Robust Bayesian estimation of a bathtub-shaped distribution under progressive Type-II censoring

The bathtub-shaped failure rate function has been used for modeling the life spans of a number of electronic and mechanical products, as well as for modeling the life spans of humans, especially when some of the data are censored. This article addresses robust methods for the estimation of unknown parameters in a two-parameter distribution with a bathtub-shaped failure rate function based on progressive Type-II censored samples. Here, a class of flexible priors is considered by using the hierarchical structure of a conjugate prior distribution, and corresponding posterior distributions are obtained in a closed-form. Then, based on the square error loss function, Bayes estimators of unknown parameters are derived, which depend on hyperparameters as parameters of the conjugate prior. In order to eliminate the hyperparameters, hierarchical Bayesian estimation methods are proposed, and these proposed estimators are compared to one another based on the mean squared error, through Monte Carlo simulations for various progressively Type-II censoring schemes. Finally, a real dataset is presented for the purpose of illustration.     

US foreign aid and economic policy concessions*

Why do donors continue to provide foreign aid despite its failure to help poor countries over the past several decades? While some scholars argue that foreign aid is purely for humanitarian purposes, others assert that such aid serves as a tool to pressure recipient countries into accepting policy concessions. In this study, we subject these arguments to empirical testing using a dataset that integrates the amount of US aid and economic policies of recipient countries for 1995–2012. The findings suggest that aid decisions correspond to the interests of the United States, such as policy concessions in economic and business liberalization. However, an increase in US aid is not directly associated with further economic reforms in recipient countries. We conclude that US foreign aid programmes are strategic in nature and successful not in alleviating economic problems in recipient countries but, at the very least, in buying their policy compliance.     

Bayesian analysis of panel data using an MTAR model

Bayesian analysis of panel data using a class of momentum threshold autoregressive (MTAR) models is considered. Posterior estimation of parameters of the MTAR models is done by using a simple Markov Chain Monte Carlo (MCMC) algorithm. Selection of appropriate differenced variables, test for asymmetry and unit roots are recast as model selections and a simple way of computing posterior probabilities of the candidate models is proposed. The proposed method is applied to the yearly unemployment rates of 51 US states and the results show strong evidence of stationarity and asymmetry.     

Double unit root tests for cross-sectionally dependent panel data

This paper proposes various double unit root tests for cross-sectionally dependent panel data. The cross-sectional correlation is handled by the projection method [P.C.B. Phillips and D. Sul, Dynamic panel estimation and homogeneity testing under cross section dependence, Econom. J. 6 (2003), pp. 217–259; H.R. Moon and B. Perron, Testing for a unit root in panels with dynamic factors, J. Econom. 122 (2004), pp. 81–126] or the subtraction method [J. Bai and S. Ng, A PANIC attack on unit roots and cointegration, Econometrica 72 (2004), pp. 1127–1177]. Pooling or averaging is applied to combine results from different panel units. Also, to estimate autoregressive parameters the ordinary least squares estimation [D.P. Hasza and W.A. Fuller, Estimation for autoregressive processes with unit roots, Ann. Stat. 7 (1979), pp. 1106–1120] or the symmetric estimation [D.L. Sen and D.A. Dickey, Symmetric test for second differencing in univariate time series, J. Bus. Econ. Stat. 5 (1987), pp. 463–473] are used, and to adjust mean functions the ordinary mean adjustment or the recursive mean adjustment are used. Combinations of different methods in defactoring to eliminate the cross-sectional dependency, integrating results from panel units, estimating the parameters, and adjusting mean functions yields various available tests for double unit roots in panel data. Simple asymptotic distributions of the proposed test statistics are derived, which can be used to find critical values of the test statistics.

We perform a Monte Carlo experiment to compare the performance of these tests and to suggest optimal tests for a given panel data. Application of the proposed tests to a real data, the yearly export panel data sets of several Latin–American countries for the past 50 years, illustrates the usefulness of the proposed tests for panel data, in that they reveal stronger evidence of double unit roots than the componentwise double unit root tests of Hasza and Fuller [Estimation for autoregressive processes with unit roots, Ann. Stat. 7 (1979), pp. 1106–1120] or Sen and Dickey [Symmetric test for second differencing in univariate time series, J. Bus. Econ. Stat. 5 (1987), pp. 463–473].     

Bayesian analysis of paired survival data using a bivariate exponential distribution

We consider a Bayesian analysis method of paired survival data using a bivariate exponential model proposed by Moran (1967, Biometrika 54:385–394). Important features of Moran’s model include that the marginal distributions are exponential and the range of the correlation coefficient is between 0 and 1. These contrast with the popular exponential model with gamma frailty. Despite these nice properties, statistical analysis with Moran’s model has been hampered by lack of a closed form likelihood function. In this paper, we introduce a latent variable to circumvent the difficulty in the Bayesian computation. We also consider a model checking procedure using the predictive Bayesian P-value.     

Changes in attitude towards people with mental illness in P-city,S. Korea: a comparison between the years 2000 and 2010

The objective of this study was to investigate changes in the attitudes towards people with mental illness in a city located in South Korea (P-city) between 2010 and 2000. Since it was not accessible to raw data of 2000, to compare with 2000 results, the results from the previous research article were used. Results from t-tests and ANCOVA analyses showed positive changes in P-city residents’ attitudes towards people with mental illness, particularly in authoritarianism and social restrictiveness. Discussion includes the role of a successful community mental health centre in bringing about such positive attitudinal changes.     

Inter-organizational information systems visibility in buyer–supplier relationships: The case of telecommunication equipment component manufacturing industry

The concept and operation of supply chain (SC) visibility remain underexplored questions in supply chain management, as is the question of the facilitating role of inter-organizational information systems (IOIS) in achieving SC visibility. This paper seeks to elaborate on the novel concept of IOIS visibility and to explore the antecedents and consequences of IOIS visibility. Investigating SC cooperation from the perspectives of both partners (buyers and suppliers) is important, especially when channel partners depend on each other and when asymmetries in IOIS visibility can exist.     

红外吸收峰的位置波动:归因于单一谱带的频移还是重叠谱带相对强度的变化?

红外吸收峰的位置波动可能由单一谱带的实际频移或是邻近重叠谱带相对强度的变化而产生。本文通过主成分分析法(PCA)有力地证明了光谱位置波动的根源往往归因于后一种机理。PCA能灵敏有效地区分单一谱带的实际频移与由重叠谱带相对强度变化所产生的最大峰移。对于由分子相互作用强度变化所诱导,并在振动光谱领域较为常见的所谓带移的概念,往往由于其隐含了谱带的固有频移而有些概念误导。在许多红外光谱中,峰极大值表面的位移,一般产生于两条重叠谱带相对贡献的变化,而不是分子相互作用强度变化所诱导的单一谱带的缓慢频移。这个观点可以帮助我们解释振动光谱所探测到的分子相互作用。     

Propaganda or public relations campaign? International communication on the war against Iraq

Cross-cultural survey on the war against Iraq from different cultures may help to assess whether the public relations war has succeeded or failed. Overall the public of the U.S. and the U.K. formed similar evaluations of the war; whereas South Korean opinion was lay on the opposite side of the continuum. The present study suggests that public relations campaigns administered by the U.S. government did not generate favorable opinions in countries other than the U.S.     

AGAPE-ET: A Methodology for Human Error Analysis of Emergency Tasks

This article introduces a human error analysis or human reliability analysis methodology, AGAPE-ET (A Guidance And Procedure for Human Error Analysis for Emergency Tasks), for analyzing emergency tasks in nuclear power plants. The AGAPE-ET method is based on a simplified cognitive model and a set of performance-influencing factors (PIFs). At each cognitive function, error-causing factors (ECF) or error-likely situations have been identified considering the characteristics of the performance of each cognitive function and the influencing mechanism of the PIFs on the cognitive function. Then, a human error analysis procedure based on the error analysis factors is organized to cue or guide the analyst in conducting the human error analysis. The method can be characterized by the structured identification of weak points of the task required to be performed and by the efficient analysis process such that the analyst has only to carry out the analysis with the necessary cognitive functions. Through the application, AGAPE-ET showed its usefulness, which effectively identifies the vulnerabilities with respect to cognitive performance as well as task execution, and that helps the analyst directly draw specific error reduction measures through the analysis.