Consistent estimation in the bilinear multivariate errors-in-variables model

A bilinear multivariate errors-in-variables model is considered. It corresponds to an overdetermined set of linear equations AXB=C, A∈ℝ^m×n, B∈ℝ^p×q, in which the data A, B, C are perturbed by errors. The total least squares estimator is inconsistent in this case.  An adjusted least squares estimator is constructed, which converges to the true value X, as m →∞, q →∞. A small sample modification of the estimator is presented, which is more stable for small m and q and is asymptotically equivalent to the adjusted least squares estimator. The theoretical results are confirmed by a simulation study. Acknowledgements. We thank two anonymous reviewers for their suggestions and corrections.? A. Kukush is supported by a postdoctoral research fellowship of the Belgian office for Scientific, Technical and Cultural Affairs, promoting Scientific and Technical Collaboration with Central and Eastern Europe.? S. Van Huffel is a full professor with the Katholieke Universiteit Leuven.? I. Markovsky is a research assistant with the Katholieke Universiteit Leuven.? This paper presents research results of the Belgian Programme on Interuniversity Poles of Attraction (IUAP V-22), initiated by the Belgian State, Prime Minister's Office – Federal Office for Scientific, Technical and Cultural Affairs of the Concerted Research Action (GOA) projects of the Flemish Government MEFISTO-666 (Mathematical Engineering for Information and Communication Systems Technology), of the IDO/99/03 project (K.U. Leuven) “Predictive computer models for medical classification problems using patient data and expert knowledge”, of the FWO projects G.0078.01, G.0200.00, and G0.0270.02.? The scientific responsibility is assumed by its authors.     

Inferential Causal Models: Integrating Quality & Quantity

In-depth data analysis plus statistical modeling can produce inferentialcausal models. Their creation thus combines aspects of analysis by close inspection,that is, reason analysis and cross-tabular analysis, with statistical analysis procedures,especially those that are special cases of the generalized linear model (McCullaghand Nelder, 1989; Agresti, 1996; Lindsey, 1997). This paper explores some of the roots of this combined method and suggests some new directions. An exercise clarifies some limitations of classic reason analysis by showing how the cross tabulation of variables with controls for test factors may produce better inferences. Then, given the cross tabulation of several variables, by explicating Coleman effect parameters, logistic regressions, and Poisson log-linear models, it shows how generalized linear models provide appropriate measures of effects and tests of statistical significance. Finally, to address a weakness of reason analysis, a case-control design is proposed and an example is developed.     

Time-Based Management of the New Product Development Process

This study explored the problem of compressing new product development by focusing on the specific phases of the innovation process. These phases manifest significant qualitative differences that require attention for understanding the complexities of accelerating new product development. Based on data from 35 high-technology companies, Necmi Karagozoglu and Warren Brown identified several different acceleration methods. Results revealed unexpected and at times inconsistent insights than those reflected in the case study and anecdotally based literature, and implied also that some of the well documented approaches to successful new product development need to be replaced with their time-based versions.     

AN EMPIRICAL INVESTIGATION INTO THE IMPACT OF PROFIT SHARING SCHEMES OF EXECUTIVIES ON THE CONTENT OF CORPORATE SUBMISSIONS ON PROPOSED ACCOUNTING STANDARDS

This paper examines the comments submitted by UK companies on 20 proposed accounting standards to test the hypotheses that executives favor standards that increase, or dampen the variance of, accounting profit numbers on which their incentive remuneration is based. Test results were generally as hypothesised but only the profit variance outcomes were statistically significant. Allowing for political environment changes affected only the profit variance results. There was no evidence that the relative monetary size of bonus payments was a significant lobbying factor. No significant differences were found between the lobbying preferences of companies with or without executive incentive schemes.     

Do Mothers Matter? A Comparison of Models of the Influence of Mothers' and Fathers' Educational and Occupational Status on Children's Educational Attainment

The first objective of this article is to clarify which model best captures the structure and trend of the influence of social origin on children's education. The second objective is to analyse how general conclusions on historical trends in educational reproduction change if we add mother's status background to the model. Six contrasting hypotheses are derived fromthe body of literature dealing with models on families' socioeconomic status. All hypotheses are translated into empirical models and their explained variance is compared. A pooled data set is used that contains data from the Netherlands, West Germany, and the USA. The Modified Dominance Model, that distinguishes the influence of the highest from thelowest status parent, has the best model fit. Regarding the second objective we see that adding the mother's influence to that of the father's does not change general conclusions on trends in educational reproduction. Over time the influence of both parents decreases continuously. However, the influence of the mother's education and occupational status on children's educational attainment is substantive.     

There's something about saving …

The paper considers how people should plan their saving, given certain assumptions about inheritance and changes in household circumstances. The results are produced by a model that takes into account tax and state benefits as well as various private savings vehicles. It concludes that, for many households, saving for retirement through a ‘pension’ might not be optimum and that the best strategy is to vary the incidence and allocation of saving in response to changes in household circumstances.     

The Multilevel Approach to Repeated Measures for Complete and Incomplete Data

Repeated measurements often are analyzed by multivariate analysis of variance (MANOVA). An alternative approach is provided by multilevel analysis, also called the hierarchical linear model (HLM), which makes use of random coefficient models. This paper is a tutorial which indicates that the HLM can be specified in many different ways, corresponding to different sets of assumptions about the covariance matrix of the repeated measurements. The possible assumptions range from the very restrictive compound symmetry model to the unrestricted multivariate model. Thus, the HLM can be used to steer a useful middle road between the two traditional methods for analyzing repeated measurements. Another important advantage of the multilevel approach to analyzing repeated measures is the fact that it can be easily used also if the data are incomplete. Thus it provides a way to achieve a fully multivariate analysis of repeated measures with incomplete data. This revised version was published online in June 2006 with corrections to the Cover Date.