Generalized piecewise linear histograms

We extend the concept of piecewise linear histogram introduced recently by Beirlant, Berlinet and Györfi. The disadvantage of that histogram is that in many models it takes on negative values with probability close to 1. We show that for a wide set of models, the extended class of estimates contains a bona fide density with probability tending to 1 as the sample size n increases to infinity. The mean integrated absolute error in the extended class of estimators decreases with the same rate n^–2/5 as in the original narrower class.     

From T-Bills to Stocks: Seasonal Anomalies Revisited

This paper employed eleven data series which consist of stocks, bonds, bills, equity premiums, term premiums, and various default premiums to investigate whether January seasonality reported in existing literature is robust across different states of the economy as this has important trading implications. For the periods 1926–1990, small stocks, small stock premiums, low grade bonds, and default premiums (spread between high grade, low grade and government bonds) reveal January seasonality and that the seasonality is robust across different states of the economy except for low grade bond returns and default premiums. January seasonality for low grade bond returns and low grade bond default premiums are primarily driven by results found during periods of economic expansion. Overall, January seasonality is more evident during the economic expansion periods although the magnitude of default premiums is larger during periods of economic contraction. Furthermore, prior findings of strong summer equity returns are primarily driven by the results found during the periods of economic contraction. It is also found that equity returns are generally higher during periods of economic expansion.     

On estimating the mean of the selected normal population under the LINEX loss function

Following Parsian and Farsipour (1999), we consider the problem of estimating the mean of the selected normal population, from two normal populations with unknown means and common known variance, under the LINEX loss function. Some admissibility results for a subclass of equivariant estimators are derived and a sufficient condition for the inadmissibility of an arbitrary equivariant estimator is provided. As a consequence, several of the estimators proposed by Parsian and Farsipour (1999) are shown to be inadmissible and better estimators are obtained. Received January 2001/Revised May 2002     

Pseudo empirical likelihood method in the presence of missing data

In this paper, we propose an estimator for the population mean when some observations on the study and auxiliary variables are missing from the sample. The proposed estimator is valid for any unequal probability sampling design, and is based upon the pseudo empirical likelihood method. The proposed estimator is compared with other estimators in a simulation study.     

On estimating the reliability after sequentially estimating the mean: The exponential case

Sequential estimation problems for the mean parameter of an exponential distribution has received much attention over the years. Purely sequential and accelerated sequential estimators and their asymptotic second-order characteristics have been laid out in the existing literature, both for minimum risk point as well as bounded length confidence interval estimation of the mean parameter. Having obtained a data set from such sequentially designed experiments, the paper investigates estimation problems for the associatedreliability function. Second-order approximations are provided for the bias and mean squared error of the proposed estimator of the reliability function, first under a general setup. An ad hoc bias-corrected version is also introduced. Then, the proposed estimator is investigated further under some specific sequential sampling strategies, already available in the literature. In the end, simulation results are presented for comparing the proposed estimators of the reliability function for moderate sample sizes and various sequential sampling strategies.     

Restricted maximum likelihood estimation under Eisenhart model Ill

For a balanced two-way mixed model, the maximum likelihood (ML) and restricted ML (REML) estimators of the variance components were obtained and compared under the non-negativity requirements of the variance components by L ee and K apadia (1984). In this note, for a mixed (random blocks) incomplete block model, explicit forms for the REML estimators of variance components are obtained. They are always non-negative and have smaller mean squared error (MSE) than the analysis of variance (AOV) estimators. The asymptotic sampling variances of the maximum likelihood (ML) estimators and the REML estimators are compared and the balanced incomplete block design (BIBD) is considered as a special case. The ML estimators are shown to have smaller asymptotic variances than the REML estimators, but a numerical result in the randomized complete block design (RCBD) demonstrated that the performances of the REML and ML estimators are not much different in the MSE sense.     

外生冲击与房地产真实价格波动--对1998～2003年中国31省(市、区)的实证研究

文章首先根据经济学模型界定了两个重要指标:自相关系数与收敛系数.认为不同的外生冲击对这两个系数有不同的影响,而它们决定了房地产真实价格波动形态的差异.在此基础上,文章利用1998～2003年中国31个省(市、区)的面板数据对中国房地产市场进行了实证研究,其结论是,在真实人均可支配收入和真实建筑成本较高、真实税后住宅抵押贷款利率较低的地区有较大的自相关系数和较小的收敛系数,从而房地产真实价格具有更大的波动性.为使房地产真实价格在均衡价格附近平稳运行,降低开发成本和提高消费者购买成本能收到较好的效果.     

Conditional mean functions of non-linear models of US output

We compare a number of models of post War US output growth in terms of the degree and pattern of non-linearity they impart to the conditional mean, where we condition on either the previous period's growth rate, or the previous two periods' growth rates. The conditional means are estimated non-parametrically using a nearest-neighbour technique on data simulated from the models. In this way, we condense the complex, dynamic, responses that may be present in to graphical displays of the implied conditional mean. First version received: Feb. 1999/Final version received: June 2001     

Are there major differences in the attitudes and service quality of standard and seasonal employees? An empirical examination and implications for practice

Despite the increased use of seasonal employees by organizations, few studies have been completed on the attitudes and service quality of seasonal office workers. Using Lautsch's classification model, we analyzed the organizational context in which the standard and seasonal workers in this study were employed. Hypotheses were developed based upon the organizational analysis and social exchange theory. Results from archival data obtained from a web‐based organizational survey of 205 clerical and professional workers indicated contrary to expectations, standard and seasonal employees did not significantly differ in terms of perceptions of overall job conditions, perceived organizational support (POS), or job engagement. However, seasonal employees did report significantly fewer opportunities to work on challenging tasks, less comfortable physical working conditions, and less job security than the standard workers. As predicted, standard employees reported significantly higher levels of service quality performance than seasonal employees. Additionally, job engagement mediated the relationship between POS and service quality for both the standard and seasonal employees. Implications for managing seasonal employees are discussed.     

On the importance of the long-term seasonal component in day-ahead electricity price forecasting with NARX neural networks

Daily and weekly seasonalities are always taken into account in day-ahead electricity price forecasting, but the long-term seasonal component has long been believed to add unnecessary complexity, and hence, most studies have ignored it. The recent introduction of the Seasonal Component AutoRegressive (SCAR) modeling framework has changed this viewpoint. However, this framework is based on linear models estimated using ordinary least squares. This paper shows that considering non-linear autoregressive (NARX) neural network-type models with the same inputs as the corresponding SCAR-type models can lead to yet better performances. While individual Seasonal Component Artificial Neural Network (SCANN) models are generally worse than the corresponding SCAR-type structures, we provide empirical evidence that committee machines of SCANN networks can outperform the latter significantly.