A bibliography of statistical tolerance regions,II

This bibiliography (completing the previous one, JILEK 1981a) contains about 130 references dealing with statistical tolerance regions. References are classified according to the type of probability distribution concerned and according to some other criteria as well     

The effect of board size on shareholder value: Evidence from bank mergers and acquisitions

This study examines the effect of board size on the economic impact of bank mergers and acquisitions (M&A) in the US. Using a hand-collected dataset of 508 M&A between 2012 and 2018, we find that board size is negatively related to acquirer excess returns. In an additional analysis, we show that large boards have positive value implications for banks that combine the CEO and chairman roles as well as for large banks. Our findings indicate that a “one-size-fits-all” approach to board size is not necessarily in the interests of shareholders; instead, a more flexible and proactive formulation is needed.     

Optimization problems for consecutive-2-out-of-n:G system

Abstract

This paper considers the optimization problems for a consecutive-2-out-of-n:G system where n is considered to be fixed or random. When the number of components is constant, the optimal number of components and the optimal replacement time are discussed by minimizing the expected cost rates. Furthermore, we focus on the above discussions again when n is a random variable. We give an approximate value of MTTF and propose the preventive replacement policy, respectively.     

Childhood exposure to the Second World War and financial risk taking in adult life

Adverse childhood experiences might have long-lasting effects on decisions under uncertainty in adult life. Merging the European Survey on Health, Ageing and Retirement with data on conflict events during the Second World War, and relying on region-by-cohort variation in war exposure, we show that warfare exposure during childhood is associated with lower financial risk taking in later life. Individuals who experienced war episodes as children hold less – and are less likely to hold – stocks, but are more likely to hold life insurance, compared to non-exposed individuals. Effects are robust to the inclusion of potential mediating factors, and are tested for nonlinearity and heterogeneity. Moreover, we provide evidence of hedonic adaptation to war, as high and low intensity of war exposure have comparable long-term effects. We also document that war exposure in childhood increases sensitivity to financial uncertainty since exposed-to-war individuals are less likely to hold stocks after periods of high volatility. Finally, we shed light on the most likely mechanism in the relationship between war exposure and financial risk taking – i.e., enhanced sensitivity to uncertainty – and we show that preferences, and not beliefs, channel this relationship.     

Conditional variance estimation via nonparametric generalized additive models

Generalized additive models provide a way of circumventing curse of dimension in a wide range of nonparametric regression problem. In this paper, we present a multiplicative model for conditional variance functions where one can apply a generalized additive regression method. This approach extends Fan and Yao (1998) to multivariate cases with a multiplicative structure. In this approach, we use squared residuals instead of using log-transformed squared residuals. This idea gives a smaller variance than Yu (2017) when the variance of squared error is smaller than the variance of log-transformed squared error. We provide estimators based on quasi-likelihood and an iterative algorithm based on smooth backfitting for generalized additive models. We also provide some asymptotic properties of estimators and the convergence of proposed algorithm. A numerical study shows the empirical evidence of the theory.     

Frequency domain bootstrap for ratio statistics under long-range dependence

A frequency domain bootstrap (FDB) is a common technique to apply Efron’s independent and identically distributed resampling technique (Efron, 1979) to periodogram ordinates – especially normalized periodogram ordinates – by using spectral density estimates. The FDB method is applicable to several classes of statistics, such as estimators of the normalized spectral mean, the autocorrelation (but not autocovariance), the normalized spectral density function, and Whittle parameters. While this FDB method has been extensively studied with respect to short-range dependent time processes, there is a dearth of research on its use with long-range dependent time processes. Therefore, we propose an FDB methodology for ratio statistics under long-range dependence, using semi- and nonparametric spectral density estimates as a normalizing factor. It is shown that the FDB approximation allows for valid distribution estimation for a broad class of stationary, long-range (or short-range) dependent linear processes, without any stringent assumptions on the distribution of the underlying process. The results of a large simulation study show that the FDB approximation using a semi- or nonparametric spectral density estimator is often robust for various values of a long-memory parameter reflecting magnitude of dependence. We apply the proposed procedure to two data examples.     

全球治理视角下G20贸易救济现状与特性分析

以倾向性贸易救济为代表的贸易保护主义对世界经济造成了强烈冲击,成为全球重点治理的领域。鉴于现时期G20的显著影响力和重要地位,从整体概况、调查与被诉国别、产品类别等层面,对G20贸易救济现状与特性进行梳理和分析,并提出G20参与全球贸易协调治理的对策建议,对于完善全球治理机制,推动国际贸易可持续发展具有重要实践意义。     

Assessing additivity in nonparametric models —A kernel‐based method

In this article, we address the testing problem for additivity in nonparametric regression models. We develop a kernel‐based consistent test of a hypothesis of additivity in nonparametric regression, and establish its asymptotic distribution under a sequence of local alternatives. Compared to other existing kernel‐based tests, the proposed test is shown to effectively ameliorate the influence from estimation bias of the additive component of the nonparametric regression, and hence increase its efficiency. Most importantly, it avoids the tuning difficulties by using estimation‐based optimal criteria, while there is no direct tuning strategy for other existing kernel‐based testing methods. We discuss the usage of the new test and give numerical examples to demonstrate the practical performance of the test. The Canadian Journal of Statistics 39: 632–655; 2011. © 2011 Statistical Society of Canada     

Testing the monotonicity or convexity of a function using regression splines

Methods for smoothed isotonic or convex regression are useful in many applications. Sometimes the shape assumptions constitute a priori knowledge about the regression function, but often the shape is part of the research question. The authors propose tests for monotonicity and convexity using constrained and unconstrained regression splines. The tests have good large‐sample properties and the small‐sample behaviour is illustrated through simulations. Extensions to the partial linear model and the generalized regression model are presented. The Canadian Journal of Statistics 39: 89–107; 2011 © 2011 Statistical Society of Canada