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.     

The Use of Meteorological Data in the Prediction of Tropical Cyclones

Recent studies have shown that the ability to forecast tropical cyclone motion and intensity has not improved significantly over the past decade, in spite of apparent advances in the statistical and dynamical modeling of the motion- and intensity-change processes. This static situation has focused attention on the meteorological data and analysis techniques that provide input to these models. The quality and quantity of data have apparently not kept pace with the modeling efforts; in fact, increased sophistication in modeling alone may be counterproductive. Steps are being taken in the meteorological community to correct this problem. This study examines the entire data problem, discusses the remedial measures being taken, and points out some of the pitfalls in the statistical use of meteorological data.     

The potential for bias in reporting of industry-sponsored clinical trials

Concerns about potentially misleading reporting of pharmaceutical industry research have surfaced many times. The potential for duality (and thereby conflict) of interest is only too clear when you consider the sums of money required for the discovery, development and commercialization of new medicines. As the ability of major, mid-size and small pharmaceutical companies to innovate has waned, as evidenced by the seemingly relentless decline in the numbers of new medicines approved by Food and Drug Administration and European Medicines Agency year-on-year, not only has the cost per new approved medicine risen: so too has the public and media concern about the extent to which the pharmaceutical industry is open and honest about the efficacy, safety and quality of the drugs we manufacture and sell. In 2005 an Editorial in Journal of the American Medical Association made clear that, so great was their concern about misleading reporting of industry-sponsored studies, henceforth no article would be published that was not also guaranteed by independent statistical analysis. We examine the precursors to this Editorial, as well as its immediate and lasting effects for statisticians, for the manner in which statistical analysis is carried out, and for the industry more generally.     

Estimation and Prediction in the Presence of Spatial Confounding for Spatial Linear Models

In studies that produce data with spatial structure, it is common that covariates of interest vary spatially in addition to the error. Because of this, the error and covariate are often correlated. When this occurs, it is difficult to distinguish the covariate effect from residual spatial variation. In an i.i.d. normal error setting, it is well known that this type of correlation produces biased coefficient estimates, but predictions remain unbiased. In a spatial setting, recent studies have shown that coefficient estimates remain biased, but spatial prediction has not been addressed. The purpose of this paper is to provide a more detailed study of coefficient estimation from spatial models when covariate and error are correlated and then begin a formal study regarding spatial prediction. This is carried out by investigating properties of the generalized least squares estimator and the best linear unbiased predictor when a spatial random effect and a covariate are jointly modelled. Under this setup, we demonstrate that the mean squared prediction error is possibly reduced when covariate and error are correlated.     

Estimating the duration of dynamic effects with temporally aggregated observations

This paper discusses how an Ordinary Least Squares (OLS) estimator can be used to obtain reasonably accurate estimates of the duration of dynamic effects in a Koyck model framework without knowledge of the true level of temporal aggregation of the data. With proper changes in the analytic derivation, the approach can be extended to other dynamic models.     

The covariance structure of sequential forecasts obtained by regression analysis

A method is presented for evaluating the covariance matrix of a set of sequential forecasts obtained by regression analysis. The matrix can be used to derive the relation between the variance of the forecasts on the one hand, and the lead times between the forecasting time and the time at which the forecasted variables are realized, on the other hand. The determination of this relation is important whenever the optimal frequency of forecasting must be determined.     

Publication Decisions Revisited: The Effect of the Outcome of Statistical Tests on the Decision to Publish and Vice Versa

This article presents evidence that published results of scientific investigations are not a representative sample of results of all scientific studies. Research studies from 11 major journals demonstrate the existence of biases that favor studies that observe effects that, on statistical evaluation, have a low probability of erroneously rejecting the so-called null hypothesis (H ₀). This practice makes the probability of erroneously rejecting H ₀ different for the reader than for the investigator. It introduces two biases in the interpretation of the scientific literature: one due to multiple repetition of studies with false hypothesis, and one due to failure to publish smaller and less significant outcomes of tests of a true hypotheses. These practices distort the results of literature surveys and of meta-analyses. These results also indicate that practice leading to publication bias have not changed over a period of 30 years.     

Review of random and group-screening designs

Experiments with real systems, but especially with simulated systems, may involve hundreds of factors. However, only a few factors are really important. Detecting these Important factors requires special designs such as random and group-screening de-signs. Random designs are simple, but they yield biased esti-mators. Group-screening is based on aggregation. The assumptions of group-screening are discussed in detail, and seem not very restrictive. References to applications of both design types are given.     

The Revenue Effects of the Kennedy and Reagan Tax Cuts: Some Time Series Estimates

Univariate time series models are estimated for sample periods ending with the enactment of major tax reductions in 1964 and 1981. These models are used to forecast government revenue for the period following the tax cut, and the pattern of forecast errors is examined. Unforecast revenue is negative and large relative to its standard error following the 1981 tax cuts but is close to zero following the 1964 cuts. This disparity occurs because national output behaved differently in the two cases, suggesting that short-run movements in output are dominated by factors other than tax rate changes.     

抽样调查理论与方法的哲学诠释

文章从哲学视角诠释了抽样调查理论和方法的科学性和先进性,并指出抽样调查的理论和方法不但体现了辩证唯物主义的认识论,还体现了唯物辩证法的思想。对于人们正确地认识抽样调查,准确地应用抽样调查,积极地发展抽样调查都是大有裨益的。     

The Probability Distribution of Future Demand

The bootstrapping technique of Efron (1979) is used to estimate the probability distribution of future electricity demand for Hydro Quebec. The application follows the regression approach of Freedman and Peters (1984a,b) but also allows for serially correlated disturbances and uncertainty in the independent variable forecasts.     

Prediction Error of Small Area Predictors Shrinking Both Means and Variances

The article considers a new approach for small area estimation based on a joint modelling of mean and variances. Model parameters are estimated via expectation–maximization algorithm. The conditional mean squared error is used to evaluate the prediction error. Analytical expressions are obtained for the conditional mean squared error and its estimator. Our approximations are second‐order correct, an unwritten standardization in the small area literature. Simulation studies indicate that the proposed method outperforms the existing methods in terms of prediction errors and their estimated values.     

The biases associated with maximum likelihood methods of estimation of the multivariate logistic risk function

The posterior probability of an object belonging to one of two populations can be estimated using multivariate logistic regression. The bias associated with this procedure is derived In the context of normal populations with different mean vectors and a common covariance matrix and is compared with the bias of the classical method based on this normality assumption, -It Is found that the bias of the more robust procedure of logistic regression is of a lower order than that of the normality based method.     

The size of under-reporting bias in recorded transnational terrorist activity

Summary.  We address an apparent gap in the applied terrorism literature providing an estimate for the size of under-reporting in transnational terrorist activity. The method that is suggested is computationally straightforward and takes into account the stochastic interactions between terrorism, polity and press freedom in a manner that utilizes their sample properties. The outcome from the application of this metric is that underreporting bias is indeed present and quite sizable.     

中国收入差距扩大诱因的实证研究——基于劳动价值低估和要素配置偏向视角

居民收入差距不断扩大是中国当前面临的重要社会经济问题,城乡二元结构、地区差距、行业垄断和非正常收入等因素,是导致中国基尼系数居高不下、收入差距不断扩大化的原因。基于主成分分析和回归分析等研究方法,对收入差距扩大化诱因的重要程度进行排序;通过分析得出生产要素中劳动价值被严重低估是导致收入差距扩大重要诱因的结论;提出缩小或控制中国收入差距的一主两翼组合的经济政策建议。     

The Integration of Artificial Neural Networks and Text Mining to Forecast Gold Futures Prices

Although a previous study found that neural network forecasts were more accurate than time series models for predicting Latin American stock indexes, the forecasting accuracy of neural network for predicting gold futures prices has never been discussed. Therefore, the first objective of this study is to compare the forecasting accuracy of a neural network model with that of ARIMA models. Furthermore, the fluctuations in gold futures are not only influenced by the quantitative variables, but also by many nonquantifiable factors, such as wars, international relations, and terrorist attacks. The second objective of this study is therefore to propose the integration of text mining and an artificial neural network to forecast gold futures prices. The historical gold futures prices from 1999 to 2008 were used as training data and testing data, and the prices of 2009 were used to examine the effectiveness of the proposed model. The results of empirical analysis showed that an artificial neural network forecasted gold futures prices better than ARIMA models did. In addition, text mining provided a reasonable explanation of the trend in gold futures prices.     

The maximum likelihood estimators of the parameters of the gamma distribution are always positively biased

Several authors have conjectured, on the basis of their numerical work, that the maximum likelihood estimators of the shape and scale parameters of the Gamma distribution are positively biased. It is proved that their conjecture is always true.     

Perception,Preference, and Patriotism: An Exploratory Analysis of the 1980 Winter Olympics

This article analyzes scores given by judges of figure skating at the 1980 Winter Olympics. Judges' scores are found to be highly correlated, with little evidence of scoring along political lines. However, an analysis of variance shows a small but consistent “patriotic” bias; judges tend to give higher scores to contestants from their own country. The influence of such effects on final placings is estimated.     

The Relative Efficiency of Prediction Intervals

There is extensive literature on the measurement of the relative efficiencies of estimators, hypothesis tests, and confidence intervals. However, the important topic of the measurement of the relative efficiencies of prediction intervals has hitherto not been properly explored. Following Hodges and Lehmann (1970 Hodges , J. L. , Lehmann , E. L. ( 1970 ). Deficiency . Ann. Mathemat. Statist. 41 : 783 – 801 .[Crossref] , [Google Scholar]), we require that a measure of the relative efficiency of two prediction intervals be stable in large samples. We examine the consequences of this requirement when these prediction intervals have coverage probability (a) equal to nominal and (b) asymptotically equal to nominal. Illustrations for independent and identically distributed data and time series data are provided.