运动Dyon粒子双矢势场方程的格林函数解

首先回顾了有磁荷(或等效磁荷)存在的情况下电磁场双四维势的描述方法，给出了场强与双四维势的关系以及具有电磁对偶对称性的Maxwell方程；利用Green函数法求出了场方程具有Lorentz变换协变性的推迟解；最后给出了广义李纳-魏谢尔势的表述形式.     

On the Stationary Version of the Generalized Hyperbolic ARCH Model

This paper finds conditions under which the generalized hyperbolic ARCH-type model is strictly stationary. Properties of the model are investigated and in particular an estimation procedure is proposed. The resulting stationary model provides with a robust non-Gaussian ARCH-type alternative.     

Types of likelihood maxima in mixture models and their implication on the performance of tests

In two-component mixtures of exponential distributions, different strategies for starting the likelihood maximization algorithm converge to different types of maxima. The power of an LR test of homogeneity against such a mixture strongly depends on the considered strategy, and global maximization need not result in the largest power. An explanation is given on basis of a systematic investigation of the likelihood function in a large number of simulations, using a variety of diagnostic tools. Thereby, we also gain a deeper insight into the properties of the samples that generate particular types of solutions of the likelihood equation. In particular, “spurious solutions” often occur; these are mainly responsible for the fact that global maximization may not result in a statistically meaningful estimator. Removing the smallest elements of a sample may drastically increase the power of previously inferior strategies. This research has been supported by a grant from the Deutsche Forschungsgemeinschaft.     

General EM algorithm for fitting non-monotone hazard functions from truncated and censored observations

Recently, many researchers focused on modeling non-monotonic hazard functions such as bath-tube and hump shapes. However, most of their estimation methods are focused on complete observations. Since reliability data are typically censored and truncated, a general EM algorithm is proposed, which can fit any of those complex hazard functions. The proposed EM algorithm is analyzed by fitting well-known 4-parameter hazard functions, where its performance is compared by their specific direct methods through extensive Monte Carlo simulations.     

A validated solid-liquid extraction method for the HPLC determination of polyphenols in apple tissues Comparison with pressurised liquid extraction

A solid-liquid extraction procedure followed by reversed-phase high-performance liquid chromatography (RP-HPLC) coupled with a photodiode array detector (DAD) for the determination of polyphenols in freeze-dried apple peel and pulp is reported. The extraction step consists in sonicating 0.5 g of freeze-dried apple tissue with 30 mL of methanol-water-acetic acid (30:69:1, v/v/v) containing 2 g of ascorbic acid/L, for 10 min in an ultrasonic bath. The whole method was validated, concluding that it is a robust method that presents high extraction efficiencies (peel: >91%, pulp: >95%) and appropriate precisions (within day: R.S.D. (n = 5) <5%, and between days: R.S.D. (n = 5) <7%) at the different concentration levels of polyphenols that can be found in apple samples. The method was compared with one previously published, consisting in a pressurized liquid extraction (PLE) followed by RP-HPLC-DAD determination. The advantages and disadvantages of both methods are discussed.     

Two-Dimensional Detergent Expansion Strategy for Membrane Protein Studies

Detergents are the most frequently applied reagents in membrane protein (MP) studies. The limited diversity of one-head-one-tailed traditional detergents, however, is far from sufficient for structurally distinct MPs. Expansion of detergent repertoire has a continuous momentum. In line with the speculation that detergent pre-assembly exerts superiority, herein we report for the first time cross-conjugation of two series of monomeric detergents for constructing a two-dimensional library of dimeric detergents. Optimum detergents stood out with unique preferences in the systematic evaluation of individual MPs. Furthermore, unprecedented hybrid detergents 14M8G and 14M9G enabled high-quality EM study of transporter MsbA and NMR study of G protein-coupled receptor A_2AAR, respectively. Given the abundance of cross-coupling chemistries, comprehensive diversity could be readily covered that would facilitate the finding of new detergents for the manipulation of thorny MPs and innovation of the functional and structural study in future.     

Multivariate measurement error models for replicated data under heavy‐tailed distributions

In this paper, we deal with multivariate measurement error models for replicated data under heavy‐tailed distributions, providing appealing robust and adaptable alternatives to the usual Gaussian assumptions. The models contain both error‐prone covariates and predictors measured without errors. The surrogates of the response and the multiple error‐prone covariates are replicated and are allowed unpaired and/or unequal cases. Under the scale mixtures of normal distribution class, we provide an explicit iterative formula of the maximum likelihood estimation via an expectation‐maximization‐type algorithm. Closed forms of asymptotic variances of the estimators are also given. The effect and robustness performances are confirmed by the simulation studies. Two real data sets are analyzed by the proposed models. Copyright © 2015 John Wiley & Sons, Ltd.     

Numerical approximation of a stochastic age-structured population model in a polluted environment with Markovian switching

In this paper, a stochastic age-structured population model with Markovian switching is investigated in a polluted environment. Both the stochastic disturbance of environment and the Markovian switching are incorporated into the model. By Itô formula and several assumptions, the boundedness in the qth moment of exact solutions of model are proved. Furthermore, making use of truncated Euler–Maruyama (EM) method, the strong convergence criterion of numerical approximation in the qth moment is established, and the rate of convergence is estimated. Numerical simulations are carried out to illustrate the theoretical results. Our results indicate that the truncated EM method can be used for stochastic age-structured population system in a polluted environment.     

Estimation and status prediction in a discrete mover‐stayer model with covariate effects on stayer's probability

A discrete‐time mover‐stayer (MS) model is an extension of a discrete‐time Markov chain, which assumes a simple form of population heterogeneity. The individuals in the population are either stayers, who never leave their initial states or movers who move according to a Markov chain. We, in turn, propose an extension of the MS model by specifying the stayer's probability as a logistic function of an individual's covariates. Such extension has been recently discussed for a continuous time MS but has not been considered before for a discrete time one. This extension allows for an in‐sample classification of subjects who never left their initial states into stayers or movers. The parameters of an extended MS model are estimated using the expectation‐maximization algorithm. A novel bootstrap procedure is proposed for out of sample validation of the in‐sample classification. The bootstrap procedure is also applied to validate the in‐sample classification with respect to a more general dichotomy than the MS one. The developed methods are illustrated with the data set on installment loans. But they can be applied more broadly in credit risk area, where prediction of creditworthiness of a loan borrower or lessee is of major interest.     

Computational issues in parameter estimation for stationary hidden Markov models

The parameters of a hidden Markov model (HMM) can be estimated by numerical maximization of the log-likelihood function or, more popularly, using the expectation–maximization (EM) algorithm. In its standard implementation the latter is unsuitable for fitting stationary hidden Markov models (HMMs). We show how it can be modified to achieve this. We propose a hybrid algorithm that is designed to combine the advantageous features of the two algorithms and compare the performance of the three algorithms using simulated data from a designed experiment, and a real data set. The properties investigated are speed of convergence, stability, dependence on initial values, different parameterizations. We also describe the results of an experiment to assess the true coverage probability of bootstrap-based confidence intervals for the parameters.