Covariance Averaging for Improved Estimation and Portfolio Allocation

We propose a new method for estimating the covariance matrix of a multivariate time series of nancial returns. The method is based on estimating sample covariances from overlapping windows of observations which are then appropriately weighted to obtain the nal covariance estimate. We extend the idea of (model) covariance averaging o ered in the covariance shrinkage approach by means of greater ease of use, exibility and robustness in averaging information over different data segments. The suggested approach does not su er from the curse of dimensionality and can be used without problems of either approximation or any demand for numerical optimization.

Analysis of Extended Partial Least Squares for Monitoring Large-Scale Processes

This paper theoretically analysis the recently proposed "Extended Partial Least Squares" (EPLS) algorithm. After pointing out some conceptual deficiencies, a revised algorithm is introduced that covers the middle ground between Partial Least Squares and Principal Component Analysis. It maximises a covariance criterion between a cause and an effect variable set (partial least squares) and allows a complete reconstruction of the recorded data (principal component analysis). The new and conceptually simpler EPLS algorithm has successfully been applied in detecting and diagnosing various fault conditions, where the original EPLS algorithm did only offer fault detection.

Less of a Puzzle: a new look at the forward forex market

The two-country monetary model is extended to include a consumption externality with habit persistence. The model is simulated using the artificial economy methodology. The 'puzzles' in the forward market are re-examined. The model is able to account for: (a) the low volatility of the forward discount; (b) the higher volatility of expected forward speculative profit; (c) the even higher volatility of the spot return; (d) the persistence in the forward discount; (e) the martingale behavior of spot exchange rates; and (f) the negative covariance between the expected spot return and expected forward speculative profit. It is unable to account for the forward market bias because the volatility of the expected spot return is too large relative to the volatility of the expected forward speculative profit.

Improved reliability in diagnosing faults using multivariate statistics

This paper analyses multivariate statistical techniques for identifying and isolating abnormal process behaviour. These techniques include contribution charts and variable reconstructions that relate to the application of principal component analysis (PCA). The analysis reveals firstly that contribution charts produce variable contributions which are linearly dependent and may lead to an incorrect diagnosis, if the number of principal components retained is close to the number of recorded process variables. The analysis secondly yields that variable reconstruction affects the geometry of the PCA decomposition. The paper further introduces an improved variable reconstruction method for identifying multiple sensor and process faults and for isolating their influence upon the recorded process variables. It is shown that this can accommodate the effect of reconstruction, i.e. changes in the covariance matrix of the sensor readings and correctly re-defining the PCA-based monitoring statistics and their confidence limits. (c) 2006 Elsevier Ltd. All rights reserved.

Non-stationary variogram models for geostatistical sampling optimisation: An empirical investigation using elevation data

A problem with use of the geostatistical Kriging error for optimal sampling design is that the design does not adapt locally to the character of spatial variation. This is because a stationary variogram or covariance function is a parameter of the geostatistical model. The objective of this paper was to investigate the utility of non-stationary geostatistics for optimal sampling design. First, a contour data set of Wiltshire was split into 25 equal sub-regions and a local variogram was predicted for each. These variograms were fitted with models and the coefficients used in Kriging to select optimal sample spacings for each sub-region. Large differences existed between the designs for the whole region (based on the global variogram) and for the sub-regions (based on the local variograms). Second, a segmentation approach was used to divide a digital terrain model into separate segments. Segment-based variograms were predicted and fitted with models. Optimal sample spacings were then determined for the whole region and for the sub-regions. It was demonstrated that the global design was inadequate, grossly over-sampling some segments while under-sampling others.

Patterns of neurocognitive impairment in first-episode bipolar disorder and schizophrenia.

BACKGROUND:
Researching psychotic disorders in unison rather than as separate diagnostic groups is widely advocated, but the viability of such an approach requires careful consideration from a neurocognitive perspective.
AIMS:
To describe cognition in people with bipolar disorder and schizophrenia and to examine how known causes of variability in individual's performance contribute to any observed diagnostic differences.
METHOD:
Neurocognitive functioning in people with bipolar disorder (n = 32), schizophrenia (n = 46) and healthy controls (n = 67) was compared using analysis of covariance on data from the Northern Ireland First Episode Psychosis Study.
RESULTS:
The bipolar disorder and schizophrenia groups were most impaired on tests of memory, executive functioning and language. The bipolar group performed significantly better on tests of response inhibition, verbal fluency and callosal functioning. Between-group differences could be explained by the greater proclivity of individuals with schizophrenia to experience global cognitive impairment and negative symptoms.
CONCLUSIONS:
Particular impairments are common to people with psychosis and may prove useful as endophenotypic markers. Considering the degree of individuals' global cognitive impairment is critical when attempting to understand patterns of selective impairment both within and between these diagnostic groups.

The Association Between Childhood Trauma and Memory Functioning in Schizophrenia

Objective: Both neurocognitive impairments and a history of childhood abuse are highly prevalent in patients with schizophrenia. Childhood trauma has been associated with memory impairment as well as hippocampal volume reduction in adult survivors. The aim of the following study was to examine the contribution of childhood adversity to verbal memory functioning in people with schizophrenia. Methods: Eighty-five outpatients with a Diagnostic and Statistical Manual of Mental Disorders (Fourth Edition) diagnosis of chronic schizophrenia were separated into 2 groups on the basis of self-reports of childhood trauma. Performance on measures of episodic narrative memory, list learning, and working memory was then compared using multivariate analysis of covariance. Results: Thirty-eight (45%) participants reported moderate to severe levels of childhood adversity, while 47 (55%) reported no or low levels of childhood adversity. After controlling for premorbid IQ and current depressive symptoms, the childhood trauma group had significantly poorer working memory and episodic narrative memory. However, list learning was similar between groups. Conclusion: Childhood trauma is an important variable that can contribute to specific ongoing memory impairments in schizophrenia.

Improved Linear Transmit Processing for Single-User and Multi-User MIMO Communications Systems

In this paper, we propose a novel linear transmit precoding strategy for multiple-input, multiple-output (MIMO) systems employing improper signal constellations. In particular, improved zero-forcing (ZF) and minimum mean square error (MMSE) precoders are derived based on modified cost functions, and are shown to achieve a superior performance without loss of spectrum efficiency compared to the conventional linear and nonlinear precoders. The superiority of the proposed precoders over the conventional solutions are verified by both simulation and analytical results. The novel approach to precoding design is also applied to the case of an imperfect channel estimate with a known error covariance as well as to the multi-user scenario where precoding based on the nullspace of channel transmission matrix is employed to decouple multi-user channels. In both cases, the improved precoding schemes yield significant performance gain compared to the conventional counterparts.

Passing quantum correlations to qubits using any two-mode state

We draw an explicit connection between the statistical properties of an entangled two-mode continuous variable (CV) resource and the amount of entanglement that can be dynamically transferred to a pair of noninteracting two-level systems. More specifically, we rigorously reformulate entanglement-transfer process by making use of covariance matrix formalism. When the resource state is Gaussian, our method makes the approach to the transfer of quantum correlations much more flexible than in previously considered schemes and allows the straightforward inclusion of the effects of noise affecting the CV system. Moreover, the proposed method reveals that the use of de-Gaussified two-mode states is almost never advantageous for transferring entanglement with respect to the full Gaussian picture, despite the entanglement in the non-Gaussian resource can be much larger than in its Gaussian counterpart. We can thus conclude that the entanglement-transfer map overthrows the

A fast multi-output RBF neural network construction method

This paper investigates the center selection of multi-output radial basis function (RBF) networks, and a multi-output fast recursive algorithm (MFRA) is proposed. This method can not only reveal the significance of each candidate center based on the reduction in the trace of the error covariance matrix, but also can estimate the network weights simultaneously using a back substitution approach. The main contribution is that the center selection procedure and the weight estimation are performed within a well-defined regression context, leading to a significantly reduced computational complexity. The efficiency of the algorithm is confirmed by a computational complexity analysis, and simulation results demonstrate its effectiveness. (C) 2010 Elsevier B.V. All rights reserved.

Moving window kernel PCA for adaptive monitoring of nonlinear processes

This paper discusses the monitoring of complex nonlinear and time-varying processes. Kernel principal component analysis (KPCA) has gained significant attention as a monitoring tool for nonlinear systems in recent years but relies on a fixed model that cannot be employed for time-varying systems. The contribution of this article is the development of a numerically efficient and memory saving moving window KPCA (MWKPCA) monitoring approach. The proposed technique incorporates an up- and downdating procedure to adapt (i) the data mean and covariance matrix in the feature space and (ii) approximates the eigenvalues and eigenvectors of the Gram matrix. The article shows that the proposed MWKPCA algorithm has a computation complexity of O(N2), whilst batch techniques, e.g. the Lanczos method, are of O(N3). Including the adaptation of the number of retained components and an l-step ahead application of the MWKPCA monitoring model, the paper finally demonstrates the utility of the proposed technique using a simulated nonlinear time-varying system and recorded data from an industrial distillation column.

The inferential monitoring of the screw disturbance torque to predict process fluctuations in polymer extrusion

Polymer extrusion is one of the major methods of processing polymer materials and advanced process monitoring is important to ensure good product quality. However, commonly used process monitoring devices, e.g. temperature and pressure sensors, are limited in providing information on process dynamics inside an extruder barrel. Screw load torque dynamics, which may occur due to changes in solids conveying, melting, mixing, melt conveying, etc., are believed to be a useful indicator of process fluctuations inside the extruder barrel. However, practical measurement of the screw load torque is difficult to achieve. In this work, inferential monitoring of the screw load torque signal in an extruder was shown to be possible by monitoring the motor current (armature and/or field) and simulation studies were used to check the accuracy of the proposed method. The ability of this signal to aid identification and diagnosis of process issues was explored through an experimental investigation. Power spectral density and wavelet frequency analysis were implemented together with a covariance analysis. It was shown that the torque signal is dominated by the solid friction in the extruder and hence it did not correlate well with melting fluctuations. However, it is useful for online identification of solids conveying issues.

The factor structure of the revised Illness Perceptions Questionnaire in a population of oesophageal cancer survivors

Objectives
To determine whether the proposed 7-factor structure of the Illness Perception Questionnaire-Revised (Timeline Acute/Chronic, Timeline Cyclical, Consequences, Personal Control, Treatment Control, Illness Coherence and Emotional Representations) is appropriate among a population of oesophageal cancer survivors.
Methods
Everyone registered with the Oesophageal Patients’ Association in the UK (n=2185) was mailed a questionnaire booklet which included the Illness Perception Questionnaire-Revised. Responses from 587 oesophageal cancer survivors (27%) were subjected to a confirmatory factor analysis.
Results
The proposed 7 factor structure provided a reasonable fit of the data. Modification indices suggested that a significantly better fit could be provided if one of the items on the Timeline Acute/Chronic factor loaded on the Treatment Control factor and an error covariance was added between 2 other items on the Timeline Acute/Chronic factor.
Conclusions
The model fit for the 7 factor structure proposed by Moss-Morris et al. (2002) was found to be adequate in our study. However, the structure of the timeline acute/chronic factor needs to be considered, particularly when the IPQ-R is to be used among older people with a potentially life-threatening illness or those receiving palliative care.

Ownership type and team climate in elderly care facilities: the moderating effect of stress factors

Aims. This paper is a report of a study examining the association between ownership type and perceived team climate among older people care staff. In addition, we examined whether work stress factors (time pressure, resident-related stress, role conflicts and role ambiguity) mediated or moderated the above mentioned association. Background. There has been a trend towards contracting out in older people care facilities in Finland and the number of private for-profit firms has increased. Studies suggest that there may be differences in employee well-being and quality of care according to the ownership type of older people care. Methods. Cross-sectional survey data was collected during the autumn of 2007 from 1084 Finnish female older people care staff aged 1869 years were used. Team Climate Inventory was used to measure team climate. Ownership type was divided into four categories: for-profit sheltered homes, not-for-profit sheltered homes, public sheltered homes and not-for-profit nursing homes. Analyses of covariance were used to examine the associations. Results. Team climate dimensions participative safety, vision and support for innovation were higher in not-for-profit organizations (both sheltered homes and nursing homes) compared to for-profit sheltered homes and public sheltered homes. Stress factors did not account for these associations but acted as moderators in a way that in terms of task orientation and participative safety employees working in for-profit organizations seemed to be slightly more sensitive to work-related stress than others. Conclusion. Our results suggest that for-profit organizations and public organizations may have difficulties in maintaining their team climate. In consequence, these organizations should focus more effort on improving their team climate.

On MIMO Radar Subarrayed Transmit Beamforming

Multiple-input-multiple-output (MIMO) radar schemes whereby the transmit array is partitioned into subarrays have recently been proposed in the literature to combine advantages of phased array and MIMO radar technology. In this work, we utilize this architecture to significantly simplify a transmit procedure in which the covariance matrix across the MIMO radar array is optimized to improve the Cramer-Rao bound (CRB) on target parameter estimation. The MIMO effective array for regular subarrayed transmit apertures is studied, and necessary conditions to obtain a filled effective aperture are presented, which is important for maintaining nonambiguous, low sidelobe beampatterns. The performance of the subarrayed transmit approach is evaluated in terms of the CRB on target parameter estimation, and the optimisation of the beamformer applied to the subarrays to minimize the CRB is considered. The subarrayed transmit scheme is found to have a CRB which is suboptimal to the full diversity transmission, as expected, but is solvable in a small fraction of the time using an iterative beamspace algorithm developed here.