Ensemble and reduced-rank approaches to prediction and assimilation rely on low-dimensional approximations of the estimation error covariances. Here stability properties of the forecast/ analysis cycle for linear, time-independent systems are used to identify factors that cause the steady-state analysis error covariance to admit a low-dimensional representation. A useful measure of forecast/ana...

Proof of Theorem 1. The proof is summarized in the following three steps. First, we prove Qn(ω, μ∗,Ω∗) ≥ Qn(ω, μ∗,Ω∗) for ‖ω(1) − ω∗ (1)‖2 = Op(n). In Step 2, we show that Qn(ω, μ ∗,Ω∗) ≥ Qn(ω, μ∗,Ω) for ‖Ω− Ω‖F = Op{(pn + an) log pn/n}. In Step 3, we prove that Qn(ω, μ∗,Ω) ≥ Qn(ω, μ,Ω) for ‖μ − μ‖2 = Op(pn log pn/n). The following are the details. 20 Step 1. Let ∆ω(1) = ω(1) − ω∗ (1), and h(ω(...

In the context of survival analysis under a progressive censoring scheme, a class of analysis of covariance tests based on suitable linear rank statistics is proposed and studied. Some invariance principles for certain (multivariate) progressively censored rank order processes are ~ established and incorporated in the study of the asymptotic properties of the proposed tests. AMS 1970 Classifica...

Appearance-based image analysis techniques require fast computation of principal components of high-dimensional image vectors. We introduce a fast incremental principal component analysis (IPCA) algorithm, called candid covariance-free IPCA (CCIPCA), used to compute the principal components of a sequence of samples incrementally without estimating the covariance matrix (so covariance-free). The...

background :being informed of new methods of teaching and comparing their outcome help teachers use more effective and efficient methods in developing and implementing new training courses. the aim of this study was to examine the efficacy of epidemiology learning software on learning epidemiology courses.   methods : this quasi-experimental study included 101 nursingand midwifery students taki...

the present study is an attempt toward evaluating the performance of portfolios and assets selecting using modified mean-variance models by utilizing a non-parametric efficiency analysis tool, namely data envelopment analysis (dea). huge amounts of money are being invested in financial market. as a result, portfolio performance evaluation has created a great deal of interest among people. we kn...

Spike-triggered averaging techniques are effective for linear characterization of neural responses. But neurons exhibit important nonlinear behaviors, such as gain control, that are not captured by such analyses. We describe a spike-triggered covariance method for retrieving suppressive components of the gain control signal in a neuron. We demonstrate the method in simulation and on retinal gan...

In this paper, we introduce the usage of a newly developed spectral decomposition technique – combined maximum covariance analysis (CMCA) – in the spatiotemporal comparison of four satellite data sets and groundbased observations of aerosol optical depth (AOD). This technique is based on commonly used principal component analysis (PCA) and maximum covariance analysis (MCA). By decomposing the c...

Sample covariance matrices are a fundamental tool of multivariate statistics. After data collection, we get an n×p data matrix X. We will call n the number of observations and p the number of predictors. The rows of X are assumed to be realizations of a random variable whose covariance structure is Σ p. For practical applications, one often wishes to estimate Σp in order to understand the depen...

Covariance function plays a critical role in functional and longitudinal data analysis. In this paper, we consider nonparametric covariance function estimation using a reproducing kernel Hilbert space framework. A regularization method is introduced through a careful characterization of the function space in which a covariance function resides. It is shown that the procedure enjoys desirable th...