A multivariate approach to socio-ecological development and endocrine variance in the spotted hyaena,Crocuta crocuta Erxleben

This investigation of the socio-ecological development and endocrine variance in the spotted hyaena,Crocuta crocuta, illustrates that the complexity of a multivariate endocrine system cannot fully be understood using the concept of a class mean (). Any comprehensive investigation of a multivariate endocrine system should also include an analysis of variance (), as it may provide additional insights into the dynamics of an endocrine hypervolume. Mean cortisol concentrations could not differentiate between various social and reproductive categories (MANOVA); however, in males an analysis of variance (principal component analysis) indicated that the contrast between cortisol and androstenedione was the principle axis of variance once the androgen secreting ability had been accounted for. On the other hand, the same contrast was found to be the principal axis of variance in the female sub-sample, suggesting that cortisol may play a significant role in regulating the endocrine dynamics of this species, despite showing little variance in mean values among various social and reproductive categories.     

On the Modelling and Forecasting of Multivariate Realized Volatility: Generalized Heterogeneous Autoregressive (GHAR) Model

Recent multivariate extensions of the popular heterogeneous autoregressive model (HAR) for realized volatility leave substantial information unmodelled in residuals. We propose to employ a system of seemingly unrelated regressions to model and forecast a realized covariance matrix to capture this information. We find that the newly proposed generalized heterogeneous autoregressive (GHAR) model outperforms competing approaches in terms of economic gains, providing better mean–variance trade‐off, while, in terms of statistical precision, GHAR is not substantially dominated by any other model. Our results provide a comprehensive comparison of the performance when realized covariance, subsampled realized covariance and multivariate realized kernel estimators are used. We study the contribution of the estimators across different sampling frequencies, and show that the multivariate realized kernel and subsampled realized covariance estimators deliver further gains compared to realized covariance estimated on a 5‐minute frequency. In order to show economic and statistical gains, a portfolio of various sizes is used. Copyright © 2016 John Wiley & Sons, Ltd.     

Variance in clutch size

Summary Most studies of the evolution of clutch size have concentrated on changes in the mean and have neglected the variance of clutch size. We suggest that the variance is very important and discuss how it might be fruitfully analyzed.Acknowledgment. I thank the Royal Australasian Ornithologists Union and the North American Nest Record Card Program for access to records of shorebird reproductive performance and Dr P. A. Baghurst for considerable assistance.     

The Importance of the Macroeconomic Variables in Forecasting Stock Return Variance: A GARCH‐MIDAS Approach

This paper applies the GARCH‐MIDAS (mixed data sampling) model to examine whether information contained in macroeconomic variables can help to predict short‐term and long‐term components of the return variance. A principal component analysis is used to incorporate the information contained in different variables. Our results show that including low‐frequency macroeconomic information in the GARCH‐MIDAS model improves the prediction ability of the model, particularly for the long‐term variance component. Moreover, the GARCH‐MIDAS model augmented with the first principal component outperforms all other specifications, indicating that the constructed principal component can be considered as a good proxy of the business cycle. Copyright © 2013 John Wiley & Sons, Ltd.     

The co-discovery of conservation laws and particle families

This paper presents an epistemological analysis of the search for new conservation laws in particle physics that was especially prominent in the 1950s and 1960s. Discovering conservation laws has posed various challenges concerning the underdetermination of theory by evidence, to which physicists have found various responses. These responses include an appeal to a plenitude principle, a maxim for inductive inference, looking for a parsimonious system of generalizations, and unifying particle ontology and particle dynamics. The connection between conservation laws and ontological categories is a major theme in my analysis: While there are infinitely many conservation law theories that are empirically equivalent to the laws physicists adopted for the fundamental standard model of particle physics, I show that the standard family laws are the only ones that determine and are determined by the simplest division of particles into families.     

Forecasting of dependence,market, and investment risks of a global index portfolio

This paper undertakes an in-sample and rolling-window comparative analysis of dependence, market, and portfolio investment risks on a 10-year global index portfolio of developed, emerging, and commodity markets. We draw our empirical results by fitting vine copulas (e.g., r-vines, c-vines, d-vines), IGARCH(1,1) RiskMetrics value-at-risk (VaR), and portfolio optimization methods based on risk measures such as the variance, conditional value-at-risk, conditional drawdown-at-risk, minimizing regret (Minimax), and mean absolute deviation. The empirical results indicate that all international indices tend to correlate strongly in the negative tail of the return distribution; however, emerging markets, relative to developed and commodity markets, exhibit greater dependence, market, and portfolio investment risks. The portfolio optimization shows a clear preference towards the gold commodity for investment, while Japan and Canada are found to have the highest and lowest market risk, respectively. The vine copula analysis identifies symmetry in the dependence dynamics of the global index portfolio modeled. Large VaR diversification benefits are produced at the 95% and 99% confidence levels by the modeled international index portfolio. The empirical results may appeal to international portfolio investors and risk managers for advanced portfolio management, hedging, and risk forecasting.     

Modeling interval trendlines: Symbolic singular spectrum analysis for interval time series

In this article we propose an extension of singular spectrum analysis for interval-valued time series. The proposed methods can be used to decompose and forecast the dynamics governing a set-valued stochastic process. The resulting components on which the interval time series is decomposed can be understood as interval trendlines, cycles, or noise. Forecasting can be conducted through a linear recurrent method, and we devised generalizations of the decomposition method for the multivariate setting. The performance of the proposed methods is showcased in a simulation study. We apply the proposed methods so to track the dynamics governing the Argentina Stock Market (MERVAL) in real time, in a case study over a period of turbulence that led to discussions of the government of Argentina with the International Monetary Fund.     

A Time‐Simultaneous Prediction Box for a Multivariate Time Series

A sample‐based method in Kolsrud (Journal of Forecasting 2007; 26 (3): 171–188) for the construction of a time‐simultaneous prediction band for a univariate time series is extended to produce a variable‐ and time‐simultaneous prediction box for a multivariate time series. A measure of distance based on the L_∞ ‐norm is applied to a learning sample of multivariate time trajectories, which can be mean‐ and/or variance‐nonstationary. Based on the ranking of distances to the centre of the sample, a subsample of the most central multivariate trajectories is selected. A prediction box is constructed by circumscribing the subsample with a hyperrectangle. The fraction of central trajectories selected into the subsample can be calibrated by bootstrap such that the expected coverage of the box equals a prescribed nominal level. The method is related to the concept of data depth, and thence modified to increase coverage. Applications to simulated and empirical data illustrate the method, which is also compared to several other methods in the literature adapted to the multivariate setting. Copyright © 2015 John Wiley & Sons, Ltd.     

Bombesin,calcitonin and leu-enkephalin immunoreactivity in endocrine cells of human lung

Summary By immunohistochemistry, bombesin, calcitonin and leu-enkephalin was localized in endocrine cells of human lungs from various age groups. It is suggested that at least 3 different peptide containing endocrine cells may be present in human lung.Acknowledgments. The authors thank Dr Camille Vaillant, University of Liverpool, for advice with immunohistochemical methods and Dr G. Dockray for his generous gift of bombesin antiserum. This work has been supported by Medical Research Council of Canada (MA-7054) and Ontario Thoracic Society.     

Scalar BEKK and indirect DCC

The paper derives the scalar special case of the well‐known BEKK multivariate GARCH model using a multivariate extension of the random coefficient autoregressive (RCA) model. This representation establishes the relevant structural and asymptotic properties of the scalar BEKK model using the theoretical results available in the literature for general multivariate GARCH. Sufficient conditions for the (direct) DCC model to be consistent with a scalar BEKK representation are established. Moreover, an indirect DCC model that is consistent with the scalar BEKK representation is obtained, and is compared with the direct DCC model using an empirical example. The paper shows, within an asset allocation and risk measurement framework, that the two models are similar in terms of providing parameter estimates and forecasting value‐at‐risk thresholds for equally weighted and minimum variance portfolios. Copyright © 2008 John Wiley & Sons, Ltd.     

Prolactin, growth hormone and the immune system in humans

Prolactin (PRL) and growth hormone (GH) qualify as lymphoid growth and differentiation factors. Yet, long-standing hyper- or hyposecretion of PRL or GH does not induce any significant alteration of the immune system. Subclinical changes in laboratory parameters (such as chemotaxis or phagocytosis by granulocytes or macrophages or natural killer cell activity) have been reported in some of these conditions. The GH-insulin-like growth factor (IGF)-I axis is dysregulated in ageing, in catabolic states and in critical illness. Immune parameters, such as infection rate, are being monitored during clinical trials with GH or IGF-I. Hyperprolactinaemia may play an aggravating role in systemic lupus erythematosus, in autoimmune thyroiditis and in other autoimmune diseases. The patient may benefit from increased alertness about interactions between the endocrine and immune systems.     

Endocrine and environmental aspects of sex differentiation in fish

This paper reviews the current knowledge concerning the endocrine and environmental regulations of both gonadal sex differentiation in gonochoristic and sex inversion in hermaphroditic fish. Within the central nervous system, gonadotropins seem to play a role in triggering sex inversion in hermaphroditic fish. In gonochorists, although potentially active around this period, the hypothalamo-pituitary axis is probably not involved in triggering sex differentiation. Although steroids and steroidogenic enzymes are probably not the initial triggers of sex differentiation, new data, including molecular approaches, have confirmed that they are key physiological steps in the regulation of this process. Environmental factors can strongly influence sex differentiation and sex inversion in gonochoristic and hermaphroditic fish, respectively. The most important environmental determinant of sex would appear to be temperature in the former species, and social factors in the latter. Interactions between environmental factors and genotype have been suggested for both gonochoristic and hermaphroditic fish.     

Predictable Return Distributions

Using quantile regression this paper explores the predictability of the stock and bond return distributions as a function of economic state variables. The use of quantile regression allows us to examine specific parts of the return distribution such as the tails and the center, and for a sufficiently fine grid of quantiles we can trace out the entire distribution. A univariate quantile regression model is used to examine the marginal stock and bond return distributions, while a multivariate model is used to capture their joint distribution. An empirical analysis on US data shows that economic state variables predict the stock and bond return distributions in quite different ways in terms of, for example, location shifts, volatility and skewness. Comparing the different economic state variables in terms of their out‐of‐sample forecasting performance, the empirical analysis also shows that the relative accuracy of the state variables varies across the return distribution. Density forecasts based on an assumed normal distribution with forecasted mean and variance is compared to forecasts based on quantile estimates and, in general, the latter yields the best performance. Copyright © 2015 John Wiley & Sons, Ltd.     

Genetic determinants of individual differences in avoidance learning: behavioral and endocrine characteristics

Bidirectional genetic selection for good and poor active avoidance learning in a shuttle box has been carried out in three independent laboratories using remarkably similar discrete-trial training procedures. The resulting strains are known as the Roman High and Low Avoidance (RHA and RLA), the Syracuse High and Low Avoidance (SHA and SLA) and the Australian High and Low Avoidance (AHA and ALA) strains, respectively. An additional unidirectionally selected strain, known as the Tokai High Avoider (THA) strain was developed in Japan using a free-operant Sidman avoidance procedure in a Skinner box. This paper reviews the selection of the Syracuse strains, enumerates the various behavioral and endocrine characteristics of the strains, and compares them to the other similarly selected strains. The behavioral work suggests that genetic selection from diverse breeding stocks has resulted in common characteristics that differentiate the strains in the emotional, not learning, domain. The endocrine data, however, are somewhat at odds. The Syracuse strains differentiate one way with respect to endocrine function, and the Roman strains differentiate in the opposite way. We suggest, therefore, that the endocrine correlates are not tightly linked to the avoidance genotype. Genetic analysis of all of the selected strains for both the avoidance phenotype and the endocrine correlates will be needed to test this hypothesis.     

Forecasting the Yield Curve in a Data‐Rich Environment Using the Factor‐Augmented Nelson–Siegel Model

This paper compares various ways of extracting macroeconomic information from a data‐rich environment for forecasting the yield curve using the Nelson–Siegel model. Five issues in extracting factors from a large panel of macro variables are addressed; namely, selection of a subset of the available information, incorporation of the forecast objective in constructing factors, specification of a multivariate forecast objective, data grouping before constructing factors, and selection of the number of factors in a data‐driven way. Our empirical results show that each of these features helps to improve forecast accuracy, especially for the shortest and longest maturities. Factor‐augmented methods perform well in relatively volatile periods, including the crisis period in 2008–9, when simpler models do not suffice. The macroeconomic information is exploited best by partial least squares methods, with principal component methods ranking second best. Reductions of mean squared prediction errors of 20–30% are attained, compared to the Nelson–Siegel model without macro factors. Copyright © 2011 John Wiley & Sons, Ltd.     

Modeling and forecasting aggregate stock market volatility in unstable environments using mixture innovation regressions

We perform Bayesian model averaging across different regressions selected from a set of predictors that includes lags of realized volatility, financial and macroeconomic variables. In our model average, we entertain different channels of instability by either incorporating breaks in the regression coefficients of each individual model within our model average, breaks in the conditional error variance, or both. Changes in these parameters are driven by mixture distributions for state innovations (MIA) of linear Gaussian state‐space models. This framework allows us to compare models that assume small and frequent as well as models that assume large but rare changes in the conditional mean and variance parameters. Results using S&P 500 monthly and quarterly realized volatility data from 1960 to 2014 suggest that Bayesian model averaging in combination with breaks in the regression coefficients and the error variance through MIA dynamics generates statistically significantly more accurate forecasts than the benchmark autoregressive model. However, compared to a MIA autoregression with breaks in the regression coefficients and the error variance, we fail to provide any drastic improvements.     

Genetic determinants of individual differences in avoidance learning: Behavioral and endocrine characteristics

Bidirectional genetic selection for good and poor active avoidance learning in a shuttle box has been carried out in three independent laboratories using remarkably similar discrete-trial training procedures. The resulting strains are known as the Roman High and Low Avoidance (RHA and RLA), the Syracuse High and Low Avoidance (SHA and SLA) and the Australian High and Low Avoidance (AHA and ALA) strains, respectively. An additional unidirectionally selected strain, known as the Tokai High Avoider (THA) strain was developed in Japan using a free-operant Sidman avoidance procedure in a Skinner box. This paper reviews the selection of the Syracuse strains, enumerates the various behavioral and endocrine characteristics of the strains, and compares them to the other similarly selected strains. The behavioral work suggests that genetic selection from diverse breeding stocks has resulted in common characteristics that differentiate the strains in the emotional, not learning, domain. The endocrine data, however, are somewhat at odds. The Syracuse strains differentiate one way with respect to endocrine function, and the Roman strains differentiate in the opposite way. We suggest, therefore, that the endocrine correlates are not tightly linked to the avoidance genotype. Genetic analysis of all of the selected strains for both the avoidance phenotype and the endocrine correlates will be needed to test this hypothesis.Preparation of this paper was supported by research grant MH-39230-3 from the National Institute of Mental Health.     

Regulation of B and T cell development by anterior pituitary hormones

Hormones produced by the anterior pituitary gland have been implicated in the regulation of primary lymphocyte development. In order to identify endocrine factors involved in that process, several strains of mice with genetic defects resulting in a selective impairment in the production of one or more anterior pituitary-derived hormones have been analysed. This study has resulted in the classification of endocrine hormones into the following four categories (i) hormones such as prolactin with no apparent effects on primary lymphopoiesis; (ii) anabolic hormones such as growth hormone and insulin-like growth factor-I whose stimulatory effects on primary lymphopoiesis are non-lineage-specific and related to their actions as systemic mediators of growth and/or differentiation; (iii) hormones such as thyroid hormones that have an obligate role in primary B lymphopoiesis; and (iv) hormones such as oestrogens that act as negative regulators of lymphopoiesis.     

A threshold stochastic volatility model

This article introduces a new model to capture simultaneously the mean and variance asymmetries in time series. Threshold non‐linearity is incorporated into the mean and variance specifications of a stochastic volatility model. Bayesian methods are adopted for parameter estimation. Forecasts of volatility and Value‐at‐Risk can also be obtained by sampling from suitable predictive distributions. Simulations demonstrate that the apparent variance asymmetry documented in the literature can be due to the neglect of mean asymmetry. Strong evidence of the mean and variance asymmetries was detected in US and Hong Kong data. Asymmetry in the variance persistence was also discovered in the Hong Kong stock market. Copyright © 2002 John Wiley & Sons, Ltd.     

巷道布置对围岩稳定性影响的数值模拟

采用理论分析不同应力场下巷道轴线与最大水平主应力方向夹角的最佳角度,利用数值模拟不同夹角情况下,巷道围岩应力分布、正前方应力分布及变形特征。得出：1）最大水平主应力σH〉最小水平主应力σh〉垂直主应力σr时,最佳角度为0°;σt〉σH〉σh时,最佳角度为90°;σH〉σa〉σA时,最佳夹角为α0.2）巷道开挖后,水平应力在两帮出现应力降低区,在顸、底板出现应力集中区;垂直应力在两帮出现应力集中区,在顶、底板出现应力降低区;随角度变化,应力变化区均有明显变化,正前方断面的最大主应力变化明显。3）巷道达到最佳角度时,围岩变形相对较小,最大主应力围绕在巷道周边,呈环状或类环状分布;其它角度时,围岩变形相对较大,最大主应力逐渐向顶、底板的四角或两帮发展。