Estimating the intensity of ward admission and its effect on emergency department access block

Emergency department access block is an urgent problem faced by many public hospitals today. When access block occurs, patients in need of acute care cannot access inpatient wards within an optimal time frame. A widely held belief is that access block is the end product of a long causal chain, which involves poor discharge planning, insufficient bed capacity, and inadequate admission intensity to the wards. This paper studies the last link of the causal chain-the effect of admission intensity on access block, using data from a metropolitan hospital in Australia. We applied several modern statistical methods to analyze the data. First, we modeled the admission events as a nonhomogeneous Poisson process and estimated time-varying admission intensity with penalized regression splines. Next, we established a functional linear model to investigate the effect of the time-varying admission intensity on emergency department access block. Finally, we used functional principal component analysis to explore the variation in the daily time-varying admission intensities. The analyses suggest that improving admission practice during off-peak hours may have most impact on reducing the number of ED access blocks.

Rating downgrade and the price impact of CDS spread on stock return

We investigate the time-varying informativeness of credit default swap (CDS) trading on stock returns for 302 US firms from July 2004 to August 2010. Using the Acharya and Johnson (2007) measure, we find that CDS trading becomes informative for an increasing number of firms as we approach the global financial crisis (GFC). Firm numbers gradually decline post-GFC, but remain high compared to the pre-GFC period. furthermore, CDS trading imposes the largest conditional price impact on firms that are recently downgraded, regardless of rating levels. Interestingly, this holds during and after the GFC, but not before. We offer two implications. First, despite post-GFC outcry against the CDS market, our results suggest it exhibits enhanced price discovery during the GFC. Second, our findings support criticism that, in the lead-up to the GFC, rating agencies are slow in downgrading firms. However, if downgrade decisions made during and after the GFC induce informed trading in the CDS market, this necessarily implies that during the midst of the GFC, rating agencies have got their act together.

State dependent asymmetric loss and the consensus forecast of real U.S. GDP growth

It has been well documented that the consensus forecast from surveys of professional forecasters shows a bias that varies over time. In this paper, we examine whether this bias may be due to forecasters having an asymmetric loss function. In contrast to previous research, we account for the time variation in the bias by making the loss function depend on the state of the economy. The asymmetry parameter in the loss function is specified to depend on set state variables which may cause forecaster to intentionally bias their forecasts. We consider both the Lin–Ex and asymmetric power loss functions. For the commonly used Lin–Ex and Lin–Lin loss functions, we show the model can be easily estimated by least squares. We apply our methodology to the consensus forecast of real U.S. GDP growth from the Survey of Professional Forecasters. We find that forecast uncertainty has an asymmetric effect on the asymmetry parameter in the loss function dependent upon whether the economy is in expansion or contraction. When the economy is in expansion, forecaster uncertainty is related to an overprediction in the median forecast of real GDP growth. In contrast, when the economy is in contraction, forecaster uncertainty is related to an underprediction in the median forecast of real GDP growth. Our results are robust to the particular loss function that is employed in the analysis.

Nativity, duration of residence and chronic health conditions in Australia: Do trends converge towards the native-born population?

Using data from waves 3, 7 and 9 of the Household, Income and Labour Dynamics in Australia (HILDA) survey, a group-mean-centred multilevel mixed model and self-reported chronic conditions, this study contributes to the limited longitudinal evidence on the nativity health gap in Australia. We investigated whether differences exist in the reporting of any chronic condition (including cancer, cardiovascular disease (CVD), arthritis, diabetes and respiratory disease), and in the total number of chronic conditions, between foreign-born (FB) from English speaking (ES) and non-English speaking (NES) countries and native-born (NB) Australians. We also investigated differences between these groups in the reporting of any chronic condition, and the total number of chronic conditions, by duration of residence. After adjusting for time varying and time invariant covariates, we found a significant difference by nativity status in the reporting of chronic condition, with immigrants from both ES and NES countries less likely to report a chronic condition and having fewer chronic conditions compared with the NB. Immigrants from both ES and NES countries living in Australia for less than 20 years were significantly less likely to report a chronic condition compared with the NB. However, the health of both these groups converged to that of the NB population in terms of reporting a chronic condition after 20 years of stay in Australia.

Existence and global asymptotic stability of positive periodic solution of delayed Cohen-Grossberg neural networks

In this paper, a class of periodic Cohen-Grossberg neural networks with discrete and distributed time-varying delays is considered. By an extension of the Lyapunov-Krasovskii functional method, a novel criterion for the existence and uniqueness and global asymptotic stability of positive periodic solution is derived in terms of M-matrix without any restriction on uniform positiveness of the amplification functions. Comparison and illustrative examples are given to illustrate the effectiveness of the obtained results. © 2014 Elsevier Inc. All rights reserved.

Modelling grouped survival times in toxicological studies using generalized additive models

A method for combining a proportional-hazards survival time model with a bioassay model where the log-hazard function is modelled as a linear or smoothing spline function of log-concentration combined with a smoothing spline function of time is described. The combined model is fitted to mortality numbers, resulting from survival times that are grouped due to a common set of observation times, using Generalized Additive Models (GAMs). The GAM fits mortalities as conditional binomials using an approximation to the log of the integral of the hazard function and is implemented using freely-available, general software for fitting GAMs. Extensions of the GAM are described to allow random effects to be fitted and to allow for time-varying concentrations by replacing time with a calibrated cumulative exposure variable with calibration parameter estimated using profile likelihood. The models are demonstrated using data from a studies of a marine and a, previously published, freshwater taxa. The marine study involved two replicate bioassays of the effect of zinc exposure on survival of an Antarctic amphipod, Orchomenella pinguides. The other example modelled survival of the daphnid, Daphnia magna, exposed to potassium dichromate and was fitted by both the GAM and the process-based DEBtox model. The GAM fitted with a cubic regression spline in time gave a 61 % improvement in fit to the daphnid data compared to DEBtox due to a non-monotonic hazard function. A simulation study using each of these hazard functions as operating models demonstrated that the GAM is overall more accurate in recovering lethal concentration values across the range of forms of the underlying hazard function compared to DEBtox and standard multiple endpoint probit analyses.

EEG data classification using wavelet features selected by Wilcoxon statistics

This paper introduces a method to classify EEG signals using features extracted by an integration of wavelet transform and the nonparametric Wilcoxon test. Orthogonal Haar wavelet coefficients are ranked based on the Wilcoxon test’s statistics. The most prominent discriminant wavelets are assembled to form a feature set that serves as inputs to the naïve Bayes classifier. Two benchmark datasets, named Ia and Ib, downloaded from the brain–computer interface (BCI) competition II are employed for the experiments. Classification performance is evaluated using accuracy, mutual information, Gini coefficient and F-measure. Widely used classifiers, including feedforward neural network, support vector machine, k-nearest neighbours, ensemble learning Adaboost and adaptive neuro-fuzzy inference system, are also implemented for comparisons. The proposed combination of Haar wavelet features and naïve Bayes classifier considerably dominates the competitive classification approaches and outperforms the best performance on the Ia and Ib datasets reported in the BCI competition II. Application of naïve Bayes also provides a low computational cost approach that promotes the implementation of a potential real-time BCI system.

A novel distributed algorithm for power loss minimizing in Smart Grid

Because power generation of renewable resources are unstable and demands of the customers are time-varying, the supply power and demands of the customers are always unequal. To meet the demands of the customers, power is transmitted from primary power generation to secondary power generation. It will cause high power loss. To solve this problem, a distributed algorithm is proposed in this paper. By using the algorithm, the micro-grids are able to exchange power with their neighbors so as to minimize the total power losses of the smart grid. Moreover, communication overhead (bandwidth) is reduced, comparing with centralized algorithm. Through computer simulations, we demonstrate that the proposed algorithm can lead to near-optimal result for alleviating the average power loss per micro-grid and reduce the communication overhead significantly in contrast with the centralized approach.

What determines the yen swap spread?

We investigate if Japanese yen denominated interest rate swap spreads price risks in addition to liquidity and default risk. These additional risks include: the time-varying correlation between interest rates of different types and maturities; business cycle risk; and market skewness risk. Our analysis, over a number of different maturities and sample periods, supports the existence of an additional risk premium. We also show that the time-varying correlation between short term market interest rates (e.g., TIBOR) and the longer term Government bond yield (e.g., Gensaki) is of particular importance. Japanese yen swap spreads are shown to contain both pro-cyclical and counter-cyclical elements of business cycle risk, positive risk premia for skewness risk and variable risk premia for correlation risk (between fixed and floating interest rates).

Discrete Wirtinger-based inequality and its application

In this paper, we derive a new inequality, which encompasses the discrete Jensen inequality. The new inequality is applied to analyze stability of linear discrete systems with an interval time-varying delay and a less conservative stability condition is obtained. Two numerical examples are given to show the effectiveness of the obtained stability condition.

EEG signal classification for BCI applications by wavelets and interval type-2 fuzzy logic systems

The nonlinear, noisy and outlier characteristics of electroencephalography (EEG) signals inspire the employment of fuzzy logic due to its power to handle uncertainty. This paper introduces an approach to classify motor imagery EEG signals using an interval type-2 fuzzy logic system (IT2FLS) in a combination with wavelet transformation. Wavelet coefficients are ranked based on the statistics of the receiver operating characteristic curve criterion. The most informative coefficients serve as inputs to the IT2FLS for the classification task. Two benchmark datasets, named Ia and Ib, downloaded from the brain-computer interface (BCI) competition II, are employed for the experiments. Classification performance is evaluated using accuracy, sensitivity, specificity and F-measure. Widely-used classifiers, including feedforward neural network, support vector machine, k-nearest neighbours, AdaBoost and adaptive neuro-fuzzy inference system, are also implemented for comparisons. The wavelet-IT2FLS method considerably dominates the comparable classifiers on both datasets, and outperforms the best performance on the Ia and Ib datasets reported in the BCI competition II by 1.40% and 2.27% respectively. The proposed approach yields great accuracy and requires low computational cost, which can be applied to a real-time BCI system for motor imagery data analysis.

An analysis of sectoral equity and CDS spreads

In this paper, we find that CDS return shocks are important in explaining the forecast error variance of sectoral equity returns for the USA. The CDS return shocks have different effects on equity returns and return volatility in the pre-crisis and crisis periods. It is the post-Lehman crisis period in which the effects of CDS return shocks are the most dominant. Finally, we construct a spillover index and find that it is time-varying and explains a larger share of total forecast error variance of sectoral equity and CDS returns for some sectors than for others.

Duration of obesity and incident hypertension in adults from the Framingham Heart Study

Background: Previous studies exploring the association between obesity and hypertension generally used a single baseline measurement of obesity. The effect of accumulating excess adiposity over time on the risk of hypertension is uncertain. This study aimed to examine the relationship between duration of obesity and incident hypertension using the Framingham Heart Study.

Methods: Two thousand, nine hundred and fifty-three participants aged 30–62 years without baseline hypertension were included. Blood pressure, height and weight were measured biennially. Duration of obesity was calculated. Time to incident hypertension was analysed using time-varying Cox proportional hazards regression with age as the time scale and censoring at time of death or end of follow-up.

Results: Eighty percent of participants developed hypertension (median follow-up 15.9 years). A positive association between obesity duration and incident hypertension was observed in women. There was no longer an association when time-varying BMI was adjusted for (hazard ratio 0.95; (95% confidence interval 0.85–1.05)).

Conclusion: These findings suggest that the mechanism by which excess adiposity may increase blood pressure is primarily immediate and that long-term exposure to obesity does not further increase the risk of developing hypertension beyond the level of BMI attained.

The economic significance of CDS price discovery

© 2015 Springer Science+Business Media New York Between 2005 and 2009, we document evident time-varying credit risk price discovery between the equity and credit default swap (CDS) markets for 174 US non-financial investment-grade firms. We test the economic significance of a simple portfolio strategy that utilizes fluctuation in CDS spreads as a trading signal to set stock positions, conditional on the CDS price discovery status of the reference entities. We show that a conditional portfolio strategy which updates the list of CDS-influenced firms over time, yields a substantively larger realized return net of transaction cost over the unconditional strategy. Furthermore, the conditional strategy’s Sharpe ratio outperforms a series of benchmark portfolios over the same trading period, including buy-and-hold, momentum and dividend yield strategies.

Mental health following acquisition of disability in adulthood-the impact of wealth

BACKGROUND: Acquisition of a disability in adulthood has been associated with a reduction in mental health. We tested the hypothesis that low wealth prior to disability acquisition is associated with a greater deterioration in mental health than for people with high wealth. METHODS: We assess whether level of wealth prior to disability acquisition modifies this association using 12 waves of data (2001-2012) from the Household, Income and Labour Dynamics in Australia survey-a population-based cohort study of working-age Australians. Eligible participants reported at least two consecutive waves of disability preceded by at least two consecutive waves without disability (1977 participants, 13,518 observations). Fixed-effects linear regression was conducted with a product term between wealth prior to disability (in tertiles) and disability acquisition with the mental health component score of the SF-36 as the outcome. RESULTS: In models adjusted for time-varying confounders, there was evidence of negative effect measure modification by prior wealth of the association between disability acquisition and mental health (interaction term for lowest wealth tertile: -2.2 points, 95% CI -3.1 points, -1.2, p<0.001); low wealth was associated with a greater decline in mental health following disability acquisition (-3.3 points, 95% CI -4.0, -2.5) than high wealth (-1.1 points, 95% CI -1.7, -0.5). CONCLUSION: The findings suggest that low wealth prior to disability acquisition in adulthood results in a greater deterioration in mental health than among those with high wealth.