The British low-wage sector and the employment prospects of the unemployed

Are low wages a way for the unemployed to switch to higher-paying jobs? Using data from the British Household Panel Survey, the labour market dynamics of unemployed, low-paid and higher-paid employed men are analysed. Moreover, the respective (un)employment duration and occupational skill level are accounted for. Results show that in general low wages significantly reduce the risk of future unemployment and increase the chances of ascending the salary ladder, especially in the case of long-term unemployment (>360 days). Furthermore, the occupational skill level has a substantial influence on the upward mobility of low-paid jobs: individuals working in the initial period in a low-paid and higher-skilled occupation have on average an 11 percentage points higher probability of entering higher pay compared to when working in a low-paid and low-skilled occupation.     

Modelling of repairable systems with various degrees of repair

The availability of a stochastic repairable system depends on the failure behaviour and on repair strategies. In this paper, we deal with a general repair model for a system using auxiliary counting processes and corresponding intensities which include various degrees of repair (between minimal repair and perfect repair). For determining the model parameters we need estimators depending on failure times and repair times: maximum likelihood (ML) estimator and Bayes estimators are considered. Special results are obtained by the use of Weibull-type intensities and random observation times.     

Some properties of the length of the shortest half

This paper discusses some properties of the length of the shortest half proposed by Rousseeuw and Leroy (1988) as a robust scale estimator.     

ML– and semiparametric estimation in logistic models with incomplete covariate data

ML–estimation of regression parameters with incomplete covariate information usually requires a distributional assumption regarding the concerned covariates that implies a source of misspecification. Semiparametric procedures avoid such assumptions at the expense of efficiency. In this paper a simulation study with small sample size is carried out to get an idea of the performance of the ML–estimator under misspecification and to compare it with the semiparametric procedures when the former is based on a correct assumption. The results show that there is only a little gain by correct parametric assumptions, which does not justify the possibly large bias when the assumptions are not met. Additionally, a simple modification of the complete case estimator appears to be nearly semiparametric efficient.     

MSE-improvement of the least squares estimator by dropping variables

It is well known that dropping variables in regression analysis decreases the variance of the least squares (LS) estimator of the remaining parameters. However, after elimination estimates of these parameters are biased, if the full model is correct. In his recent paper, Boscher (1991) showed that the LS-estimator in the special case of a mean shift model (cf. Cook and Weisberg, 1982) which assumes no “outliers” can be considered in the framework of a linear regression model where some variables are deleted. He derived conditions under which this estimator outperforms the LS-estimator of the full model in terms of the mean squared error (MSE)-matrix criterion. We demonstrate that this approach can be extended to the general set-up of dropping variables. Necessary and sufficient conditions for the MSE-matrix superiority of the LS-estimator in the reduced model over that in the full model are derived. We also provide a uniformly most powerful F-statistic for testing the MSE-improvement.     

用神经网络对开式冷挤压极限变形程度的预测

给出了基于 BP网络的极限变形程度预测的分析方法 ,得到了开式冷挤压εmax神经网络计算结果 ,与实验结果比较 ,其精度较高 ,证明了神经网络模型在开式冷挤压工艺中应用的可行性。     

Modified efficient importance sampling for partially non‐Gaussian state space models

The construction of an importance density for partially non‐Gaussian state space models is crucial when simulation methods are used for likelihood evaluation, signal extraction, and forecasting. The method of efficient importance sampling is successful in this respect, but we show that it can be implemented in a computationally more efficient manner using standard Kalman filter and smoothing methods. Efficient importance sampling is generally applicable for a wide range of models, but it is typically a custom‐built procedure. For the class of partially non‐Gaussian state space models, we present a general method for efficient importance sampling. Our novel method makes the efficient importance sampling methodology more accessible because it does not require the computation of a (possibly) complicated density kernel that needs to be tracked for each time period. The new method is illustrated for a stochastic volatility model with a Student's t distribution.     

基于最大熵原理的政府投资工程委托代建风险研究

本文根据政府投资工程的风险特点,引入了熵的概念,建立了最大熵风险分析模型,利用熵权的特性求出各指标的客观权重,同时利用专家打分法得出各指标的主观权重,将两者结合作为各指标的综合权重,为进一步对风险指标的深入分析及风险决策研究奠定了良好的基础。最后,用实例验证了该模型的可行性和实用性。     

Credit risk,managerial behaviour and macroeconomic equilibrium within dual banking systems: Interest-free vs. interest-based banking industries

In this paper, an attempt has been made to explore the determinants of credit risk in the banking system with a particular interest toward the Islamic banking industry. We analyze the link between credit risk and a set of bank-specific and macroeconomic along with institutional variables using two complementary approaches. First, we investigate the factors of credit risk using one-step generalized method of moments (GMM) system estimator. Then, we explore the feedback between credit risk and its determinants in a panel vector autoregressive (PVAR) model. We have used a sample of Middle Eastern, North African (MENA) and Asian countries to apply our model. The major purpose of this paper is to find factors that could explain credit risk within the interest-free banking system relative to the interest-based one.     

Numerical Maximisation of Likelihood: A Neglected Alternative to EM?

There is by now a long tradition of using the EM algorithm to find maximum‐likelihood estimates (MLEs) when the data are incomplete in any of a wide range of ways, even when the observed‐data likelihood can easily be evaluated and numerical maximisation of that likelihood is available as a conceptually simple route to the MLEs. It is rare in the literature to see numerical maximisation employed if EM is possible. But with excellent general‐purpose numerical optimisers now available free, there is no longer any reason, as a matter of course, to avoid direct numerical maximisation of likelihood. In this tutorial, I present seven examples of models in which numerical maximisation of likelihood appears to have some advantages over the use of EM as a route to MLEs. The mathematical and coding effort is minimal, as there is no need to derive and code the E and M steps, only a likelihood evaluator. In all the examples, the unconstrained optimiser nlm available in R is used, and transformations are used to impose constraints on parameters. I suggest therefore that the following question be asked of proposed new applications of EM: Can the MLEs be found more simply and directly by using a general‐purpose numerical optimiser?