Search

Single‐nitrogen‐vacancy (NV) centers in diamond are highly promising quantum nuclear magnetic resonance (NMR) sensors. However, their exposure to many sources of noise, such as surface impurities, shot noise from avalanche photodiode overlaps, and spin‐state projection errors inherent in quantum systems, limits their usefulness. Often, long measurement durations are required to accumulate sufficient data for NMR signal detection via fast Fourier transform spectrometry. For practical reasons, methods to shorten the necessary accumulation time for NMR signal detection are greatly desired. In this article, an on‐line formulation of maximum likelihood estimation (MLE) signal processing for quantum heterodyne NMR measurements is presented as a step toward this goal. This MLE method reduces the required data accumulation time by orders of magnitude and provides good estimates of target frequency locales in real time. These results are significant to practitioners of NMR detection with single‐NV centers in diamond who require a quick litmus test for potential signals when probing a wide area. [ABSTRACT FROM AUTHOR]

This paper, discusses the problem of partially step-stress ALTs (accelerated life tests) form Rayleigh competing risks model. Type-II censored scheme is used in obtaining the observed censoring data. The method of MLE (maximum likelihood estimation) of the model parameters for point and approximate confidence intervals are considered. Also, bootstrap confidence intervals of model parameters are discussed. Simulation study is adopted to assess and compare our proposed method. Finally, some comment to illustrate the behavior of numerical results. [ABSTRACT FROM AUTHOR]

Most protein substitution models use a single amino acid replacement matrix summarizing the biochemical properties of amino acids. However, site evolution is highly heterogeneous and depends on many factors that influence the substitution patterns. In this paper, we investigate the use of different substitution matrices for different site evolutionary rates. Indeed, the variability of evolutionary rates corresponds to one of the most apparent heterogeneity factors among sites, and there is no reason to assume that the substitution patterns remain identical regardless of the evolutionary rate. We first introduce LG4M, which is composed of four matrices, each corresponding to one discrete gamma rate category (of four). These matrices differ in their amino acid equilibrium distributions and in their exchangeabilities, contrary to the standard gamma model where only the global rate differs from one category to another. Next, we present LG4X, which also uses four different matrices, but leaves aside the gamma distribution and follows a distribution-free scheme for the site rates. All these matrices are estimated from a very large alignment database, and our two models are tested using a large sample of independent alignments. Detailed analysis of resulting matrices and models shows the complexity of amino acid substitutions and the advantage of flexible models such as LG4M and LG4X. Both significantly outperform single-matrix models, providing gains of dozens to hundreds of log-likelihood units for most data sets. LG4X obtains substantial gains compared with LG4M, thanks to its distribution-free scheme for site rates. Since LG4M and LG4X display such advantages but require the same memory space and have comparable running times to standard models, we believe that LG4M and LG4X are relevant alternatives to single replacement matrices. Our models, data, and software are available from http://www.atgc-montpellier.fr/models/lg4x. [ABSTRACT FROM PUBLISHER]

The question of whether more Socially Responsible (SR) firms outperform or underperform other conventional firms has been debated in the economic literature. In this study, using the Socially Responsible Investment (SRI) indexes and conventional stock indexes in the US, the UK and Japan, first and second moments of firm performance distributions are estimated based on the Markov Switching (MS) model. We find two distinct regimes (bear and bull) in the SRI markets as well as the stock markets for all the three countries. These regimes occur with the same timing in both types of market. No statistical difference in means and volatilities generated from the SRI indexes and conventional indexes in either region was found. Furthermore, we find strong comovements between the two indexes in both the regimes. [ABSTRACT FROM AUTHOR]

Poisson point processes play important role in various domains of Probability Theory and Mathematical Statistics. In this article, we investigate only two applications of Poisson point processes: a generated white noise problem and parameters estimation problem. This work continues the investigations started in paper Egorov and Kondybaev (2009). [ABSTRACT FROM AUTHOR]

Much current research in functional magnetic resonance imaging (fMRI) employs multivariate machine learning approaches (e.g., support vector machines) to detect distributed spatial patterns from the temporal fluctuations of the neural signal. The aim of many studies is not classification, however, but investigation of multivariate spatial patterns, which pattern classifiers detect only indirectly. Here we propose a direct statistical measure for the existence of distributed spatial patterns (or spatial heterogeneity) applicable to fMRI datasets. We extend the univariate general linear model (GLM), typically used in fMRI analysis, to a multivariate case. We demonstrate that contrasting maximum likelihood estimations of different restrictions on this multivariate model can be used to estimate the extent of spatial heterogeneity in fMRI data. Under asymptotic assumptions inference can be made with reference to the χ2 distribution. The test statistic is then assessed using simulated timecourses derived from real fMRI data followed by analyzing data from a real fMRI experiment. These analyses demonstrate the utility of the proposed measure of heterogeneity as well as considerations in its application. Measuring spatial heterogeneity in fMRI has important theoretical implications in its own right and may have potential uses for better characterising neurological conditions such as stroke and Alzheimer's disease. [ABSTRACT FROM PUBLISHER]

The use of indicator variables for computing predictions for the linear model is a well known technique. Fuller (Fuller, W. A. (1980). The use of indicator variables in computing predictions. J. Econometrics2: 231–243.) extends this to predictions for models with a general covariance structure and nonlinear models. In this work we use indicator variables for spatial data models with trend and a parametrized but unknown covariance function. We show that Restricted Maximum Likelihood (REML) estimates are a natural way to estimate the covariance parameters under this schema. We use dummy variables to predict the response at any number of sites, on a random Gaussian field. A simulation study was conducted to study the performance of the estimate and predictor when we consider indicator variables in the model. [ABSTRACT FROM AUTHOR]