Ethnic ghettos in Britain: a fact or a myth?

A common claim is that Britain's minorities live isolated parallel lives in inner cities that have become ethnic ghettos. Does the evidence confirm such dangerous lack of integration? Ludi Simpson and Nissa Finney find a basic misunderstanding about population change—a misunderstanding that makes changing the ethnic mix of neighbourhoods an unrealistic policy goal.     

Which Measure Should be Used for Testing in a Paired Design: Simple Difference,Percent Change,or Symmetrized Percent Change?

We aimed to determine the most proper change measure among simple difference, percent, or symmetrized percent changes in simple paired designs. For this purpose, we devised a computer simulation program. Since distributions of percent and symmetrized percent change values are skewed and bimodal, paired t-test did not give good results according to Type I error and the test power. To be to able use percent change or symmetrized percent change as change measure, either the distribution of test statistics should be transformed to a known theoretical distribution by transformation methods or a new test statistic for these values should be developed.     

Nonparametric Estimation of Sojourn Time Distributions for Truncated Serial Event Data—a Weight-adjusted Approach

In follow-up studies, survival data often include subjects who have had a certain event at recruitment and may potentially experience a series of subsequent events during the follow-up period. This kind of survival data collected under a cross-sectional sampling criterion is called truncated serial event data. The outcome variables of interest in this paper are serial sojourn times between successive events. To analyze the sojourn times in truncated serial event data, we need to confront two potential sampling biases arising simultaneously from a sampling criterion and induced informative censoring. In this study, nonparametric estimation of the joint probability function of serial sojourn times is developed by using inverse probabilities of the truncation and censoring times as weight functions to accommodate these two sampling biases under various situations of truncation and censoring. Relevant statistical properties of the proposed estimators are also discussed. Simulation studies and two real data are presented to illustrate the proposed methods.     

How Long Is a Spell of Unemployment? Illusions and Biases in the Use of CPS Data

Most data used to study the durations of unemployment spells come from the Current Population Survey (CPS), which is a point-in-time survey and gives an incomplete picture of the underlying duration distribution. We introduce a new sample of completed unemployment spells obtained from panel data and apply CPS sampling and reporting techniques to replicate the type of data used by other researchers. Predicted duration distributions derived from this CPS-like data are then compared to the actual distribution. We conclude that the best inferences that can be made about unemployment durations by using CPS-like data are seriously biased.     

SAVE: Robust or not?

Abstract

Sliced average variance estimation (SAVE) is one of the best methods for estimating central dimension-reduction subspace in semi parametric regression models when covariates are normal. In recent days SAVE is being used to analyze DNA microarray data especially in tumor classification but most important drawback is normality of covariates. In this article, the asymptotic behavior of estimates of CDR space under varying slice size is studied through simulation studies when covariates are non normal but follows linearity condition as well as when covariates slightly perturbed from normal distribution and we observed that serious error may occur under violation normality assumption.     

Revisiting the Berger location model: Fallacious confidence interval or a rigged example?

Since the 1960s the Bayesian case against frequentist inference has been partly built on several “classic” examples which are devised to show how frequentist inference procedures can give rise to fallacious results; see Berger and Wolpert (1988) [2]. The primary aim of this note is to revisit one of these examples, the Berger location model, that is supposed to demonstrate the fallaciousness of frequentist Confidence Interval (CI) estimation. A closer look at the example, however, reveals that the fallacious results stem primarily from the problematic nature of the example itself, since it is based on a non-regular probability model that enables one to (indirectly) assign probabilities to the unknown parameter. Moreover, the proposed confidence set is not a proper frequentist CI in the sense that it is not defined in terms of legitimate error probabilities.     

Is There a Jump in the Transition?

This article develops a statistical test for the presence of a jump in an otherwise smooth transition process. In this testing, the null model is a threshold regression and the alternative model is a smooth transition model. We propose a quasi-Gaussian likelihood ratio statistic and provide its asymptotic distribution, which is defined as the maximum of a two parameter Gaussian process with a nonzero bias term. Asymptotic critical values can be tabulated and depend on the transition function employed. A simulation method to compute empirical critical values is also developed. Finite-sample performance of the test is assessed via Monte Carlo simulations. The test is applied to investigate the dynamics of racial segregation within cities across the United States.     

Formal Serials Education: A Problem We Can't Solve or a Solution We Can Live With?

Abstract

Serialists have long believed their field is underrepresented in the library and information science (LIS) curriculum. A recent review of Web sites of ALA-accredited LIS programs shows no significant change in the percentage of formal serials courses in those programs. The problem of adequate formal serials education is examined in the broader context of LIS education as a whole. Increasing traditional, formal serials education is an impractical goal. Instead, we should develop continuing education opportunities, and work to dispel some of the mystique of serials.     

Biomarkers in drug development: friend or foe? A personal reflection gained working within oncology

Hopes and expectations for the use and utility of new, emerging biomarkers in drug development have probably never been higher, especially in oncology. Biomarkers are exalted as vital patient selection tools in an effort to target those most likely to benefit from a new drug, and so to reduce development costs, lessen risk and expedite developments times. It is further hoped that biomarkers can be used as surrogate endpoints for clinical outcomes, to demonstrate effectiveness and, ultimately, to support drug approval. However, I perceive that all is not straightforward, and, particularly in terms of the promise of accelerated drug development, biomarker strategies may not in all cases deliver the advances and advantages hoped for.     

Analyzing partially paired data: when can the unpaired portion(s) be safely ignored?

Partially paired data, either with incompleteness in one or both arms, are common in practice. For testing equality of means of two arms, practitioners often use only the portion of data with complete pairs and perform paired tests. Although such tests (referred as ‘naive paired tests’) are legitimate, their powers might be low as only partial data are utilized. The recently proposed ‘P-value pooling methods’, based on combining P-values from two tests, use all data, have reasonable type-I error control and good power property. While it is generally believed that ‘P-value pooling methods’ are superior to ‘naive paired tests’ in terms of power as the former use more data than the latter, no detailed power comparison has been done. This paper aims to compare powers of ‘naive paired tests’ and ‘P-value pooling methods’ analytically and our findings are counterintuitive, i.e. the ‘P-value pooling methods’ do not always outperform the naive paired tests in terms of power. Based on these results, we present guidance on how to select the best test for testing equality of means with partially paired data.     

ML estimation for factor analysis: EM or non-EM?

To obtain maximum likelihood (ML) estimation in factor analysis (FA), we propose in this paper a novel and fast conditional maximization (CM) algorithm, which has quadratic and monotone convergence, consisting of a sequence of CM log-likelihood (CML) steps. The main contribution of this algorithm is that the closed form expression for the parameter to be updated in each step can be obtained explicitly, without resorting to any numerical optimization methods. In addition, a new ECME algorithm similar to Liu’s (Biometrika 81, 633–648, 1994) one is obtained as a by-product, which turns out to be very close to the simple iteration algorithm proposed by Lawley (Proc. R. Soc. Edinb. 60, 64–82, 1940) but our algorithm is guaranteed to increase log-likelihood at every iteration and hence to converge. Both algorithms inherit the simplicity and stability of EM but their convergence behaviors are much different as revealed in our extensive simulations: (1) In most situations, ECME and EM perform similarly; (2) CM outperforms EM and ECME substantially in all situations, no matter assessed by the CPU time or the number of iterations. Especially for the case close to the well known Heywood case, it accelerates EM by factors of around 100 or more. Also, CM is much more insensitive to the choice of starting values than EM and ECME.     

Publish or Perish: How Are Research and Reputation Related?

A study of twenty-seven fields in 350 highly ranked universities examines the relationship between reputation and rank. We find that many metrics associated with research prowess significantly correlate to university reputation. However, the next logical step– looking at the relationship that links different academic fields with the reputation of the university–did not always offer the expected results. The phrase “publish or perish” clearly has very different meanings in different fields.     

When and Why are Principal Component Scores a Good Tool for Visualizing High‐dimensional Data?

Principal component analysis is a popular dimension reduction technique often used to visualize high‐dimensional data structures. In genomics, this can involve millions of variables, but only tens to hundreds of observations. Theoretically, such extreme high dimensionality will cause biased or inconsistent eigenvector estimates, but in practice, the principal component scores are used for visualization with great success. In this paper, we explore when and why the classical principal component scores can be used to visualize structures in high‐dimensional data, even when there are few observations compared with the number of variables. Our argument is twofold: First, we argue that eigenvectors related to pervasive signals will have eigenvalues scaling linearly with the number of variables. Second, we prove that for linearly increasing eigenvalues, the sample component scores will be scaled and rotated versions of the population scores, asymptotically. Thus, the visual information of the sample scores will be unchanged, even though the sample eigenvectors are biased. In the case of pervasive signals, the principal component scores can be used to visualize the population structures, even in extreme high‐dimensional situations.     

Gender and income inequality in the UK 1968–90: the feminization of earnings or of poverty?

This paper uses data from the Family Expenditure Survey for five selected years between 1968 and 1990 to examine trends in the income distribution in the UK, highlighting the role of women's labour force participation and earnings. The increased labour force participation of married women (especially mothers of young children in the 1980s) made a greater contribution to the decline of the 'traditional' male breadwinner family than the increased number of lone parents. The lower half of the distribution of weekly earnings became increasingly dominated by women. Though women's weekly earnings remained low relative to men's, the increase in their participation meant that, over the period, an increased share of family income came from women's labour market income: in 1990 nearly a quarter of the income of families with children came from women's earnings. Women's earnings were an important factor in keeping families out of poverty. There was no trend towards increasing feminization of poverty over the sample period. Adult women were somewhat more likely to be poor than adult men were, but female-headed families were very much more likely to be in poverty, and much more dependent on state benefits, than male-headed families were. Women's increased role in the labour market affected those in male-headed families more than those in female-headed families. Alongside a broad tendency for women's earnings to reduce poverty and inequality, there is evidence that the female population has become more economically polarized.     

A Model-based Approach to Measure School Achievements in Latent Groups of Students: A Simulation Study

In this article, we present a model-based framework to estimate the educational attainments of students in latent groups defined by unobservable or only partially observed features that are likely to affect the outcome distribution, as well as being interesting to be investigated. We focus our attention on the case of students in the first year of the upper secondary schools, for which the teachers’ suggestion at the end of their lower educational level toward the subsequent type of school is available. We use this information to develop latent strata according to the compliance behavior of students simplifying to the case of binary data for both counseled and attended school (i.e., academic or technical institute). We consider a likelihood-based approach to estimate outcome distributions in the latent groups and propose a set of plausible assumptions with respect to the problem at hand. In order to assess our method and its robustness, we simulate data resembling a real study conducted on pupils of the province of Bologna in year 2007/2008 to investigate their success or failure at the end of the first school year.