Modification of the kolmogorov-smirnov test for the erlang-2 distribution

An adjusted Kolmogorov-Smirnov statistic and critical values are developed for the Erlang-2 probability distribution using data from Monte Carlo simulations. The process used is similar to that of Stephens in the 1970s. The test statistic produced features of compactness and ease of implementation. It is quite accurate for sample sizes as low as ten.     

SOME PROPERTIES OF A FOLDOVER DESIGN

The foldover design of the 12-run Plackett-Burman design is shown to give a 2⁴and 2^4-1design in every set of four factors, and to be resolution V in every set of five factors. In addition, the design allows the search and estimation of up to two non-zero interactions.     

POPAN-4: Enhancements to a system for the analysis of mark-recapture data from open populations

We describe recent developments in the POPAN system for the analysis of mark-recapture data from Jolly-Seber type experiments. The previous versions (POPAN-3 for SUN/OS workstations and POPAN-PC for IBM-PC running DOS or Windows) included general statistics gathering and testing procedures, a wide range of analysis options for estimating population abundance, survival and birth parameters, and a general simulation capability. POPAN-4 adds a very general procedure for fitting constrained models based on a new unified theory for Jolly-Seber models. Users can impose constraints on capture, survival and birth rates over time and/or across attribute groups (e.g. sex or age groups) and can model these rates using covariate models involving auxiliary variables (e.g. sampling effort).     

POPAN-6: Exploring convergence and estimate properties with SIMULATE

We describe some developments in the P OPAN system for the analysis of mark-recapture data from Jolly-Seber (JS) type experiments. The latest version, P OPAN-6, adopts the Design Matrix approach for specifying constraints and then uses it in the constrained maximization of the likelihood. We describe how this is done and the difference it makes to convergence and parameter identifiability over the constraint contrast-equation methods used in P OPAN-5. Then we show how the SIMULATE capabilities of P OPAN can be used to explore the properties of estimates, including their identifiability, precision, and robustness to model misspecification or capture heterogeneity.     

How managers plan—the analysis of managers' activities

This paper analyzes the positions of Mintzberg and planning experts on the extent to which managers plan and the importance of planning to management. Mintzberg contends that managers do not plan. The ‘planning literature’ contends that planning leads to effective management. In a replication of Mintzberg's study, the authors found support for the planning literature's position. The discrepancy between Mintzberg and the planning literature is explained by the shortcomings of the observational method used by Mintzberg. A suggested modification of Mintzberg's method is given.     

Likelihood Inference for Discretely Observed Nonlinear Diffusions

This paper is concerned with the Bayesian estimation of nonlinear stochastic differential equations when observations are discretely sampled. The estimation framework relies on the introduction of latent auxiliary data to complete the missing diffusion between each pair of measurements. Tuned Markov chain Monte Carlo (MCMC) methods based on the Metropolis‐Hastings algorithm, in conjunction with the Euler‐Maruyama discretization scheme, are used to sample the posterior distribution of the latent data and the model parameters. Techniques for computing the likelihood function, the marginal likelihood, and diagnostic measures (all based on the MCMC output) are developed. Examples using simulated and real data are presented and discussed in detail.