微妙地使用权力

要想实现一个人的目标，至关重要的一点就是要学会有效地利用自己手中的权力。在完成了与苏联的裁减核武器条约谈判，并主持过Bechtel公司的工作之后，乔治·舒尔茨开始真正彻底地理解了权力的内涵。他的建议是：“不要害怕权力，但一定要学会对它负责。”     

An evaluation of the use of diaries in a study of medication in later life

This paper is based upon our experience of commissioning diaries to be kept by research subjects. First the paper reviews some previous research based on diaries, then it outlines the aims of our research and the method of data collection. Then the paper goes on to evaluate the design and use of a diary that participants in our research completed. In particular we consider the ways in which the diary may have biased our sample, caused the participants difficulties and generated poor data. There is also a discussion on how it may have affected behaviour and on the ethical issues that are raised by commissioned diaries. In conclusion, observations are made about the strengths and weaknesses of diaries as an investigative tool with a wider applicability. The diary proved particularly revealing and we recommend that other researchers consider diaries as a method of investigating everyday life.     

An Analysis of Nonlinearities,Heteroscedasticity, and Functional Form in the Market Model

Using a generalized specification of the single-index market model, this study examines the sources of statistical anomalies previously found in estimating the market model. Two generalized models are developed for juxtaposition with the traditional linear specification. The most general model is a Box–Cox model with different λ's and heteroscedastic errors. The empirical results indicate that previous findings of significant “nonlinearities” are primarily attributable to nonnormalities and unequal variance.     

Testing for heteroskedasticity in the tobit and probit models

Non-constant variance across observations (heteroskedasticity) results in the maximum likelihood estimators of tobit and probit model parameters being inconsistent. Some of the available tests for constant variance across observations (homoskedasticity) are discussed and examined in a small Monte Carlo experiment.     

Improved Sampling-Importance Resampling and Reduced Bias Importance Sampling

Abstract.  The sampling-importance resampling (SIR) algorithm aims at drawing a random sample from a target distribution π. First, a sample is drawn from a proposal distribution q , and then from this a smaller sample is drawn with sample probabilities proportional to the importance ratios π/ q . We propose here a simple adjustment of the sample probabilities and show that this gives faster convergence. The results indicate that our version converges better also for small sample sizes. The SIR algorithms are compared with the Metropolis–Hastings (MH) algorithm with independent proposals. Although MH converges asymptotically faster, the results indicate that our improved SIR version is better than MH for small sample sizes. We also establish a connection between the SIR algorithms and importance sampling with normalized weights. We show that the use of adjusted SIR sample probabilities as importance weights reduces the bias of the importance sampling estimate.