Correlation between process cheese meltability determined by sensory analysis, computer vision method and Olson and Price test

The meltabilities of 14 process cheese samples were determined at 2 and 4 weeks after manufacture using sensory analysis, a computer vision method, and the Olson and Price test. Sensory analysis meltability correlated with both computer vision meltability (R-2 = 0.71, P < 0.001) and Olson and Price meltability (R-2 = 0.69, P < 0.001). There was a marked lack of correlation between the computer vision method and the Olson and Price test. This study showed that the Olson and Price test gave greater repeatability than the computer vision method. Results showed process cheese meltability decreased with increasing inorganic salt content and with lower moisture/fat ratios. There was very little evidence in this study to show that process cheese meltability changed between 2 and 4 weeks after manufacture..

The correlation structure of spatial autoregressions

This paper investigates how the correlations implied by a first-order simultaneous autoregressive (SAR(1)) process are affected by the weights matrix and the autocorrelation parameter. A graph theoretic representation of the covariances in terms of walks connecting the spatial units helps to clarify a number of correlation properties of the processes. In particular, we study some implications of row-standardizing the weights matrix, the dependence of the correlations on graph distance, and the behavior of the correlations at the extremes of the parameter space. Throughout the analysis differences between directed and undirected networks are emphasized. The graph theoretic representation also clarifies why it is difficult to relate properties ofW to correlation properties of SAR(1) models defined on irregular lattices.

Modeling data with structural and temporal correlation using lower level and higher level multilevel models

Novel imaging techniques are playing an increasingly important role in drug development, providing insight into the mechanism of action of new chemical entities. The data sets obtained by these methods can be large with complex inter-relationships, but the most appropriate statistical analysis for handling this data is often uncertain - precisely because of the exploratory nature of the way the data are collected. We present an example from a clinical trial using magnetic resonance imaging to assess changes in atherosclerotic plaques following treatment with a tool compound with established clinical benefit. We compared two specific approaches to handle the correlations due to physical location and repeated measurements: two-level and four-level multilevel models. The two methods identified similar structural variables, but higher level multilevel models had the advantage of explaining a greater proportion of variation, and the modeling assumptions appeared to be better satisfied.

Mouse movements of motion-impaired users: a submovement analysis

Understanding human movement is key to improving input devices and interaction techniques. This paper presents a study of mouse movements of motion-impaired users, with an aim to gaining a better understanding of impaired movement. The cursor trajectories of six motion-impaired users and three able-bodied users are studied according to their submovement structure. Several aspects of the movement are studied, including the frequency and duration of pauses between submovements, verification times, the number of submovements, the peak speed of submovements and the accuracy of submovements in two-dimensions. Results include findings that some motion-impaired users pause more often and for longer than able-bodied users, require up to five times more submovements to complete the same task, and exhibit a correlation between error and peak submovement speed that does not exist for able-bodied users.

Correlation shifts and real estate portfolio management

The success of any diversification strategy depends upon the quality of the estimated correlation between assets. It is well known, however, that there is a tendency for the average correlation among assets to increase when the market falls and vice-versa. Thus, assuming that the correlation between assets is a constant over time seems unrealistic. Nonetheless, these changes in the correlation structure as a consequence of changes in the market’s return suggests that correlation shifts can be modelled as a function of the market return. This is the idea behind the model of Spurgin et al (2000), which models the beta or systematic risk, of the asset as a function of the returns in the market. This is an approach that offers particular attractions to fund managers as it suggest ways by which they can adjust their portfolios to benefit from changes in overall market conditions. In this paper the Spurgin et al (2000) model is applied to 31 real estate market segments in the UK using monthly data over the period 1987:1 to 2000:12. The results show that a number of market segments display significant negative correlation shifts, while others show significantly positive correlation shifts. Using this information fund managers can make strategic and tactical portfolio allocation decisions based on expectations of market volatility alone and so help them achieve greater portfolio performance overall and especially during different phases of the real estate cycle.

Asset allocation, cross-class correlation and the structure of property returns

Practical applications of portfolio optimisation tend to proceed on a “top down” basis where funds are allocated first at asset class level (between, say, bonds, cash, equities and real estate) and then, progressively, at sub-class level (within property to sectors, office, retail, industrial for example). While there are organisational benefits from such an approach, it can potentially lead to sub-optimal allocations when compared to a “global” or “side-by-side” optimisation. This will occur where there are correlations between sub-classes across the asset divide that are masked in aggregation – between, for instance, City offices and the performance of financial services stocks. This paper explores such sub-class linkages using UK monthly stock and property data. Exploratory analysis using clustering procedures and factor analysis suggests that property performance and equity performance are distinctive: there is little persuasive evidence of contemporaneous or lagged sub-class linkages. Formal tests of the equivalence of optimised portfolios using top-down and global approaches failed to demonstrate significant differences, whether or not allocations were constrained. While the results may be a function of measurement of market returns, it is those returns that are used to assess fund performance. Accordingly, the treatment of real estate as a distinct asset class with diversification potential seems justified.

A simple analysis of thermodynamic properties for classical plasmas: I. theory

By eliminating the short range negative divergence of the Debye–Hückel pair distribution function, but retaining the exponential charge screening known to operate at large interparticle separation, the thermodynamic properties of one-component plasmas of point ions or charged hard spheres can be well represented even in the strong coupling regime. Predicted electrostatic free energies agree within 5% of simulation data for typical Coulomb interactions up to a factor of 10 times the average kinetic energy. Here, this idea is extended to the general case of a uniform ionic mixture, comprising an arbitrary number of components, embedded in a rigid neutralizing background. The new theory is implemented in two ways: (i) by an unambiguous iterative algorithm that requires numerical methods and breaks the symmetry of cross correlation functions; and (ii) by invoking generalized matrix inverses that maintain symmetry and yield completely analytic solutions, but which are not uniquely determined. The extreme computational simplicity of the theory is attractive when considering applications to complex inhomogeneous fluids of charged particles.