of the selected covariates, which could thus specify the semiparametric modelling structure. The developed model selection and structure specification approach not only inherits many nice statistical properties from the local maximum likelihood estimation and nonconcave penalised likelihood method, but also computationally attractive thanks to the computational algorithm that is proposed to implement our method. Under some mild conditions, we establish the asymptotic properties for the proposed model selection and estimation procedure such as the sparsity and oracle property.We also conduct simulation studies to examine the finite sample performance of the proposed method, and finally apply the method to analyse a real data set, which leads to some interesting findings

A neural network-based adaptive power-sharing strategy for hybrid frame inverters in a microgrid

The capacitive-coupling inverter (CCI) is more cost-effective in reactive power conditioning and enhanced reactive power regulation ability when compared with the inductive-coupling inverter (ICI). As power conditioning capability is vital for a microgrid (MG) system, a new MG frame with hybrid parallel-connected ICIs and CCIs was proposed in this paper. With lower DC-link voltage for the CCI, an adaptive power sharing method was proposed for reducing total rated power and losses. A power-sharing control layer based on a back-propagation neural network that guarantees rapid and accurate sharing ratio computation was investigated as well. The results of simulations and experiments were used to verify the effectiveness of the proposed method

The ER-membrane transport system is critical for intercellular trafficking of the NSm movement protein and Tomato Spotted Wilt Tospovirus

Plant viruses move through plasmodesmata to infect new cells. The plant endoplasmic reticulum (ER) is interconnected among cells via the ER desmotubule in the plasmodesma across the cell wall, forming a continuous ER network throughout the entire plant. This ER continuity is unique to plants and has been postulated to serve as a platform for the intercellular trafficking of macromolecules. In the present study, the contribution of the plant ER membrane transport system to the intercellular trafficking of the NSm movement protein and Tomato spotted wilt tospovirus (TSWV) is investigated. We showed that TSWV NSm is physically associated with the ER membrane in Nicotiana benthamiana plants. An NSm-GFP fusion protein transiently expressed in single leaf cells was trafficked into neighboring cells. Mutations in NSm that impaired its association with the ER or caused its mis-localization to other subcellular sites inhibited cell-to-cell trafficking. Pharmacological disruption of the ER network severely inhibited NSm-GFP trafficking but not GFP diffusion. In the Arabidopsis thaliana mutant rhd3 with an impaired ER network, NSm-GFP trafficking was significantly reduced, whereas GFP diffusion was not affected. We also showed that the ER-to-Golgi secretion pathway and the cytoskeleton transport systems were not involved in the intercellular trafficking of TSWV NSm. Importantly, TSWV cell-to-cell spread was delayed in the ER-defective rhd3 mutant, and this reduced viral infection was not due to reduced replication. On the basis of robust biochemical, cellular and genetic analysis, we established that the ER membrane transport system serves as an important direct route for intercellular trafficking of NSm and TSWV