浅析语言学习中的错误及对策

学生语言学习的错误主要有两类操作性错误即语法错误与交际性错误,对于一般性语法错误,不要纠正过多.对于交际性错误要给予重视,要注意加强社会文化教育.     

On the asymptotic normality of the R-estimators of the slope parameters of simple linear regression models with associated errors

ABSTRACT

The purpose of this paper is to prove, under mild conditions, the asymptotic normality of the rank estimator of the slope parameter of a simple linear regression model with stationary associated errors. This result follows from a uniform linearity property for linear rank statistics that we establish under general conditions on the dependence of the errors. We prove also a tightness criterion for weighted empirical process constructed from associated triangular arrays. This criterion is needed for the proofs which are based on that of Koul [Behavior of robust estimators in the regression model with dependent errors. Ann Stat. 1977;5(4):681–699] and of Louhichi [Louhichi S. Weak convergence for empirical processes of associated sequences. Ann Inst Henri Poincaré Probabilités Statist. 2000;36(5):547–567].     

Comparison of the effectiveness of forecasts obtained by means of selected probability functions with respect to forecast error distributions

The forecasting of sales in a company is one of the crucial challenges that must be faced. Nowadays, there is a large spectrum of methods that enable making reliable forecasts. However, sometimes the nature of time series excludes many well-known and widely used forecasting methods (e.g., econometric models). Therefore, the authors decided to forecast on the basis of a seasonally adjusted median of selected probability distributions. The obtained forecasts were verified by means of distributions of the Theil U² coefficient and unbiasedness coefficient.     

完善初级阶段基本经济制度的几个问题

坚持和完善初级阶段基本经济制度 ,必须坚持在公有制为主体与非公有制经济加快发展的结合中完善 ,特别是在消除束缚生产力发展的体制障碍中完善公有制为主体 ,并注意走出基本经济制度即公有制与非公有制简单相加等几个理论误区     

Impact of socio-economic infrastructure investments on income inequality in Iran

One of the main subjects, governments have been facing is fair distribution of income, and making effort to improve it. In this study, we evaluated the effects of economic (energy, water, ICT) and social (health, education) infrastructure expenses on income inequality in the Iranian provinces for the period of 2007–2016 by the panel corrected standard errors (PCSE) model. The results show that social and economic infrastructures improvements reduce income inequality. However, the magnitude of these effects varies. Investment on education, healthcare, communication technology, energy, and water infrastructures has the greatest impact on income inequality reduction, respectively. Therefore, in order to reduce inequality in deprived areas, combination and optimal allocation of economic and social infrastructures should be considered.     

Price Manipulation and Quasi‐Arbitrage

In an environment where trading volume affects security prices and where prices are uncertain when trades are submitted, quasi‐arbitrage is the availability of a series of trades that generate infinite expected profits with an infinite Sharpe ratio. We show that when the price impact of trades is permanent and time‐independent, only linear price‐impact functions rule out quasi‐arbitrage and thus support viable market prices. When trades have also a temporary price impact, only the permanent price impact must be linear while the temporary one can be of a more general form. We also extend the analysis to a time‐dependent framework.     

Operational Risk in Financial Services: A Review and New Research Opportunities

We present a framework to describe and analyze operational risk in financial services from an operations management perspective, focusing in particular on process design, process management, and human behavior aspects. The financial services industry differs from other service industries in ways that affect the nature of the operational risks it is subject to. In recent decades, many books and papers have focused on operational risk in financial services; however, this literature has focused mainly on the conceptual and statistical aspects of operational risk management and not on its operational aspects. Operational risk in financial services has not received much attention from the operations management community. The framework presented here is based on the premise that operational risk in financial services can reap significant benefits from research done in the theory and practice of operations management in manufacturing industries as well as in other services industries. The objective of this study is to propose particular challenges and questions raised in the practice of operational risk management that may stimulate future research in this particular area of operations management.     

Bootstrap methods for bias correction and confidence interval estimation for nonlinear quantile regression of longitudinal data

This paper examines the use of bootstrapping for bias correction and calculation of confidence intervals (CIs) for a weighted nonlinear quantile regression estimator adjusted to the case of longitudinal data. Different weights and types of CIs are used and compared by computer simulation using a logistic growth function and error terms following an AR(1) model. The results indicate that bias correction reduces the bias of a point estimator but fails for CI calculations. A bootstrap percentile method and a normal approximation method perform well for two weights when used without bias correction. Taking both coverage and lengths of CIs into consideration, a non-bias-corrected percentile method with an unweighted estimator performs best.     

Diagnostics for elliptical linear mixed models with first-order autoregressive errors

For longitudinal time series data, linear mixed models that contain both random effects across individuals and first-order autoregressive errors within individuals may be appropriate. Some statistical diagnostics based on the models under a proposed elliptical error structure are developed in this work. It is well known that the class of elliptical distributions offers a more flexible framework for modelling since it contains both light- and heavy-tailed distributions. Iterative procedures for the maximum-likelihood estimates of the model parameters are presented. Score tests for the presence of autocorrelation and the homogeneity of autocorrelation coefficients among individuals are constructed. The properties of test statistics are investigated through Monte Carlo simulations. The local influence method for the models is also given. The analysed results of a real data set illustrate the values of the models and diagnostic statistics.