Survivor Function Estimators Under Group Sequential Monitoring Based on the Logrank Statistic

In this paper we investigate a group sequential analysis of censored survival data with staggered entry, in which the trial is monitored using the logrank test while comparisons of treatment and control Kaplan-Meier curves at various time points are performed at the end of the trial. We concentrate on two-sample tests under local alternatives. We describe the relationship of the asymptotic bias of Kaplan-Meier curves between the two groups. We show that even if the asymptotic bias of the Kaplan-Meier curve is negligible relative to the true survival, this is not the case for the difference between the curves of the two arms of the trial. A corrected estimator for the difference between the survival curves is presented and by simulations we show that the corrected estimator reduced the bias dramatically and has a smaller variance. The methods of estimation are applied to the Beta-Blocker Heart Attack Trial (1982), a well-known group sequential trial.     

On the Restricted Liu Estimator in the Logistic Regression Model

The logistic regression model is used when the response variables are dichotomous. In the presence of multicollinearity, the variance of the maximum likelihood estimator (MLE) becomes inflated. The Liu estimator for the linear regression model is proposed by Liu to remedy this problem. Urgan and Tez and Mansson et al. examined the Liu estimator (LE) for the logistic regression model. We introduced the restricted Liu estimator (RLE) for the logistic regression model. Moreover, a Monte Carlo simulation study is conducted for comparing the performances of the MLE, restricted maximum likelihood estimator (RMLE), LE, and RLE for the logistic regression model.     

The small sample properties of the restricted principal component regression estimator in linear regression model

In regression analysis, to deal with the problem of multicollinearity, the restricted principal components regression estimator is proposed. In this paper, we compared the restricted principal components regression estimator, the principal components regression estimator, and the ordinary least-squares estimator with each other under the Pitman's closeness criterion. We showed that the restricted principal components regression estimator is always superior to the principal components regression estimator, under certain conditions the restricted principal components regression estimator is superior to the ordinary least-squares estimator under the Pitman's closeness criterion and under certain conditions the principal components regression estimator is superior to the ordinary least-squares estimator under the Pitman's closeness criterion.     

Countering globalization and regionalization: is there a united front within Turkish labour and disadvantaged groups?

Turkey, situated in the periphery of Europe, is governed by anti-labour policies of the Justice and Development Party (AKP) Governments in the last 15 years. Embarking on a historical materialist perspective that draws concepts from Gramsci, this paper questions whether labour can come up with an alternative and form a united front vis-a-vis globalization and European Union membership. It then examines impediments behind the lack of a united front and a viable alternative. The analyses rely on empirical data generated through interviews conducted with trade unionists and representatives from civil society for struggles against patriarchy, environmental degradation, and human rights violations at two critical junctures in 2010 and 2017. This paper argues that labour and struggles within civil society contest neoliberal restructuring with two rival class strategies, namely Ha-vet (Turkish abbreviations for No to Capitalist Europe – Yes to Social Europe) and neomerchantilism, none of which stands as a viable alternative.     

Top foundations' 10‐year plunge into alternatives yields mixed results and high fees

Private foundations control a sizable pool of investment capital. This paper examines the endowment returns of private foundations. Over the 10‐year period, 2006–2015, the top 56 of these organizations fully embraced the “Yale model” of portfolio construction. As a group, they substantially cut allocations to publicly traded stocks and bonds in favor of illiquid alternatives that supposedly offered higher returns and lower volatility. Despite pursuing risky investments in illiquid alternative assets, our study concludes that the foundations were not “paid” for illiquidity in terms of a meaningfully return premium (vs. public markets). Moreover, their annual return volatility, or risk, was similar to either a 60–40 composite index portfolio or a typical public pension plan, both of which had lower equity‐type exposure and greater liquidity. The implication is that foundations can achieve the same returns with better liquidity and/or lower risk with passive investments in broad index mutual funds or similar vehicles. In addition, we estimate the third‐party money management fees of the top foundations to equal 1.43% of the assets for the fiscal year 2016. This 1.43% is a sizable number when compared to the 5% of assets (including overhead expenses) that the federal government requires that foundations distribute each year in furtherance of their charitable missions.     

Testing a Regression Model When We Have Smooth Alternatives in Mind

Goodness-of-fit tests based on residual sums of squares are standard procedures used when fitting regression models. Often we have a smooth alternative in mind, a qualitative feature that the χ²-test does not take into account. We show that the power of detecting a smooth alternative increases when we smooth the current model as well. The proposed test is shown to be able to detect any continuous local alternative tending to zero slower than n ^−1/2. Theoretical results also address minimax non-parametric hypothesis testing in Sobolev spaces. A simulation study is presented, and the procedure is applied to expenditure curve estimation.     

Non-minimaxity of linear combinations of restricted location estimators and related problems

The estimation of a linear combination of several restricted location parameters is addressed from a decision-theoretic point of view. Although the corresponding linear combination of the unbiased estimators is minimax under the restricted problem, it has a drawback of taking values outside the restricted parameter space. Thus, it is reasonable to use the linear combination of the restricted estimators such as maximum likelihood or truncated estimators. In this paper, a necessary and sufficient condition for such restricted estimators to be minimax is derived, and it is shown that the restricted estimators are not minimax when the number of the location parameters is large. The condition for minimaxity is examined for some specific distributions. Finally, similar problems of estimating the product and sum of the restricted scale parameters are studied, and it is shown that analogous non-dominance properties appear when the number of the scale parameters is large.     

Conditional tests in a competing risks model

Testing for equality of competing risks based on their cumulative incidence functions (CIFs) or their cause specific hazard rates (CSHRs) has been considered by many authors. The finite sample distributions of the existing test statistics are in general complicated and the use of their asymptotic distributions can lead to conservative tests. In this paper we show how to perform some of these tests using the conditional distributions of their corresponding test statistics instead (conditional on the observed data). The resulting conditional tests are initially developed for the case of k = 2 and are then extended to k > 2 by performing a sequence of two sample tests and by combining several risks into one. A simulation study to compare the powers of several tests based on their conditional and asymptotic distributions shows that using conditional tests leads to a gain in power. A real life example is also discussed to show how to implement such conditional tests.     

服务转换成本对顾客忠诚的影响——满意度与替代者吸引力的调节效应

通过将顾客满意和替代者吸引力看作是调节转换成本与忠诚之间关系的变量,考察了当满意度和替代者吸引力都不同时,转换成本对忠诚的直接影响效果会发生哪些变化的问题。通过对来自于网络宽带服务业的数据进行分析,结果显示:转换成本只有在满意度和替代者吸引力同时较高或同时较低的情况下,才能直接明显地起到提升顾客忠诚、防止顾客流失的作用;在满意度较低而替代者吸引力较高的情况下,转换成本对于顾客忠诚的作用会失效;当满意度较高而替代者吸引力较低时,由于一定数量的唯利是图者的存在,其对于促销利益和转换损失同样敏感,只要替代者的促销利益还不足以弥补其转换损失,就仍会忠诚于现供应商。     

Presenting welfare reform: poverty porn,telling sad stories or achieving change?

Much is written about the suffering resulting from welfare reform and other cuts for disabled people and others on the receiving end. Usually this is seen as a ‘good thing’. But is it as simple as that? It may not be the same as so-called television poverty porn, but what is the point of constantly recycling welfare reform’s effects? Judging from last May’s general election, doing this does not seem to have had a significant effect on public politics/attitudes. So perhaps we need to look at it more carefully. When might discussion of welfare reform be helpful and when unhelpful? Is not the real task to develop alternatives, rather than hope as the Fabians did, that if we show how awful things are, ‘then something will have to be done’?