Visual Partitioning for Multivariate Models: An approach for identifying and visualizing complex multivariate dataset

Users of statistics quite frequently use multivariate models to make conditional inferences (e.g., stress affects depression, after controlling for gender). These inferences are often done without adequately considering (or understanding) the assumptions one makes when claiming these inferences. A particularly problematic instance of assumption violations is with nonlinear and/or interactive effects. Many of these inferences are not merited because the inference is "contaminated" by the variables and their relationships within the model. In this paper, we highlight when conditional inferences are contaminated by other features of the model and identify the conditions under which variable effects are marginally independent. We then show a strategy for partitioning multivariate effects into uncontaminated blocks using visualizations. This approach simplifies multivariate analyses immensely, without oversimplifying the analysis.

Causation and the Probability of Causal Conditionals

Indicative and counterfactual conditionals are central to reasoning in general and causal reasoning in particular. Normative theorists and psychologists have held a range of views on how natural language indicative and counterfactual conditionals, and probability judgments about them, are related to causation. There is the question of whether “causal” conditionals, referring to possible causes and effects, can be used to explain causation, or whether causation can be used to explain the conditionals. There are questions about how causation, conditionals, Bayesian inferences, conditional probability, and imaging are related to each other. Psychological results are relevant to these questions, including findings on how people make conditional inferences and judgments about possibilities, conditionals, and conditional probability. Deeper understanding of the relation between causation and conditionals will come in further research on people’s reasoning from counterfactuals as premises, and to counterfactuals as conclusions.

AXIOMS FOR FINITE COLLAPSE MODELS OF ARITHMETIC

The collapse models of arithmetic are inconsistent, nontrivial models obtained from ℕ and set out in the Logic of Paradox (LP). They are given a general treatment by Priest (Priest, 2000). Finite collapse models are decidable, and thus axiomatizable, because finite. LP, however, is ill-suited to normal axiomatic reasoning, as it invalidates Modus Ponens, and almost all other usual conditional inferences. I set out a logic, A3, first given by Avron (Avron, 1991), and give a first order axiom system for the finite collapse models. I present some standard arithmetical axioms in addition to a cyclic axiom and prove that these axioms are sound and complete for the cyclic models, reporting a similar result for the heap models. The state of the situation for the each of the kinds of infinite collapse model is, however, left an open question.

A framework for benchmarking of homogenisation algorithm performance on the global scale

The International Surface Temperature Initiative (ISTI) is striving towards substantively improving our ability to robustly understand historical land surface air temperature change at all scales. A key recently completed first step has been collating all available records into a comprehensive open access, traceable and version-controlled databank. The crucial next step is to maximise the value of the collated data through a robust international framework of benchmarking and assessment for product intercomparison and uncertainty estimation. We focus on uncertainties arising from the presence of inhomogeneities in monthly mean land surface temperature data and the varied methodological choices made by various groups in building homogeneous temperature products. The central facet of the benchmarking process is the creation of global-scale synthetic analogues to the real-world database where both the "true" series and inhomogeneities are known (a luxury the real-world data do not afford us). Hence, algorithmic strengths and weaknesses can be meaningfully quantified and conditional inferences made about the real-world climate system. Here we discuss the necessary framework for developing an international homogenisation benchmarking system on the global scale for monthly mean temperatures. The value of this framework is critically dependent upon the number of groups taking part and so we strongly advocate involvement in the benchmarking exercise from as many data analyst groups as possible to make the best use of this substantial effort.

Concepts for benchmarking of homogenisation algorithm performance on the global scale

The International Surface Temperature Initiative (ISTI) is striving towards substantively improving our ability to robustly understand historical land surface air temperature change at all scales. A key recently completed first step has been collating all available records into a comprehensive open access, traceable and version-controlled databank. The crucial next step is to maximise the value of the collated data through a robust international framework of benchmarking and assessment for product intercomparison and uncertainty estimation. We focus on uncertainties arising from the presence of inhomogeneities in monthly surface temperature data and the varied methodological choices made by various groups in building homogeneous temperature products. The central facet of the benchmarking process is the creation of global scale synthetic analogs to the real-world database where both the "true" series and inhomogeneities are known (a luxury the real world data do not afford us). Hence algorithmic strengths and weaknesses can be meaningfully quantified and conditional inferences made about the real-world climate system. Here we discuss the necessary framework for developing an international homogenisation benchmarking system on the global scale for monthly mean temperatures. The value of this framework is critically dependent upon the number of groups taking part and so we strongly advocate involvement in the benchmarking exercise from as many data analyst groups as possible to make the best use of this substantial effort.

Observations From Radical Innovation Projects Considering the Company Context

The development of product-service innovation projects within the context of a company is not yet supported by clear theories and methodologies. Our objective is to analyze innovation and idea generation for such projects from the fuzzy front end to the selected design concept, assessing their potential to be successfully developed and launched on the market. We present a protocol study, using which data derived from 19 innovation projects of five types and conducted by 86 students are analyzed. Sixty-one variables are observed, thus generating 700 data vectors. Bayesian network learning is used to explore conditional inferences among these variables. We examine conditional probabilities between the innovation process means and the significant results produced for the company, modulated by the influence of contextual variables. A number of surprising findings are drawn about the link between problem setting and problem solving processes, the importance of certain contextual variables, and the potential discrepancies between the apparent and produced results of innovative projects. Conducted analyses imply the need for novel innovation evaluation frameworks.