Donald F. Morrison

Bio: Donald F. Morrison is an academic researcher. The author has contributed to research in topics: Multivariate statistics & Multivariate t-distribution. The author has an hindex of 1, co-authored 1 publications receiving 5797 citations.

Multivariate statistical methods

Abstract: A text designed to make multivariate techniques available to behavioural, social, biological and medical students. Special features include an approach to multivariate inference based on the union-intersection and generalized likelihood ratio principles.

Exploring internal stickiness: Impediments to the transfer of best practice within the firm

Abstract: The ability to transfer best practices internally is critical to a firtn's ability to build competitive advantage through the appropriation of rents from scarce internal knowledge. Just as a firm's distinctive competencies tnight be dificult for other firms to imitate, its best prczctices could be dfficult to imitate internnlly. Yet, little systematic attention has been pcrid to such internal stickiness. The author analyzes itlterrzal stickiness of knowledge transfer crnd tests the resulting model using canonical correlation analysis of a data set consisting of 271 observations of 122 best-practice transfers in eight companies. Contrary to corzverztiorzrzl wisdom that blames primarily motivational factors, the study findings show the major barriers to internal knowledge transfer to be knowledge-related factors such as the recipient's lack oj absorptive capacity, causal anzbiguity, and an arciuous relationship between the source and the recipient. The identification and transfer of best practices cally are hindered less by confidentiality and legal is emerging as one of the most important and obstacles than external transfers, they could be widespread practical management issues of the faster and initially less complicated, all other latter half of the 1990s. Armed with meaningful, things being equal. For those reasons, in an era detailed performance data, firms that use fact- when continuous organizational learning and based management methods such as TQM, bench- relentless performance improvement are needed to marking, and process reengineering can regularly remain competitive, companies must increasingly compare the performance of their units along resort to the internal transfer of capabilitie~.~ operational dimensions. Sparse but unequivocal Yet, experience shows that transferring capaevidence suggests that such comparisons often bilities within a firm is far from easy. General reveal surprising performance differences between Motors had great difficulty in transferring manuunits, indicating a need to improve knowledge facturing practices between divisions (Kerwin and utilization within the firm (e.g., Chew, Bresnahan, Woodruff, 1992: 74) and IBM had limited suc

Sample size in factor analysis.

Abstract: The factor analysis literature includes a range of recommendations regarding the minimum sample size necessary to obtain factor solutions that are adequately stable and that correspond closely to population factors. A fundamental misconception about this issue is that the minimum sample size, or the

Analysis of serial measurements in medical research.

Abstract: In medical research data are often collected serially on subjects. The statistical analysis of such data is often inadequate in two ways: it may fail to settle clinically relevant questions and it may be statistically invalid. A commonly used method which compares groups at a series of time points, possibly with t tests, is flawed on both counts. There may, however, be a remedy, which takes the form of a two stage method that uses summary measures. In the first stage a suitable summary of the response in an individual, such as a rate of change or an area under a curve, is identified and calculated for each subject. In the second stage these summary measures are analysed by simple statistical techniques as though they were raw data. The method is statistically valid and likely to be more relevant to the study questions. If this method is borne in mind when the experiment is being planned it should promote studies with enough subjects and sufficient observations at critical times to enable useful conclusions to be drawn. Use of summary measures to analyse serial measurements, though not new, is potentially a useful and simple tool in medical research.

Optimal Versus Naive Diversification: How Inefficient is the 1/N Portfolio Strategy?

Abstract: We evaluate the out-of-sample performance of the sample-based mean-variance model, and its extensions designed to reduce estimation error, relative to the naive 1-N portfolio. Of the 14 models we evaluate across seven empirical datasets, none is consistently better than the 1-N rule in terms of Sharpe ratio, certainty-equivalent return, or turnover, which indicates that, out of sample, the gain from optimal diversification is more than offset by estimation error. Based on parameters calibrated to the US equity market, our analytical results and simulations show that the estimation window needed for the sample-based mean-variance strategy and its extensions to outperform the 1-N benchmark is around 3000 months for a portfolio with 25 assets and about 6000 months for a portfolio with 50 assets. This suggests that there are still many "miles to go" before the gains promised by optimal portfolio choice can actually be realized out of sample. The Author 2007. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org, Oxford University Press.