Robust nonparametric regression in time series

AbstractConsider a stationary time series (Xt, Yt), t = 0, ±1, … with Xt being Rd-valued and Yt real-valued. Let ψ(·) denote a monotone function and let θ(·) denote the robust conditional location functional so that E[ψ(Y0 − θ(X0))|X0] = 0. Given a finite realization (X1, Y1), …, (Xn, Yn), the problem of estimating θ(·) is considered. Under appropriate regularity conditions, it is shown that a sequence of the robust conditional location functional estimators can be chosen to achieve the optimal rate of convergence n−1(2 + d) both pointwise and in Lq (1 ≤ q < ∞) norms restricted to a compact; it can also be chosen to achieve the optimal rate of convergence (n−1 log(n))1(2 + d) in L∞ norm restricted to a compact

Nonparametric Independent Component Analysis for the Sources with Mixed Spectra

Independent component analysis (ICA) is a blind source separation method to recover source signals of interest from their mixtures. Most existing ICA procedures assume independent sampling. Second-order-statistics-based source separation methods have been developed based on parametric time series models for the mixtures from the autocorrelated sources. However, the second-order-statistics-based methods cannot separate the sources accurately when the sources have temporal autocorrelations with mixed spectra. To address this issue, we propose a new ICA method by estimating spectral density functions and line spectra of the source signals using cubic splines and indicator functions, respectively. The mixed spectra and the mixing matrix are estimated by maximizing the Whittle likelihood function. We illustrate the performance of the proposed method through simulation experiments and an EEG data application. The numerical results indicate that our approach outperforms existing ICA methods, including SOBI algorithms. In addition, we investigate the asymptotic behavior of the proposed method.Comment: 27 pages, 10 figure

Renal cell carcinoma: a nomogram for the CT imaging-inclusive prediction of indolent, non-clear cell renal cortical tumours

Aim To develop a nomogram from clinical and computed tomography (CT) data for pre-treatment identification of indolent renal cortical tumours. Patients and methods A total of 1201 consecutive patients underwent dedicated contrast-enhanced CT prior to nephrectomy for a renal cortical tumour between January 2000 and July 2011. Two radiologists evaluated all tumours on CT for size, necrosis, calcification, contour, renal vein invasion, collecting system invasion, contact with renal sinus fat, multicystic tumour architecture, nodular enhancement, and the degree of nephrographic phase enhancement. CT and clinical predictors (gender, body mass index [BMI], age) were incorporated into the nomogram. We employed multivariable logistic regression analysis to predict tumour type and internally validated the final model using the data from reader 1. External validation was performed by using all data from reader 2. We applied Wilcoxon rank sum test and Fisher's exact test to investigate for differences in tumour size, BMI, age, and differences in CT imaging features between patients with aggressive and those with indolent tumours. Results 63.6% (764/1201) of patients had clear-cell or other aggressive non-clear-cell RCC (i.e. papillary RCC type 2, unclassified RCC) and 36.4% (437/1201) had indolent renal cortical tumours (i.e. papillary RCC type 1, chromophobe RCC, angiomyolipoma, or oncocytoma). On CT, indolent tumours were significantly smaller (p &lt; 0.001) than aggressive tumours and significantly associated with well-defined tumour contours (p &lt; 0.001). Aggressive RCC were significantly associated with necrosis, calcification, renal vein invasion, collecting system invasion, contact with renal sinus fat, multicystic tumour architecture, and nodular enhancement (all, p &lt; 0.001). The nomogram's concordance index (C-index) was 0.823 after internal and 0.829 after external validation. Concluding statement We present a nomogram based on 1201 patients combining CT features with clinical data for the prediction of indolent renal cortical tumours. When externally validated, this nomogram resulted in a C-index of 0.829

Novel Characterization and Live Imaging of Schlemm's Canal Expressing Prox-1

Schlemm's canal is an important structure of the conventional aqueous humor outflow pathway and is critically involved in regulating the intraocular pressure. In this study, we report a novel finding that prospero homeobox protein 1 (Prox-1), the master control gene for lymphatic development, is expressed in Schlemm's canal. Moreover, we provide a novel in vivo method of visualizing Schlemm's canal using a transgenic mouse model of Prox-1-green fluorescent protein (GFP). The anatomical location of Prox-1+ Schlemm's canal was further confirmed by in vivo gonioscopic examination and ex vivo immunohistochemical analysis. Additionally, we show that the Schlemm's canal is distinguishable from typical lymphatic vessels by lack of lymphatic vessel endothelial hyaluronan receptor (LYVE-1) expression and absence of apparent sprouting reaction when inflammatory lymphangiogenesis occurred in the cornea. Taken together, our findings offer new insights into Schlemm's canal and provide a new experimental model for live imaging of this critical structure to help further our understanding of the aqueous humor outflow. This may lead to new avenues toward the development of novel therapeutic intervention for relevant diseases, most notably glaucoma

Quantitative speech production profiles and focal left hemisphere lesion

The clinical differentiation between apraxia of speech (AOS) and aphasia with phonemic paraphasia is based on impressionistic consideration of a varying list of speech properties. The diagnosing clinician is challenged with determining the presence or absence of these disorders by considering the extent to which characteristic features are evident in the speech output and by determining how much relative weight to assign to each. Predictably, the subjective nature of the diagnostic process can translate to limited agreement, even among experienced clinicians (Haley, Jacks, de Riesthal, Abou-Khalil, & Roth, 2012), and the risks of misdiagnosis and diagnostic uncertainty are substantial (Wambaugh, 2006). Additionally it is likely that the adherence to a strictly dichotomous classification system overlooks theoretically and clinically important heterogeneity. The purpose of this study was to identify a preliminary set of speech production profiles based on naturally occurring variations in individuals with acquired focal brain lesions. To avoid classification circularity, assessments were conducted without consideration of clinical speech diagnosis, and metrics were selected to represent diverse and robust observations about speech properties associated with left hemisphere lesions

School Entry Requirements and Coverage of Nontargeted Adolescent Vaccines

Low human papillomavirus (HPV) vaccination coverage is an urgent public health problem requiring action. To identify policy remedies to suboptimal HPV vaccination, we assessed the relationship between states’ school entry requirements and adolescent vaccination

An exploratory trial implementing a community-based child oral health promotion intervention for Australian families from refugee and migrant backgrounds: a protocol paper for Teeth Tales

Introduction: Inequalities are evident in early childhood caries rates with the socially disadvantaged experiencing greater burden of disease. This study builds on formative qualitative research, conducted in the Moreland/Hume local government areas of Melbourne, Victoria 2006–2009, in response to community concerns for oral health of children from refugee and migrant backgrounds. Development of the community-based intervention described here extends the partnership approach to cogeneration of contemporary evidence with continued and meaningful involvement of investigators, community, cultural and government partners. This trial aims to establish a model for child oral health promotion for culturally diverse communities in Australia.&lt;p&gt;&lt;/p&gt; Methods and analysis: This is an exploratory trial implementing a community-based child oral health promotion intervention for Australian families from refugee and migrant backgrounds. Families from an Iraqi, Lebanese or Pakistani background with children aged 1–4 years, residing in metropolitan Melbourne, were invited to participate in the trial by peer educators from their respective communities using snowball and purposive sampling techniques. Target sample size was 600. Moreland, a culturally diverse, inner-urban metropolitan area of Melbourne, was chosen as the intervention site. The intervention comprised peer educator led community oral health education sessions and reorienting of dental health and family services through cultural Competency Organisational Review (CORe).&lt;p&gt;&lt;/p&gt; Ethics and dissemination: Ethics approval for this trial was granted by the University of Melbourne Human Research Ethics Committee and the Department of Education and Early Childhood Development Research Committee. Study progress and output will be disseminated via periodic newsletters, peer-reviewed research papers, reports, community seminars and at National and International conferences.&lt;p&gt;&lt;/p&gt

A SUPERVISED SINGULAR VALUE DECOMPOSITION FOR INDEPENDENT COMPONENT ANALYSIS OF fMRI

Functional Magnetic Resonance Imaging (fMRI) is a non-invasive tech-nique for studying the brain activity. The data acquisition process results a tempo-ral sequence of 3D brain images. Due to the high sensitivity of MR scanners, spikes are commonly observed in the data. Along with the temporal and spatial features of fMRI data, this artifact raises a challenging problem in the statistical analysis. In this paper, we introduce a supervised singular value decomposition technique as a data reduction step of independent component analysis (ICA), which is an effective tool for exploring spatio-temporal features in fMRI data. Two major advantages are discussed: first, the proposed method improves the robustness of ICA against spikes; second, the method uses the fMRI experimental designs to guide the fully data-driven ICA, yielding a more computationally efficient procedure and highly interpretable results. The advantages are demonstrated using spatio-temporal sim-ulation studies as well as a data analysis

Robust analysis of semiparametric renewal process models

A rate model is proposed for a modulated renewal process comprising a single long sequence, where the covariate process may not capture the dependencies in the sequence as in standard intensity models. We consider partial likelihood-based inferences under a semiparametric multiplicative rate model, which has been widely studied in the context of independent and identical data. Under an intensity model, gap times in a single long sequence may be used naively in the partial likelihood with variance estimation utilizing the observed information matrix. Under a rate model, the gap times cannot be treated as independent and studying the partial likelihood is much more challenging. We employ a mixing condition in the application of limit theory for stationary sequences to obtain consistency and asymptotic normality. The estimator's variance is quite complicated owing to the unknown gap times dependence structure. We adapt block bootstrapping and cluster variance estimators to the partial likelihood. Simulation studies and an analysis of a semiparametric extension of a popular model for neural spike train data demonstrate the practical utility of the rate approach in comparison with the intensity approach