Confidence bands in nonparametric time series regression

We consider nonparametric estimation of mean regression and conditional variance (or volatility) functions in nonlinear stochastic regression models. Simultaneous confidence bands are constructed and the coverage probabilities are shown to be asymptotically correct. The imposed dependence structure allows applications in many linear and nonlinear auto-regressive processes. The results are applied to the S&P 500 Index data.Comment: Published in at http://dx.doi.org/10.1214/07-AOS533 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Profile control charts based on nonparametric LL-1 regression methods

Classical statistical process control often relies on univariate characteristics. In many contemporary applications, however, the quality of products must be characterized by some functional relation between a response variable and its explanatory variables. Monitoring such functional profiles has been a rapidly growing field due to increasing demands. This paper develops a novel nonparametric $L$-1 location-scale model to screen the shapes of profiles. The model is built on three basic elements: location shifts, local shape distortions, and overall shape deviations, which are quantified by three individual metrics. The proposed approach is applied to the previously analyzed vertical density profile data, leading to some interesting insights.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS501 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Composition analysis and preliminary safety evaluation of edible Monascus red pigment

ObjectiveTo provide scientific basis for the development and utilization of edible pigment Monascus red， the analysis of its key components and toxicological evaluation were carried out.MethodsIdentification test and physicochemical indexes test were carried out based on the samples according to the method in National food safety standard GB 1886.181—2016. The content of citrinin （CIT） and Monacolin K were detected through high performance liquid chromatography （HPLC）. UPLC-Orbitrap-MS2 were used for chemical composition and content determination， in which the molecular structure was also characterized. The 14-day oral toxicity test of SD rats was carried out， based on the acute oral toxicity test of Monascus red by the limited method.ResultsThe quality and specification of test samples conformed to the provisions of China National Food Safety Standard. The content of CIT and Monacolin K was 0.030 8 mg/kg （converted as one color value） and 0.166 mg/g. Twenty compounds were separated in Monascus red by UPLC-Orbitrap-MS2， red pigment， orange pigment and yellow pigment of it accounted for 88.38%， 2.04% and 5.96%， respectively. The results of acute oral toxicity showed that Monascus red was innocuous （LD50>20 g/kg·BW）. In the 14-day repeated oral dose toxicity test， compared with the control group， rats in 5 g/kg·BW dose group exhibited no significant differences in the general clinical observation， growth and development， hematology， blood biochemistry， routine urine detection， gross anatomy，organ weight，organ-to-body ratio， and histopathological examinations.ConclusionMonascus red is composed of multiple components characterized by red pigments. There is no obvious toxic effects found in the preliminary safety evaluation， which can provide reference for further long-term research

Molecular Control of Follicular Helper T cell Development and Differentiation

Follicular helper T cells (Tfh) are specialized helper T cells that are predominantly located in germinal centers and provide help to B cells. The development and differentiation of Tfh cells has been shown to be regulated by transcription factors, such as B-cell lymphoma 6 protein (Bcl-6), signal transducer and activator of transcription 3 (STAT3) and B lymphocyte-induced maturation protein-1 (Blimp-1). In addition, cytokines, including IL-21, have been found to be important for Tfh cell development. Moreover, several epigenetic modifications have also been reported to be involved in the determination of Tfh cell fate. The regulatory network is complicated, and the number of novel molecules demonstrated to control the fate of Tfh cells is increasing. Therefore, this review aims to summarize the current knowledge regarding the molecular regulation of Tfh cell development and differentiation at the protein level and at the epigenetic level to elucidate Tfh cell biology and provide potential targets for clinical interventions in the future

Parametric and nonparametric models and methods in financial econometrics

Financial econometrics has become an increasingly popular research field. In this paper we review a few parametric and nonparametric models and methods used in this area. After introducing several widely used continuous-time and discrete-time models, we study in detail dependence structures of discrete samples, including Markovian property, hidden Markovian structure, contaminated observations, and random samples. We then discuss several popular parametric and nonparametric estimation methods. To avoid model mis-specification, model validation plays a key role in financial modeling. We discuss several model validation techniques, including pseudo-likelihood ratio test, nonparametric curve regression based test, residuals based test, generalized likelihood ratio test, simultaneous confidence band construction, and density based test. Finally, we briefly touch on tools for studying large sample properties.

Density estimation for nonlinear parametric models with conditional heteroscedasticity

This article studies density and parameter estimation problems for nonlinear parametric models with conditional heteroscedasticity. We propose a simple density estimate that is particularly useful for studying the stationary density of nonlinear time series models. Under a general dependence structure, we establish the root n consistency of the proposed density estimate. For parameter estimation, a Bahadur type representation is obtained for the conditional maximum likelihood estimate. The parameter estimate is shown to be asymptotically efficient in the sense that its limiting variance attains the Cramér-Rao lower bound. The performance of our density estimate is studied by simulations.Bahadur representation Conditional heteroscedasticity Density estimation Fisher information Nonlinear time series Nonparametric kernel density Stationary density Stochastic regression

Nonparametric model validations for hidden Markov models with applications in financial econometrics

We address the nonparametric model validation problem for hidden Markov models with partially observable variables and hidden states. We achieve this goal by constructing a nonparametric simultaneous confidence envelope for transition density function of the observable variables and checking whether the parametric density estimate is contained within such an envelope. Our specification test procedure is motivated by a functional connection between the transition density of the observable variables and the Markov transition kernel of the hidden states. Our approach is applicable for continuous-time diffusion models, stochastic volatility models, nonlinear time series models, and models with market microstructure noise.Confidence envelope Diffusion model Hidden Markov model Market microstructure noise Model validation Nonlinear time series Transition density Stochastic volatility