Parents,Perceptions and Belonging: Exploring Flexible Working among UK Fathers and Mothers

This paper advances knowledge regarding how fathers and mothers perceive and experience flexible working opportunities. It does this through applying the theoretical concept ‘belonging’ to ‘Parsonian’ classifications of parenting and work. In so doing it makes transparent the misconceptions and inequities which exist among parents and their organizational environments. Focusing initially on a qualitative study of fathers’ experience of working flexibly, the paper shows how fathers felt marginalized from the possibilities of flexible work due to line managers’ assumptions that men belonged to an ‘instrumental’ economic provider group. The paper contributes a new angle to debate by articulating how fathers perceived employed mothers as belonging to an ‘expressive’ child‐oriented group, with privileged access to flexibility. However, drawing upon a study of maternity and flexible work we query fathers’ assumptions that flexibility was easily available to mothers, suggesting that fathers’ perceptions of maternal privilege were misconceived. While mothers were categorized as belonging within an ‘expressive’ group associated with childcare, they were nevertheless discouraged from accessing flexibility. Inequities between women and men (with regard to flexibility) thus appeared to be less significant than fathers supposed.     

Does Uncertainty of Firing Costs Reduce Hirings?

This paper examines hiring decision under uncertainty of firing costs. We extend the labor demand problem of the firm to account for a random shock that hits the firing cost function. We also consider recursive preferences for the employer in order to separate risk aversion from intertemporal substitution. We find that a rise of the variance of the shock decreases hiring investment if and only if the intertemporal elasticity of substitution is greater than one. The risk aversion parameter does not determine the sign of the uncertainty effect but only its magnitude.     

韩国公企业的民营化

本研究首先概述有关公企业民营化的理论和模型,然后考察了韩国公企业民营化的历史、实况以及代表性事例,并对其结果进行评价,目的在于为以后成功的民营化探索方向、为中国国有企业的私有化提供可行的方案.     

A simple procedure for the selection of significant effects

Summary.  Given a large number of test statistics, a small proportion of which represent departures from the relevant null hypothesis, a simple rule is given for choosing those statistics that are indicative of departure. It is based on fitting by moments a mixture model to the set of test statistics and then deriving an estimated likelihood ratio. Simulation suggests that the procedure has good properties when the departure from an overall null hypothesis is not too small.     

Bayesian variable selection and regularization for time–frequency surface estimation

Summary.  We describe novel Bayesian models for time–frequency inverse modelling of non-stationary signals. These models are based on the idea of a Gabor regression , in which a time series is represented as a superposition of translated, modulated versions of a window function exhibiting good time–frequency concentration. As a necessary consequence, the resultant set of potential predictors is in general overcomplete—constituting a frame rather than a basis—and hence the resultant models require careful regularization through appropriate choices of variable selection schemes and prior distributions. We introduce prior specifications that are tailored to representative time series, and we develop effective Markov chain Monte Carlo methods for inference. To highlight the potential applications of such methods, we provide examples using two of the most distinctive time–frequency surfaces—speech and music signals—as well as standard test functions from the wavelet regression literature.     

House price indices

The new FT House Price Index aims to end the confusion that arises from the current plethora of indices. Simon Briscoe examines these various indices and explains the methodology behind the FT Index.     

Scholarly Communication 2.0: Exploring Researchers' Opinions on Web 2.0 for Scientific Knowledge Creation, Evaluation and Dissemination

This paper presents the results of a survey aimed at gauging the potential acceptance of a collaborative and Web 2.0 inspired scholarly communication sector. While this sector has seen the birth of a multitude of innovative initiatives, there is still little empirical evidence of the acceptance of such initiatives by researchers. We received 349 completed questionnaires from researchers of many different disciplines. The results of the survey show that there is a strong positive attitude towards Web 2.0 and open publishing approaches. However, the major challenge still resides in combining free dissemination of results with robust and reliable quality control mechanisms.     

Proposed best practice for statisticians in the reporting and publication of pharmaceutical industry‐sponsored clinical trials

In this paper we set out what we consider to be a set of best practices for statisticians in the reporting of pharmaceutical industry‐sponsored clinical trials. We make eight recommendations covering: author responsibilities and recognition; publication timing; conflicts of interest; freedom to act; full author access to data; trial registration and independent review. These recommendations are made in the context of the prominent role played by statisticians in the design, conduct, analysis and reporting of pharmaceutical sponsored trials and the perception of the reporting of these trials in the wider community. Copyright © 2010 John Wiley & Sons, Ltd.     

Bayesian kernel projections for classification of high dimensional data

A Bayesian multi-category kernel classification method is proposed. The algorithm performs the classification of the projections of the data to the principal axes of the feature space. The advantage of this approach is that the regression coefficients are identifiable and sparse, leading to large computational savings and improved classification performance. The degree of sparsity is regulated in a novel framework based on Bayesian decision theory. The Gibbs sampler is implemented to find the posterior distributions of the parameters, thus probability distributions of prediction can be obtained for new data points, which gives a more complete picture of classification. The algorithm is aimed at high dimensional data sets where the dimension of measurements exceeds the number of observations. The applications considered in this paper are microarray, image processing and near-infrared spectroscopy data.     

An alternate version of the conceptual predictive statistic based on a symmetrized discrepancy measure

The conceptual predictive statistic, C_p, is a widely used criterion for model selection in linear regression. C_p serves as an estimator of a discrepancy, a measure that reflects the disparity between the generating model and a fitted candidate model. This discrepancy, based on scaled squared error loss, is asymmetric: an alternate measure is obtained by reversing the roles of the two models in the definition of the measure. We propose a variant of the C_p statistic based on estimating a symmetrized version of the discrepancy targeted by C_p. We claim that the resulting criterion provides better protection against overfitting than C_p, since the symmetric discrepancy is more sensitive towards detecting overspecification than its asymmetric counterpart. We illustrate our claim by presenting simulation results. Finally, we demonstrate the practical utility of the new criterion by discussing a modeling application based on data collected in a cardiac rehabilitation program at University of Iowa Hospitals and Clinics.