{"title":"A degrees-of-freedom approximation for a t-statistic with heterogeneous variance","authors":"S. Lipsitz, J. Ibrahim, M. Parzen","doi":"10.1111/1467-9884.00207","DOIUrl":"https://doi.org/10.1111/1467-9884.00207","url":null,"abstract":"Summary. Ordinary least squares is a commonly used method for obtaining parameter estimates for a linear model. When the variance is heterogeneous, the ordinary least squares estimate is unbiased, but the ordinary least squares variance estimate may be biased. Various jackknife estimators of variance have been shown to be consistent for the asymptotic variance of the ordinary least squares estimator of the parameters. In small samples, simulations have shown that the coverage probabilities of confidence intervals using the jackknife variance estimators are often low. We propose a finite sample degrees-of-freedom approximation to use with the t-distribution, which, in simulations, appears to raise the coverage probabilities closer to the nominal level. We also discuss an ‘estimated generalized least squares estimator’, in which we model and estimate the variance, and use the estimated variance in a weighted regression. Even when the variance is modelled correctly, the coverage can be low in small samples, and, in terms of the coverage probability, one appears better off using ordinary least squares with a robust variance and our degrees-of-freedom approximation. An example with real data is given to demonstrate how potentially misleading conclusions may arise when the degrees-of-freedom approximation is not used.","PeriodicalId":83973,"journal":{"name":"The Statistician : journal of the Institute of Statisticians","volume":"25 1","pages":"495-506"},"PeriodicalIF":0.0,"publicationDate":"1999-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77151884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Fractional Factorial Design for Bench‐mark Testing of a Bayesian Method for Multilocation Audits","authors":"V. Barnett, J. Haworth","doi":"10.1111/1467-9884.00159","DOIUrl":"https://doi.org/10.1111/1467-9884.00159","url":null,"abstract":"Modern auditing methods require statistical input which can be quite sophisticated. A Bayesian approach to multilocation audit is described. This complex methodology needed computer software which operated sufficiently speedily for practical application. A fractional replicate of a factorial designed experiment was conducted to determine the effects on operating speeds of a number of critical input factors.","PeriodicalId":83973,"journal":{"name":"The Statistician : journal of the Institute of Statisticians","volume":"65 1","pages":"617-628"},"PeriodicalIF":0.0,"publicationDate":"1998-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85279854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Conflict between pretest estimators induced by three large sample tests under a regression model with student t-error","authors":"Md. Baki Billah, A. Saleh","doi":"10.1111/1467-9884.00157","DOIUrl":"https://doi.org/10.1111/1467-9884.00157","url":null,"abstract":"We consider the preliminary test approach to the estimation of regression parameters, in a multiple-regression model with non-normal error. Here, we consider the multivariate t-distribution as the error distribution. The preliminary test estimators (PTEs) based on the Wald, likelihood ratio (LR) and Lagrangian multiplier (LM) tests are given. Their bias, mean-square error matrix M and weighted mean-square error (WMSE) functions are derived and compared. We show that there is conflict between the three estimators with respect to their bias, M and WMSE because of certain inequalities that exist inherently among the test statistics and because of approximating them by the same asymptotic distribution. In the neighbourhood of the null hypothesis the PTE based on the LM test has the smallest WMSE followed by the LR-based estimator, and the estimator based on the Wald test is the worst. However, the PTE based on the Wald test performs the best followed by the LR-based estimator when the parameter moves away from the subspace of the restriction and the LM-based estimator is the worst. It has been shown that in the choice of the smallest level of significance to yield the best estimator in the sense of the highest minimum guaranteed efficiency the Wald-based PTE dominates the other two estimators. Further, the optimum choice of the level of significance becomes the traditional choice by using the Wald test.","PeriodicalId":83973,"journal":{"name":"The Statistician : journal of the Institute of Statisticians","volume":"122 1","pages":"593-606"},"PeriodicalIF":0.0,"publicationDate":"1998-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79466586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Demonstrating the Durbin-Watson statistic","authors":"R. Champion, C. Lenard, T. Mills","doi":"10.1111/1467-9884.00161","DOIUrl":"https://doi.org/10.1111/1467-9884.00161","url":null,"abstract":"The paper describes one way to demonstrate the Durbin-Watson statistic to a large class of students.","PeriodicalId":83973,"journal":{"name":"The Statistician : journal of the Institute of Statisticians","volume":"44 1","pages":"643-644"},"PeriodicalIF":0.0,"publicationDate":"1998-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85284055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Using Maximum Entropy to Double One's Expected Winnings in the UK National Lottery","authors":"S. J. Cox, G. Daniell, D. Nicole","doi":"10.1111/1467-9884.00160","DOIUrl":"https://doi.org/10.1111/1467-9884.00160","url":null,"abstract":"We have used the Maximum Entropy method to estimate the probability of each of the 14 million tickets being chosen by players in the UK National Lottery. As data, we used the numbers of winners in the 3, 4, and 5-match categories and the total number of tickets sold in each of the first 113 draws. We have computed the marginal distributions for players choosing single numbers and pairs of numbers. A striking conclusion is that players preferentially pick numbers towards the centre of the ticket. By choosing unpopular combinations of numbers, one?s expected winnings can be doubled.","PeriodicalId":83973,"journal":{"name":"The Statistician : journal of the Institute of Statisticians","volume":"2218 1","pages":"629-641"},"PeriodicalIF":0.0,"publicationDate":"1998-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86560386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Can Takeover Targets be Identified by Statistical Techniques?: Some UK Evidence","authors":"P. Barnes","doi":"10.1111/1467-9884.00156","DOIUrl":"https://doi.org/10.1111/1467-9884.00156","url":null,"abstract":"Summary. This paper examines the proposition that takeover targets may be forecasted by using similar methods as bankruptcy prediction which has been very successful in both the USA and the UK. The study shows that they cannot be predicted simply by using published accounting data as inputs into these models. It also examines the possibility of combining such data with anticipatory share price changes, which are also publicly available information, but this did not improve the predictive accuracy. These results support the efficient capital market hypothesis in the semi-strong form-that the market may not be beaten by using publicly available information alone. The fact that there are anticipatory price changes suggests that the market may not be efficient in the strong form.","PeriodicalId":83973,"journal":{"name":"The Statistician : journal of the Institute of Statisticians","volume":"11 1","pages":"573-591"},"PeriodicalIF":0.0,"publicationDate":"1998-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84597848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Non‐linear curve fitting and the true method of least squares","authors":"R. Farebrother","doi":"10.1111/1467-9884.00119","DOIUrl":"https://doi.org/10.1111/1467-9884.00119","url":null,"abstract":"This paper is concerned with the fitting of non-linear relationships such as the logistic and Gompertz functions. Although Gauss had proposed the now standard Gauss-Newton procedure for this purpose in 1809, and it was strongly championed by Schultz in 1930, this procedure did not come into common use until modern computing equipment was introduced in the 1960s. In its stead a variety of virtually arbitrary procedures were employed. These arbitrary procedures are still used when the practitioner requires preliminary estimates of the parameters of a given non-linear function.","PeriodicalId":83973,"journal":{"name":"The Statistician : journal of the Institute of Statisticians","volume":"30 1","pages":"137-147"},"PeriodicalIF":0.0,"publicationDate":"1998-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82763780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"On type I error rates in the analysis of unreplicated 23‐factorials","authors":"T. Loughin","doi":"10.1111/1467-9884.00124","DOIUrl":"https://doi.org/10.1111/1467-9884.00124","url":null,"abstract":"Unreplicated factorial and fractional factorials at two levels of each factor are common designs in many research endeavours. However, analysis methods which work well for large numbers of factors may be difficult to apply and interpret when the number of factors is as small as 3. Various popular methods of analysis of 2 3 -factorials are reviewed with special attention to the assumptions made, both explicitly and implicitly, in the course of the analysis, as well as the control (or lack thereof) that each maintains on the type I error rate. A result is presented based on randomization theory which indicates that a type I error rate of as much as 0.20 may be implicit in the analysis of 2 3 -factorials when knowledge regarding the distribution and variance of random errors is lacking.","PeriodicalId":83973,"journal":{"name":"The Statistician : journal of the Institute of Statisticians","volume":"44 1","pages":"207-210"},"PeriodicalIF":0.0,"publicationDate":"1998-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80069513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Eliciting expert beliefs in substantial practical applications","authors":"A. O’Hagan","doi":"10.1111/1467-9884.00114","DOIUrl":"https://doi.org/10.1111/1467-9884.00114","url":null,"abstract":"The practical elicitation of expert beliefs is considered through two contrasting examples. The first example concerns elicitation of engineers' prior beliefs about various quantities relating to the future capital investment need of a water company. Prior beliefs needed to be elicited about very many quantities, but only in the form of prior means, variances and covariances. A computerized procedure was required that could be routinely used by engineers, unsupervised. The second example is a single, application-specific elicitation of the beliefs of hydrogeologists about properties of certain rocks. A full probability specification was ideally to be obtained from a two-day intensive, supervised, elicitation with several experts together. The two different approaches used for these problems are described and contrasted, but a common principle of trying to identify and elicit separately the various sources of expert uncertainty is identified.","PeriodicalId":83973,"journal":{"name":"The Statistician : journal of the Institute of Statisticians","volume":"56 1","pages":"21-35"},"PeriodicalIF":0.0,"publicationDate":"1998-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90397358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Bartlett‐type modified test for moving seasonality with applications","authors":"B. Sutradhar, E. Dagum","doi":"10.1111/1467-9884.00123","DOIUrl":"https://doi.org/10.1111/1467-9884.00123","url":null,"abstract":"Socioeconomic time series are often presented in a seasonally adjusted form so that the underlying short-term trend can be more easily analysed, and current socioeconomic conditions can be easily assessed. As a first step towards seasonally adjusting a series, it is essential to test for a changing (moving) seasonal pattem against a stable seasonal pattern over time. To test for moving seasonality, Sutradhar and Dagum have applied a modified Bartlett test to trend-free data consisting of seasonality only. In general, however, such trend-free or detrended data appear to consist of seasonality plus irregular components. This makes the estimation of the parameters more complicated than was the case for Sutradhar and Dagum. We discuss this estimation problem in the present paper in connection with three Canadian export series. As Sutradhar and Dagum did, we then develop a modified Bartlett test under the new set-up and apply the test for testing for moving seasonality in the three Canadian export series.","PeriodicalId":83973,"journal":{"name":"The Statistician : journal of the Institute of Statisticians","volume":"33 1","pages":"191-206"},"PeriodicalIF":0.0,"publicationDate":"1998-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83928396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}