Contour projected dimension reduction

In regression analysis, we employ contour projection (CP) to develop a new dimension reduction theory. Accordingly, we introduce the notions of the central contour subspace and generalized contour subspace. We show that both of their structural dimensions are no larger than that of the central subspace Cook [Regression Graphics (1998b) Wiley]. Furthermore, we employ CP-sliced inverse regression, CP-sliced average variance estimation and CP-directional regression to estimate the generalized contour subspace, and we subsequently obtain their theoretical properties. Monte Carlo studies demonstrate that the three CP-based dimension reduction methods outperform their corresponding non-CP approaches when the predictors have heavy-tailed elliptical distributions. An empirical example is also presented to illustrate the usefulness of the CP method.Comment: Published in at http://dx.doi.org/10.1214/08-AOS679 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Quantile correlations and quantile autoregressive modeling

In this paper, we propose two important measures, quantile correlation (QCOR) and quantile partial correlation (QPCOR). We then apply them to quantile autoregressive (QAR) models, and introduce two valuable quantities, the quantile autocorrelation function (QACF) and the quantile partial autocorrelation function (QPACF). This allows us to extend the classical Box-Jenkins approach to quantile autoregressive models. Specifically, the QPACF of an observed time series can be employed to identify the autoregressive order, while the QACF of residuals obtained from the fitted model can be used to assess the model adequacy. We not only demonstrate the asymptotic properties of QCOR, QPCOR, QACF, and PQACF, but also show the large sample results of the QAR estimates and the quantile version of the Ljung-Box test. Simulation studies indicate that the proposed methods perform well in finite samples, and an empirical example is presented to illustrate usefulness

Markov-Switching Model Selection Using Kullback-Leibler Divergence

In Markov-switching regression models, we use Kullback-Leibler (KL) divergence between the true and candidate models to select the number of states and variables simultaneously. In applying Akaike information criterion (AIC), which is an estimate of KL divergence, we find that AIC retains too many states and variables in the model. Hence, we derive a new information criterion, Markov switching criterion (MSC), which yields a marked improvement in state determination and variable selection because it imposes an appropriate penalty to mitigate the over-retention of states in the Markov chain. MSC performs well in Monte Carlo studies with single and multiple states, small and large samples, and low and high noise. Furthermore, it not only applies to Markov-switching regression models, but also performs well in Markov- switching autoregression models. Finally, the usefulness of MSC is illustrated via applications to the U.S. business cycle and the effectiveness of media advertising.Research Methods/ Statistical Methods,

Score Tests for the Single Index Model

The single index model is a generalization of the linear regression model with E(y|x) = g, where g is an unknown function. The model provides a flexible alternative to the linear regression model while providing more structure than a fully nonparametric approach. Although the fitting of single index models does not require distributional assumptions on the error term, the properties of the estimates depend on such assumptions, as does practical application of the model. In this article score tests are derived for three potential misspecifications of the single index model: heteroscedasticity in the errors, autocorrelation in the errors, and the omission of an important variable in the linear index. These tests have a similar structure to corresponding tests for nonlinear regression models. Monte Carlo simulations demonstrate that the first two tests hold their nominal size well and have good power properties in identifying model violations, often outperforming other tests. Testing for the need for additional covariates can be effective, but is more difficult. The score tests are applied to three real datasets, demonstrating that the tests can identify important model violations that affect inference, and that approaches that do not take model misspecifications into account can lead to very different results.Statistics Working Papers Serie

Semiparametric and Additive Model Selection Using an Improved Akaike Information Criterion

An improved AIC-based criterion is derived for model selection in general smoothing-based modeling, including semiparametric models and additive models. Examples are provided of applications to goodness-of-fit, smoothing parameter and variable selection in an additive model and semiparametric models, and variable selection in a model with a nonlinear function of linear terms.Statistics Working Papers Serie

DNA Damage and Its Links to Neurodegeneration

The integrity of our genetic material is under constant attack from numerous endogenous and exogenous agents. The consequences of a defective DNA damage response are well studied in proliferating cells, especially with regards to the development of cancer, yet its precise roles in the nervous system are relatively poorly understood. Here we attempt to provide a comprehensive overview of the consequences of genomic instability in the nervous system. We highlight the neuropathology of congenital syndromes that result from mutations in DNA repair factors and underscore the importance of the DNA damage response in neural development. In addition, we describe the findings of recent studies, which reveal that a robust DNA damage response is also intimately connected to aging and the manifestation of age-related neurodegenerative disorders such as Alzheimer's disease and amyotrophic lateral sclerosis. Video Abstract: In this Review, Madabhushi etal. summarize the current state of knowledge about how DNA damage and changes to the DNA damage response in neurons might underlie neurodegenerative diseases

Chromatin Regulation of DNA Damage Repair and Genome Integrity in the Central Nervous System

With the continued extension of lifespan, aging and age-related diseases have become a major medical challenge to our society. Aging is accompanied by changes in multiple systems. Among these, the aging process in the central nervous system is critically important but very poorly understood. Neurons, as post-mitotic cells, are devoid of replicative associated aging processes, such as senescence and telomere shortening. However, because of the inability to self-replenish, neurons have to withstand challenge from numerous stressors over their lifetime. Many of these stressors can lead to damage of the neurons' DNA. When the accumulation of DNA damage exceeds a neuron's capacity for repair, or when there are deficiencies in DNA repair machinery, genome instability can manifest. The increased mutation load associated with genome instability can lead to neuronal dysfunction and ultimately to neuron degeneration. In this review, we first briefly introduce the sources and types of DNA damage and the relevant repair pathways in the nervous system (summarized in Fig. 1). We then discuss the chromatin regulation of these processes and summarize our understanding of the contribution of genomic instability to neurodegenerative diseases. Abbreviations DDRDNA damage response NHEJnonhomologous end joining HRhomologous recombination BERbase excision repair NERnucleotide excision repair SSBsingle-strand break SSBRsingle-strand break repair DSBRdouble-strand break repair DSBdouble-strand break mtDNAmitochondrial DNA PARpoly(ADP-ribose) HAThistone acetyltransferase HDAChistone deacetylase ATMataxia telangiectasia mutated MMRmismatch repair CNVcopy number variation iPSCinduced pluripotent stem cell HDHuntington's disease ADAlzheimer's disease PDParkinson's disease ALSamyotrophic lateral sclerosis Keywords neurodegenerative diseases DNA repair DNA damage response histone modifications central nervous syste

Prevalence and Correlates of Depression among Chronic Kidney Disease Patients in Taiwan

Background: Chronic kidney disease (CKD) is a progressive disease that causes a permanent impairment of renal function and premature mortality. The associated prognosis may result in serious psychological distress to the affected individual. However, there are limited data on the psychological correlates, and in particular depression, in Chinese CKD patients. This study aimed to examine the prevalence of depression, as well as the influence of other psychosocial factors on depression, among Taiwanese CKD patients. Methods: We used a cross-sectional research design to recruit 270 CKD patients who were not undergoing dialysis treatment at a hospital in southern Taiwan during 2011. The structured questionnaire used in this study gathered information on respondent demographic and disease characteristics, and information obtained from the Taiwanese Depression Questionnaire. Factors associated with depression were examined by a multiple logistic regression analysis. Results: The crude and age-standardized prevalence of depression were 22.6% and 20.6%, respectively. Those who had sleep disturbances, reported having no religious beliefs, followed no regular exercise regimen, and were diagnosed with stage III or above CKD demonstrated a significantly higher risk of depression. Conclusion: Our findings are beneficial to healthcare providers, as they identify both the prevalence of depression and several of its correlates. By identifying CKD patients with a higher risk of depression, healthcare providers may be better able to ensure the provision of appropriate rehabilitation to this population