Estimation of signals in colored non gaussian noise based on Gaussian Mixture models

Non-Gaussianity of signals/noise often results in significant performance degradation for systems, which are designed using the Gaussian assumption. So non-Gaussian signals/noise require a different modelling and processing approach. In this paper, we discuss a new Bayesian estimation technique for non-Gaussian signals corrupted by colored non Gaussian noise. The method is based on using zero mean finite Gaussian Mixture Models (GMMs) for signal and noise. The estimation is done using an adaptive non-causal nonlinear filtering technique. The method involves deriving an estimator in terms of the GMM parameters, which are in turn estimated using the EM algorithm. The proposed filter is of finite length and offers computational feasibility. The simulations show that the proposed method gives a significant improvement compared to the linear filter for a wide variety of noise conditions, including impulsive noise. We also claim that the estimation of signal using the correlation with past and future samples leads to reduced mean squared error as compared to signal estimation based on past samples only.

An Empirical Study of the Mixture of Time and Movements in Prices

This paper investigates the persistent pattern in the Helsinki Exchanges. The persistent pattern is analyzed using a time and a price approach. It is hypothesized that arrival times are related to movements in prices. Thus, the arrival times are defined as durations and formulated as an Autoregressive Conditional Duration (ACD) model as in Engle and Russell (1998). The prices are defined as price changes and formulated as a GARCH process including duration measures. The research question follows from market microstructure predictions about price intensities defined as time between price changes. The microstructure theory states that long transaction durations might be associated with both no news and bad news. Accordingly, short durations would be related to high volatility and long durations to low volatility. As a result, the spread will tend to be larger under intensive moments. The main findings of this study are 1) arrival times are positively autocorrelated and 2) long durations are associated with low volatility in the market.

Ambiguity-free polarization measurements from mixture initial preparations

Starting from beam and target spin systems which are polarized in the usual way by applying external magnetic fields, measurements of appropriate final state tensor parameters, viz., {t0,1k, k=1,...,2j} of particle d with spin j in a reaction a+b→d+c1+c2+. . .are suggested to determine the reaction amplitudes in spin space free from any associated discrete ambiguity.

Simultaneous effect of lead and cadmium on granulosa cells: A cellular model for ovarian toxicity

Lead (Pb) and cadmium (Cd) are known reproductive toxicants, which accumulate in granulosa cells of the ovary. Female Charles foster rats were treated with sodium acetate (control), lead acetate and cadmium acetate either alone or in combination at a dose 0.05 mg/kg body weight intra-peritoneally for 15 days daily. Animals were killed at proestrous stage and granulosa cells were isolated from the ovaries. Binding of I-125-luteinizing hormone (I-125-LH), I-125-follicle stimulating hormone (I-125-FSH) and 17 beta-hydroxysteroid dehydrogenase activity were measured. As these receptors are localized on the surface of the cell membrane, we also estimated the membrane parameters of these cells. Our results demonstrated that both lead and cadmium caused a significant reduction in gonadotropin binding, which altered steroidogenic enzyme activity of granulosa cells. These changes exhibited a positive correlation with membrane changes of the granulosa cells.

Nonlinear mixture autoregressive model: economic applications

The aim of this dissertation is to model economic variables by a mixture autoregressive (MAR) model. The MAR model is a generalization of linear autoregressive (AR) model. The MAR -model consists of K linear autoregressive components. At any given point of time one of these autoregressive components is randomly selected to generate a new observation for the time series. The mixture probability can be constant over time or a direct function of a some observable variable. Many economic time series contain properties which cannot be described by linear and stationary time series models. A nonlinear autoregressive model such as MAR model can a plausible alternative in the case of these time series. In this dissertation the MAR model is used to model stock market bubbles and a relationship between inflation and the interest rate. In the case of the inflation rate we arrived at the MAR model where inflation process is less mean reverting in the case of high inflation than in the case of normal inflation. The interest rate move one-for-one with expected inflation. We use the data from the Livingston survey as a proxy for inflation expectations. We have found that survey inflation expectations are not perfectly rational. According to our results information stickiness play an important role in the expectation formation. We also found that survey participants have a tendency to underestimate inflation. A MAR model has also used to model stock market bubbles and crashes. This model has two regimes: the bubble regime and the error correction regime. In the error correction regime price depends on a fundamental factor, the price-dividend ratio, and in the bubble regime, price is independent of fundamentals. In this model a stock market crash is usually caused by a regime switch from a bubble regime to an error-correction regime. According to our empirical results bubbles are related to a low inflation. Our model also imply that bubbles have influences investment return distribution in both short and long run.

Enhanced Pair Hydrophobicity in the Water-Dimethylsulfoxide (DMSO) Binary Mixture at Low DMSO Concentrations

We observe a surprisingly sharp increase in the pair hydrophobicity in the water climethylsulfoxide (DMSO) binary mixture at small DMSO concentrations, with the mole fraction of DMSO (x(D)) in the range 0.12-0.16. The increase in pair hydrophobicity is measured by an increase in the depth of the first minimum in the potential of mean force (PMF) between two methane molecules. However, this enhanced hydrophobicity again weakens at higher DMSO concentrations. We find markedly unusual behavior of the pure binary mixture (in the same composition range) in the diffusion coefficient of DMSO and in the local composition fluctuation of water, We find that, in the said composition range, the average coordination number of the methyl groups (of distinct DMSO) varies between 2.4 and 2.6, indicating the onset of the formation of a chain-like extended connectivity in an otherwise stable tetrahedral network comprising of water and DMSO molecules. We propose that the enhanced pair hydrophobicity of the binary mixture at low DMSO concentrations is due to the participation of the two methane molecules in the local structural order and the emerging molecular associations in the water-DMSO mixture.

Acute toxicity of methyl isocyanate in mammals. II. Induction of hyperglycemia, lactic acidosis, uraemia, and hypothermia in rats

When rats were administered methyl isocyanate (MIC) by inhalation or subcutaneous route it produced severe hyperglycemia, clinical lactic acidosis, highly elevated plasma urea, and reduced plasma cholinesterase activity with unaltered erythrocytc acetyl cholinesterase activity. Irrespective of the route of administration, MIC also caused severe hypothermia, which was not ameliorated by prior administration of atropine sulphate. Acute toxic effects of MIC are essentially similar by either route except for the intensity of the effects