Survival of adult,territorial Longtailed Wagtails Motacilla clara : The effects of environmental factors and individual covariates

The Longtailed Wagtail is a non-migratory African passerine that is confined exclusively to small, fast-flowing rivers in a largely arboreal environment. The breeding adults hold permanent, life-long, linear territories in their riverine habitat and this makes it easy to locate colour-marked birds. They are confiding by nature and permit close approach, often to less than 10 m, and this allows their unique permutations of colourrings to be read. Using data from the 21 year period, 1 August 1978 to 31 July 1999, of a dozen territories it has been shown that the breeding territories have not changed at all, even though there has been a continual, but slow turnover of territory holders. A total of 109 territorial adult birds were monitored for a total of 1121 bird-quarters and survival was estimated for each of four quarters in a year. The average survival rate is estimated at 68.8% yr -1 (95% confidence limits: 63.3% to 69.3%) and this is high for such a small bird (approximately 20 g) and there have been some remarkably long-lived individuals, e.g. 10 to 12 years. In this paper, a generalized linear model is built of the survival of territorial adults. It is shown that bigger birds have a higher survival rate and that there are seasonal differences in survival that are ascribable to the cost of breeding and possibly cost of moult. There is an underlying long-term quadratic trend in survival that is related to increasing environmental degradation and decreasing chemical pollution.     

A variable selection approach to monotonic regression with Bernstein polynomials

One of the standard problems in statistics consists of determining the relationship between a response variable and a single predictor variable through a regression function. Background scientific knowledge is often available that suggests that the regression function should have a certain shape (e.g. monotonically increasing or concave) but not necessarily a specific parametric form. Bernstein polynomials have been used to impose certain shape restrictions on regression functions. The Bernstein polynomials are known to provide a smooth estimate over equidistant knots. Bernstein polynomials are used in this paper due to their ease of implementation, continuous differentiability, and theoretical properties. In this work, we demonstrate a connection between the monotonic regression problem and the variable selection problem in the linear model. We develop a Bayesian procedure for fitting the monotonic regression model by adapting currently available variable selection procedures. We demonstrate the effectiveness of our method through simulations and the analysis of real data.     

Robustness of the EWMA control chart for individual observations

The traditional exponentially weighted moving average (EWMA) chart is one of the most popular control charts used in practice today. The in-control robustness is the key to the proper design and implementation of any control chart, lack of which can render its out-of-control shift detection capability almost meaningless. To this end, Borror et al. [5 Borror, C. M., Montgomery, D. C. and Runger, G. C. 1999. Robustness of the EWMA control chart to non-normality. J. Qual. Technol., 31(3): 309–316. [Taylor & Francis Online], [Web of Science ®] [Google Scholar]] studied the performance of the traditional EWMA chart for the mean for i.i.d. data. We use a more extensive simulation study to further investigate the in-control robustness (to non-normality) of the three different EWMA designs studied by Borror et al. [5 Borror, C. M., Montgomery, D. C. and Runger, G. C. 1999. Robustness of the EWMA control chart to non-normality. J. Qual. Technol., 31(3): 309–316. [Taylor & Francis Online], [Web of Science ®] [Google Scholar]]. Our study includes a much wider collection of non-normal distributions including light- and heavy-tailed and symmetric and asymmetric bi-modal as well as the contaminated normal, which is particularly useful to study the effects of outliers. Also, we consider two separate cases: (i) when the process mean and standard deviation are both known and (ii) when they are both unknown and estimated from an in-control Phase I sample. In addition, unlike in the study done by Borror et al. [5 Borror, C. M., Montgomery, D. C. and Runger, G. C. 1999. Robustness of the EWMA control chart to non-normality. J. Qual. Technol., 31(3): 309–316. [Taylor & Francis Online], [Web of Science ®] [Google Scholar]], the average run-length (ARL) is not used as the sole performance measure in our study, we consider the standard deviation of the run-length (SDRL), the median run-length (MDRL), and the first and the third quartiles as well as the first and the 99th percentiles of the in-control run-length distribution for a better overall assessment of the traditional EWMA chart's in-control performance. Our findings sound a cautionary note to the (over) use of the EWMA chart in practice, at least with some types of non-normal data. A summary and recommendations are provided.     

Robust estimation of AR coefficients under simultaneously influencing outliers and missing values

A family of robust estimators for coefficients of Gaussian AR(p) time series under simultaneously influencing distortions of two types: outliers and missing values, is proposed. The estimators are based on special properties of the Cauchy probability distribution; consistency and the asymptotic normality of these estimators are proven. An approximate solution of the problem of minimization of the asymptotic variance within the proposed family of estimators is found. Performance of the proposed estimators is illustrated for simulated time series and for real data sets.     

Detection of structural breaks in a time-varying heteroskedastic regression model

A Bayesian method for estimating a time-varying regression model subject to the presence of structural breaks is proposed. Heteroskedastic dynamics, via both GARCH and stochastic volatility specifications, and an autoregressive factor, subject to breaks, are added to generalize the standard return prediction model, in order to efficiently estimate and examine the relationship and how it changes over time. A Bayesian computational method is employed to identify the locations of structural breaks, and for estimation and inference, simultaneously accounting for heteroskedasticity and autocorrelation. The proposed methods are illustrated using simulated data. Then, an empirical study of the Taiwan and Hong Kong stock markets, using oil and gas price returns as a state variable, provides strong support for oil prices being an important explanatory variable for stock returns.     

Reduction of bias and skewness with applications to second order accuracy

Suppose [^(q)]{\widehat{\theta}} is an estimator of θ in \mathbbR{\mathbb{R}} that satisfies the central limit theorem. In general, inferences on θ are based on the central limit approximation. These have error O(n ^−1/2), where n is the sample size. Many unsuccessful attempts have been made at finding transformations which reduce this error to O(n ⁻¹). The variance stabilizing transformation fails to achieve this. We give alternative transformations that have bias O(n ⁻²), and skewness O(n ⁻³). Examples include the binomial, Poisson, chi-square and hypergeometric distributions.     

Poisson regression with misclassified counts: application to cervical cancer

"Errors in disease classification can give misleading inferences for covariate effects when the probability of error is itself related to the covariates. More accurate inferences are possible using supplemental data on both true and fallible disease counts at various covariate levels. We present a method for incorporating such supplemental data into disease rate regression and use it to show that, although observed intercountry differences in European cervical cancer mortality rates are exaggerated by errors in death certification, they are not completely explained by such errors." The data concern Belgium, England and Wales, France, and Italy.     

A divergence test for autoregressive time series models

In this paper, we study the normality test for the innovations of unstable autoregressive models based on the divergence test. In order to investigate the asymptotic behavior of the tests, we use the link between the divergence test and the residual empirical process. Simulation results are provided for illustration.     

Health care futures. Part 2: The future of physician executives? Panel discussion

What is the future of health care in America? This is Part 2 of The Physician Executive panel discussion that explores the future of health care in America. To narrow this ambitious focus somewhat, the future is defined as five to 10 years hence. In Part 1, which was published in the May/June issue, Russell C. Coile, Jr., Barbara LeTourneau, MD, MBA, FACPE, James Reinertsen, MD, Uwe Reinhardt, PhD, Marshall Ruffin, MD, MPH, MBA, FACPE, and David Vogel, MS, shared their opinions about what the future holds in managed care, information technology, and biotechnology. In Part 2, Susan Cejka, Barbara LeTourneau, MD, MBA, FACPE, John Henry Pfifferling, PhD, Uwe Reinhardt, PhD, and James Todd, MD, share their views on the future of medical education and physician executives.     

Perception of Ecological Risk to Water Environments

This paper examines lay and expert perceptions of the ecological risks associated with a range of human activities that could adversely affect water resource environments. It employs the psychometric paradigm pioneered in characterizing perceptions of human health risks, which involves surveys to obtain judgments from subjects about risk items in terms of several important characteristics of the risks. The paper builds on a previous study that introduced ecological risk perception. This second study employs a larger, more diverse sample, a more focused topic area, and comparisons between lay and expert judgments. The results confirm that a small set of underlying factors explain a great deal of variability in lay judgments about ecological risks. These have been termed Ecological Impact, Human Benefits, Controllability , and Knowledge. The results are useful in explaining subjects' judgments of the general riskiness of, and need for regulation of, various risk items. The results also indicate several differences and areas of agreement among the lay and expert samples that point to potential key issues in future ecological risk management efforts for water resources.