Why perceived organizational and supervisory family support is important for organizations? Evidence from the field

Review of Managerial Science - The relationship between perceived organizational support and its work-outcomes were usually based on social exchange theories. By keeping the social exchange...     

CAN TRADE REALLY HURT? AN EMPIRICAL FOLLOW‐UP ON SAMUELSON'S CONTROVERSIAL PAPER

This paper investigates Samuelson's [Samuelson, P. A. “Where Ricardo and Mill Rebut and Confirm Arguments of Mainstream Economists Supporting Globalization.” Journal of Economic Perspectives, 18(3), 2004, 135–46] argument that technical progress of the trade partner may hurt the home country. We illustrate this prospect in a simple Ricardian model for situations with outward knowledge spillovers. Within this framework Samuelson's Act II effects may occur. Based on industry level panel data for 17 OECD countries for the period 1973–2000 we show econometrically that the outflow of domestic knowledge via exports or foreign direct investment (FDI) to the rest of the world may have a negative impact on industry output in the home country. This is particularly so when exporting to technologically less advanced countries and, more specifically, China. (JEL F10, F11, F14, O30)     

Telling about something that you do not really know: blind people are talking about vision!

In Turkey, the laws which define disability have become increasingly harsh, and require disabled people to be assessed in order to determine ‘how disabled’ they are. For blind people, as for other disabled people, these assessments have real consequences. This article aims to discuss an appealing piece that arose during a project, which has bearing on how disabled people tend to answer these questions. The participants are legally blind adults. We, as sighted researchers, asked totally blind individuals to describe their vision. Interestingly, they all answered this question without hesitation. The paradoxical nature of this question was only realized during the analysis. We requested the participants to talk about something they do not really know. This might be a reflection of our over-visualized world and might be due to our subtle prejudices. This type of questioning not only has implications for research, but also for assessments of disability in Turkey.     

Prostate-specific antigen decline pattern in advanced prostate cancer receiving androgen deprivation therapy and relationship with prostate-specific antigen progression

Introduction: The aim of this study is to evaluate prostate-specific antigen decline pattern including prostate-specific antigen kinetics following androgen deprivation therapy on prostate-specific antigen progression in the patients with advanced prostate cancer.

Materials and methods: Ninety-seven advanced prostate cancer patients receiving maximum androgen deprivation therapy were enrolled in case–control study. Baseline prostate-specific antigen, Gleason Score, bone metastase, nadir prostate-specific antigen, time to nadir prostate-specific antigen, declining slope to nadir prostate-specific antigen, estimated baseline prostate-specific antigen half-time, current prostate-specific antigen, post-nadir prostate-specific antigen time, estimated prostate-specific antigen, estimated decline of baseline prostate-specific antigen as quantitative, and ratio were recorded and calculated.

Results: The ratio of prostate-specific antigen progression was significantly lower at the patients who had slower declining slope to prostate-specific antigen, longer time to nadir prostate-specific antigen, and lower estimated decline ratio of baseline prostate-specific antigen (p: .016, p: .020, and p: .026, respectively).

Conclusions: The shorter time to nadir prostate-specific antigen following androgen deprivation therapy, faster declining slope to nadir prostate-specific antigen and higher estimated decline ratio of baseline prostate-specific antigen are associated with higher risk of disease progression in patients with hormone-sensitive prostate cancer.     

The evaluation of socio-economic development of development agency regions in Turkey using classical and robust principal component analyses

In this study, classical and robust principal component analyses are used to evaluate socioeconomic development of regions of development agencies that give service on the purpose of decreasing development difference among regions in Turkey. Due to the high differences between development levels of regions outlier problem occurs, hence robust statistical methods are used. Also, classical and robust statistical methods are used to investigate if there are any outliers in data set. In classic principal component analyse, the number of observations must be larger than the number of variables. Otherwise determinant of covariance matrix is zero. In Robust method for Principal Component Analysis (ROBPCA), a robust approach to principal component analyse in high-dimensional data, even if the number of variables is larger than the number of observations, principal components are obtained. In this paper, firstly 26 development agencies are evaluated with 19 variables by using principal component analysis based on classical and robust scatter matrices and then these 26 development agencies are evaluated with 46 variables by using the ROBPCA method.     

Should occupational codes be obtained from census data or from retrospective survey data in studies on occupational health?

The aim of this study was to assess the quality of occupational history information obtained via retrospective interview questions by comparison with occupational information for the same individuals obtained via a number of censuses. Moreover, morbidity patterns were investigated for different occupational exposure categories that were constructed either on occupational data gathered retrospectively or on data gathered longitudinally through censuses. The study population consisted of 6764 men and 2104 women who were interviewed within the Swedish Survey on Living Conditions. Census data on occupation from 1960, 1970 and 1975 were used for comparison. There was high level of agreement between the two data bases for occupational categories containing workers who were stable in their occupations. The correspondence was better for women. Prevalence rates for different long-term illnesses were almost the same for the categories, no matter which of the data bases was used for constructing the categories. Thus, we have found that self-reported work histories might be a useful way of controlling for occupational exposures in the past.     

A copula-based multivariate hidden Markov model for modelling momentum in football

AStA Advances in Statistical Analysis - We investigate the potential occurrence of change points—commonly referred to as “momentum shifts”—in the dynamics of football...     

Replenishing the pipeline: A quantitative approach to optimising the sourcing of new projects

Large pharmaceutical companies maintain a portfolio of assets, some of which are projects under development while others are on the market and generating revenue. The budget allocated to R&D may not always be sufficient to fund all the available projects for development. Much attention has been paid to the selection of optimal subsets of available projects to fit within the available budget. In this paper, we argue the need for a forward-looking approach to portfolio decision-making. We develop a quantitative model that allows the portfolio management to evaluate the need for future inflow of new projects to achieve revenue at desired levels, often aspiring to a certain annual revenue growth. Optimisation methods are developed for the presented model, allowing an optimal choice of number, timing and type of projects to be added to the portfolio. The proposed methodology allows for a proactive approach to portfolio management, prioritisation, and optimisation. It provides a quantitatively based support for strategic decisions regarding the efforts needed to secure the future development pipeline and revenue stream of the company.     

An evaluation of ridge estimator in linear mixed models: an example from kidney failure data

This paper is concerned with the ridge estimation of fixed and random effects in the context of Henderson's mixed model equations in the linear mixed model. For this purpose, a penalized likelihood method is proposed. A linear combination of ridge estimator for fixed and random effects is compared to a linear combination of best linear unbiased estimator for fixed and random effects under the mean-square error (MSE) matrix criterion. Additionally, for choosing the biasing parameter, a method of MSE under the ridge estimator is given. A real data analysis is provided to illustrate the theoretical results and a simulation study is conducted to characterize the performance of ridge and best linear unbiased estimators approach in the linear mixed model.