Likelihood Contour Method for the Calculation of Asymptotic Upper Confidence Limits on the Risk Function for Quantitative Responses

This article develops a computationally and analytically convenient form of the profile likelihood method for obtaining one-sided confidence limits on scalar-valued functions phi = phi(psi) of the parameters psi in a multiparameter statistical model. We refer to this formulation as the likelihood contour method (LCM). In general, the LCM procedure requires iterative solution of a system of nonlinear equations, and good starting values are critical because the equations have at least two solutions corresponding to the upper and lower confidence limits. We replace the LCM equations by the lowest order terms in their asymptotic expansions. The resulting equations can be solved explicitly and have exactly two solutions that are used as starting values for obtaining the respective confidence limits from the LCM equations. This article also addresses the problem of obtaining upper confidence limits for the risk function in a dose-response model in which responses are normally distributed. Because of normality, considerable analytic simplification is possible and solution of the LCM equations reduces to an easy one-dimensional root-finding problem. Simulation is used to study the small-sample coverage of the resulting confidence limits.     

Concepts of aggregation and their quantification: A critical review with some new results and applications

Summary Various indices of spatial patterns based on plot counts are reviewed for theoretical population models appropriate to ecological studies. It is seen that many of the indices proposed in the literature are essentially equivalent to either the index ω= σ²/μ or to the index γ= (σ²-μ)/μ², thus providing a variety of motiviations and interpretations of these two indices as measures of spatial patterns. A vector approach to measuring spatial patterns which suggests both a unifying relationship between these indices and an extension of them is proposed. This leads to an interpretation of the measures of spatial patterns in terms of the transition probabilities of a pure birth process. Part of a Doctoral Dissertation of the junior author at the Pennsylvania State University.     

The Impact of Gender Characteristics on Mentoring in Graduate Departments of Sociology

There has been much research on gender inequality in graduate education and the benefits of mentoring. However, most of this research focuses on how mentoring addresses female graduate students’ experiences of gender inequality instead of how the gender characteristics of departments impact the level of mentoring they offer. In particular, I examine how the gender composition of departments in terms of faculty and administration as well as faculty awareness of gender inequality or feminist issues influences the types of mentoring structures found in graduate departments of sociology. Bivariate correlations indicated that these characteristics (gender characteristics in particular) had significant relationships with mentoring structures in departments. This study concludes with a discussion of these relationships, an overview of policy implications, and suggestions for future research.     

Fertility Behaviour under Income Uncertainty

This paper develops a two-period stochastic model of fertility behaviour to provide a possible explanation for the recent sharp decline in birth rates in the former Soviet Republics and Eastern European countries. Due to the existence of irreversibilities associated with the childbearing decision and the option of postponing childbearing for a later time, it may be optimal for individuals to postpone childbearing during times of increased income uncertainty.     

Ranked set sample sign test for quantiles

A ranked set sample version of the sign test is proposed for testing hypotheses concerning the quantiles of a population characteristic. Both equal and unequal allocations are considered and the relative performance of different allocations is assessed in terms of Pitman's asymptotic relative efficiency. In particular, for each quantile, the allocation that maximizes the efficacy is identified and shown to not depend on the population distribution.     

Comparison of Sensitivity Analysis Methods Based on Applications to a Food Safety Risk Assessment Model

Sensitivity analysis (SA) methods are a valuable tool for identifying critical control points (CCPs), which is one of the important steps in the hazard analysis and CCP approach that is used to ensure safe food. There are many SA methods used across various disciplines. Furthermore, food safety process risk models pose challenges because they often are highly nonlinear, contain thresholds, and have discrete inputs. Therefore, it is useful to compare and evaluate SA methods based upon applications to an example food safety risk model. Ten SA methods were applied to a draft Vibrio parahaemolyticus (Vp) risk assessment model developed by the Food and Drug Administration. The model was modified so that all inputs were independent. Rankings of key inputs from different methods were compared. Inputs such as water temperature, number of oysters per meal, and the distributional assumption for the unrefrigerated time were the most important inputs, whereas time on water, fraction of pathogenic Vp, and the distributional assumption for the weight of oysters were the least important inputs. Most of the methods gave a similar ranking of key inputs even though the methods differed in terms of being graphical, mathematical, or statistical, accounting for individual effects or joint effect of inputs, and being model dependent or model independent. A key recommendation is that methods be further compared by application on different and more complex food safety models. Model independent methods, such as ANOVA, mutual information index, and scatter plots, are expected to be more robust than others evaluated.     

A Note on the Use of Some Functions of Spacings in the Estimation of Common Scale Parameter of Several Symmetric Distributions

In this article, we consider the problem of best linear unbiased estimation and best linear invariant estimation of the common scale parameter of several symmetric distributions using some functions of spacings of all observations taken from individual samples. We also proved a sufficient condition for the non negativity of the common scale estimator obtained by the above method. Furthermore, we obtained necessary and sufficient conditions for the derived estimators to be constant multiple of the sum of first and last spacings of the pooled sample.     

On discrete weighted distributions and their use in model choice for observed data

This paper provides a brief structural perspective of discrete weighted distributions in theory and practice.. It develops a unified view of previous work involving univariate and bivariate models with some new results pertaining to mixtures, form-invariance and Bayesian inference     

Incentive issues in performance-based outsourcing contracts in the UK defence industry: a simulation study

Traditional outsourcing literature has claimed gains for the customer in terms of quality and costs. However, such gains are illusory in outsourcing of high-risk, complex tasks. The use of contracts and governance mechanisms for handling complex procurements is essential in obtaining rewards from outsourcing. Powerful incentives and risks are normally used in industrial service contracts to transfer risks to measure compliance with performance measures. The availability contracts for complex engineering services provision are forms of outsourcing contracts that transfer resources from government to external service providers on a substantial scale. The change moves the contractor role from creating resources to managing resources. Such role change mandates collaboration with customers and suppliers in supply/value chains. The management task is then perceived in terms of linking and optimising alignments rather than increasing service levels. Incentive design is one mechanism for linking the coordination of resources required in availability contracting to the business model. This article studies the impacts of agreed contract type and incentive mechanism on the customer and service provider profits using agent-based discrete event simulation model under multiple risk sharing scenarios.     

Researchers’ interpretations of research integrity: A qualitative study

Despite increasing interest in integrity issues, relatively few studies have examined researchers’ own interpretations of integrity. As part of the Perspectives on Research Integrity in Science and Medicine (PRISM) project, we sought to explore how researchers themselves define research integrity. We conducted 33 semi-structured interviews with clinical and laboratory-based researchers from across Switzerland. Data were transcribed and coded using thematic analysis and illustrative quotes were selected. Researchers defined integrity in terms of honesty, transparency, and objectivity, and generally stressed the importance of sticking to the research question and avoiding bias in data interpretation. Some saw research integrity as being synonymous with scientific integrity, but others regarded research integrity as being a subset of the wider domain of scientific integrity. A few participants equated research integrity with mere absence of misconduct, but the majority of participants regarded integrity as being more than this. Researchers regarded truth as the key aspect of integrity, though they expressed this in different ways and with various emphases on honesty, transparency, and objectivity. Integrity goes beyond avoiding misconduct, and scientific integrity has a wider domain than research integrity.