Correlated random effects regression analysis for a log-normally distributed variable

In regression analysis, it is assumed that the response (or dependent variable) distribution is Normal, and errors are homoscedastic and uncorrelated. However, in practice, these assumptions are rarely satisfied by a real data set. To stabilize the heteroscedastic response variance, generally, log-transformation is suggested. Consequently, the response variable distribution approaches nearer to the Normal distribution. As a result, the model fit of the data is improved. Practically, a proper (seems to be suitable) transformation may not always stabilize the variance, and the response distribution may not reduce to Normal distribution. The present article assumes that the response distribution is log-normal with compound autocorrelated errors. Under these situations, estimation and testing of hypotheses regarding regression parameters have been derived. From a set of reduced data, we have derived the best linear unbiased estimators of all the regression coefficients, except the intercept which is often unimportant in practice. Unknown correlation parameters have been estimated. In this connection, we have derived a test rule for testing any set of linear hypotheses of the unknown regression coefficients. In addition, we have developed the confidence ellipsoids of a set of estimable functions of regression coefficients. For the fitted regression equation, an index of fit has been proposed. A simulated study illustrates the results derived in this report.     

Record-breaking data: a parametric comparison of the inverse-sampling and the random-sampling schemes

In many industrial quality control experiments and destructive stress testing, the only available data are successive minima (or maxima)i.e., record-breaking data. There are two sampling schemes used to collect record-breaking data: random sampling and inverse sampling. For random sampling, the total sample size is predetermined and the number of records is a random variable while in inverse-sampling the number of records to be observed is predetermined; thus the sample size is a random variable. The purpose of this papper is to determinevia simulations, which of the two schemes, if any, is more efficient. Since the two schemes are equivalent asymptotically, the simulations were carried out for small to moderate sized record-breaking samples. Simulated biases and mean square errors of the maximum likelihood estimators of the parameters using the two sampling schemes were compared. In general, it was found that if the estimators were well behaved, then there was no significant difference between the mean square errors of the estimates for the two schemes. However, for certain distributions described by both a shape and a scale parameter, random sampling led to estimators that were inconsistent. On the other hand, the estimated obtained from inverse sampling were always consistent. Moreover, for moderated sized record-breaking samples, the total sample size that needs to be observed is smaller for inverse sampling than for random sampling.     

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.     

Uniform Test for Predictive Regression With AR Errors

Testing predictability is of importance in economics and finance. Based on a predictive regression model with independent and identically distributed errors, some uniform tests have been proposed in the literature without distinguishing whether the predicting variable is stationary or nearly integrated. In this article, we extend the empirical likelihood methods of Zhu, Cai, and Peng with independent errors to the case of an AR error process. Again, the proposed new tests do not need to know whether the predicting variable is stationary or nearly integrated, and whether it has a finite variance or an infinite variance. A simulation study shows the new methodologies perform well in finite sample.     

曹学佺《蜀中名胜记》辨误

明人曹学佺编撰的《蜀中名胜记》保存了大量巴蜀文献,但由于编撰时间仓促,他对于一些材料的审核不够精细,故书中存在一些讹误之处。本文就其中四则比较典型的讹误辨正于此:卷八误将崔公辅诗句为李渭作,卷十五误以岑参嵩阳南溪别业为叙州南溪县别业,卷十七误以为白居易诗《涂山寺独游》中的"涂山寺"在巴县涂山,卷二十九误以李峤父李镇恶梓州事迹为苏味道父苏荣事迹。     

基于语料库的汉英中介语动名搭配失误分析

搭配问题是外语学习者用目标语进行言语输出时所遇到的主要障碍之一,因为母语和目标语都有一些自己独特的词语搭配规范。通过对比分析汉英中介语动名搭配模式和Collins COBUILD语料库中检索的相应动名搭配模式,发现汉英中介语动名搭配大多不符合目标语的搭配规范。造成动名搭配失误的主要原因是中国英语学习者将汉语的搭配知识和已经掌握的英语搭配知识迁移到中介语中。     

提高民族学生英语发音的方法

提高学生的英语语音发音对提高民族学生的英语整体水平起着不容质疑的基石作用。本文就如何开展该方面的教学、培养学生的学习兴趣及学习技巧提出一些浅显的建议。     

Generalized estimator of population mean by using conventional and non-conventional measures in the presence of measurement errors

In survey research, it is assumed that reported response by the individual is correct. However, given the issues of prestige bias, self-respect, respondent's reported data often produces estimated values which are highly deviated from the true values. This causes measurement error (ME) to be present in the sample estimates. In this article, the estimation of population mean in the presence of measurement error using information on a single auxiliary variable is studied. A generalized estimator of population mean is proposed. The class of estimators is obtained by using some conventional and non-conventional measures. Simulation and numerical study is also conducted to assess the performance of estimators in the presence and absence of measurement error.     

对语料库中高年级学生写作词汇搭配错误的分析

分析和探讨了《中国学习者英语语料库》中标注的动词+名词和形容词+名词搭配错误及其原因。研究发现：对于动词+ 名词搭配错误,由于搭配的意义主要是由名词决定的,忽视或违背搭配的固定性并不影响对整个搭配意思的理解,但缺乏地道 的搭配会影响写作的准确性。对于形容词+名词搭配错误,L1干扰是主要的原因。词语搭配运用是否得当,会影响到写作的 准确性和地道性。帮助学习者掌握搭配类型,利用语料库学习和探索搭配,能有效地解决学习者写作中词不达意的问题,提高 写作表达的准确性。