DETERMINING THE APPROPRIATE DISCOUNT RATES IN PURE CAPITAL RATIONING

This paper proposes an interpretation of the pure capital rationing problem as it is faced by many managers in decentralized firms in which budgets serve as the principal means of control. It is argued that the appropriate objective for situations such as these is the maximization of either undiscounted earnings over the planning horizon or total value of the investments at the horizon. When either objective function is used in conjunction with the frequently encountered linear programming models for the capital rationing problem, shadow prices result which give rise to discount rates that will reproduce the optimal solution using discounted cash flow as a criterion. These results are then used as a means for clarifying several confusing and misleading statements that have appeared in the literature.     

New imperfect rankings models for ranked set sampling

Ranked set sampling is a sampling approach that leads to improved statistical inference in situations where the units to be sampled can be ranked relative to each other prior to formal measurement. This ranking may be done either by subjective judgment or according to an auxiliary variable, and it need not be completely accurate. In fact, results in the literature have shown that no matter how poor the quality of the ranking, procedures based on ranked set sampling tend to be at least as efficient as procedures based on simple random sampling. However, efforts to quantify the gains in efficiency for ranked set sampling procedures have been hampered by a shortage of available models for imperfect rankings. In this paper, we introduce a new class of models for imperfect rankings, and we provide a rigorous proof that essentially any reasonable model for imperfect rankings is a limit of models in this class. We then describe a specific, easily applied method for selecting an appropriate imperfect rankings model from the class.     

Singularity and Autoregressive Disturbances in Linear Logit Models

The implications of including autoregressive disturbances in linear logit models of demand systems are explored. It is argued that the normality assumption of the error terms is more appropriate in the linear logit model than in a share equation model with additive disturbances (commonly found in the literature). Autoregressive disturbances and their implications for model estimation are discussed in that context. Both theoretical arguments and empirical evidence are presented in favor of the logit specification given the presence of serial correlation.     

An improved comorbidity summary score for measuring disease burden and predicting mortality with applications to two national cohorts

Abstract

Research involving administrative healthcare data to study patient outcomes requires the investigator to account for the patient’s disease burden in order to reduce the potential for biased results. Here we develop a comorbidity summary score based on variable importance measures derived from several statistical and machine learning methods and show it has superior predictive performance to the Elixhauser and Charlson indices when used to predict 1-year, 5-year, and 10-year mortality. We used two large Veterans Administration cohorts to develop and validate the summary score and compared predictive performance using the area under ROC curve (AUC) and the Brier score.     

Tolerance‐based error reduction in multi‐agent bioassays

Some degree of error is inevitable in multi‐agent bioassays regardless of design or measurement technology. Estimation error can be reduced post facto by exploiting the matrix partial ordering of the bioassay survival estimates. The standard method for this is order‐restricted regression (ORR). If the joint action of the bioassay agents admits a tolerance‐based interpretation, additional structure beyond matrix partial ordering is available, leading to a new method of error reduction. This tolerance‐based error reduction (TBER) procedure almost always outperforms ORR. Like ORR, TBER applies to complete factorial bioassay designs and, using weighting, to incomplete designs.     

Amassing rural power in the fight against fracking in Maryland: A report from the field

ABSTRACT

Social workers can mobilize vulnerable populations to shape policy decisions about industrial practices that could have adverse impacts on their wellbeing. One such practice is hydraulic fracturing or “fracking” to extract oil and natural gas from shale rock deposits. There is scant social work literature on mobilizing opposition to fracking despite a proliferation of literature from other disciplines. This article documents the campaign in Maryland that led to the adoption of the first legislative ban on fracking in a U.S. state with shale gas reserves, using social movement theory to identify factors that led to this successful outcome.     

Predictive Inference for Big,Spatial, Non‐Gaussian Data: MODIS Cloud Data and its Change‐of‐Support

Remote sensing of the earth with satellites yields datasets that can be massive in size, nonstationary in space, and non‐Gaussian in distribution. To overcome computational challenges, we use the reduced‐rank spatial random effects (SRE) model in a statistical analysis of cloud‐mask data from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on board NASA's Terra satellite. Parameterisations of cloud processes are the biggest source of uncertainty and sensitivity in different climate models’ future projections of Earth's climate. An accurate quantification of the spatial distribution of clouds, as well as a rigorously estimated pixel‐scale clear‐sky‐probability process, is needed to establish reliable estimates of cloud‐distributional changes and trends caused by climate change. Here we give a hierarchical spatial‐statistical modelling approach for a very large spatial dataset of 2.75 million pixels, corresponding to a granule of MODIS cloud‐mask data, and we use spatial change‐of‐Support relationships to estimate cloud fraction at coarser resolutions. Our model is non‐Gaussian; it postulates a hidden process for the clear‐sky probability that makes use of the SRE model, EM‐estimation, and optimal (empirical Bayes) spatial prediction of the clear‐sky‐probability process. Measures of prediction uncertainty are also given.     

Well balanced: 8 steps to wellness for adults with mental illness and diabetes

Serious mental illness places a tremendous burden on clients, their families, and behavioral and medical health care providers. The co-occurrence of diabetes with mental illness may further compromise daily functioning. Psychiatric nurses can make a significant difference in improving the health and medical outcomes of this client population. A partnership was developed between the University of Rochester School of Nursing and the Western New York Care Coordination Program to evaluate a novel nursing model for adults with both serious mental illness and diabetes mellitus. The Well Balanced program incorporated health promotion, disease management, nursing care management, and evidence-based practice guidelines into 8 Steps to Wellness for a community-based mental health population. During a 16-visit intervention period, psychiatric nurses interacted with 74 clients. As a result of the program, clients experienced improvements in health risk status and in their hemoglobin A1C and reported high satisfaction with the Well Balanced program.