Disturbance Variance Research Articles

Generation of random sea clutter amplitude sequence with quantitative control on distribution tail

AbstractThis letter proposes a data‐driven method to simulate random sea clutter amplitude sequence with distribution and correlation characteristics directly inherited from the measured clutter. First, a generation model is developed to simulate clutter amplitude sequence with desired distribution. By multiplying the measured amplitudes with random disturbance coefficients, the model introduces sample diversity between the simulated and measured sequences. Meanwhile, to quantitatively control the difference of the distribution tails between the simulated and measured sequences (i.e. to ensure their distributions are similar), the threshold error metric is introduced to set the disturbance variance. Second, a scheme combining the generation model with the correlation transfer technique is presented, which allows to simulate clutter amplitude sequence with both desired distribution and correlation for engineering purpose. Experiments based on different measured clutter sets indicate the proposed method works very well.

Multiyear variations of time-correlated mesoscale OH temperature perturbations near the mesopause at Maymaga, Tory and Zvenigorod

Ground-based observations of the natural hydroxyl (OH) nightglow at altitudes of 85–90 km are used for deriving the rotational temperature of excited OH, which is close to the neutral atmospheric temperature. For filtering of mesoscale perturbations, we use differences between pairs of measured values of OH rotational temperature separated with fixed time intervals in the range of 0.5–2 h. The filtering is applied for studying mesoscale variations of temperature near the mesopause according to the data of spectral OH nightglow measurements at observatories of Zvenigorod (56°N, 37°E.) in the years 2004–2016, Tory (52°N, 103°E) in 2012–2017 and Maymaga (63°N, 130°E) in 2000–2015. Monthly-mean values and variances of temperature disturbances with periods 0.7–8.2 h are determined. Semiempirical and statistical approaches are used to estimate and subtract variances of the instrumental dark current noise and uncorrelated in time fluctuations. Seasonal and interannual variations in standard deviations of correlated in time mesoscale perturbations of the OH rotational temperature at the considered observational sites are studied. They can give information about multiyear changes in the activity of atmospheric acoustic-gravity waves propagating through the OH layer near the mesopause.

Likelihood Ratio Processes under Nonstandard Settings

This paper establishes the LAN property for the curved normal families and the simultaneous equation systems. In addition, we show that one-way random ANOVA models fail to have the LAN property. We consider the two cases when the variance of random effect lies in the interior and boundary of parameter space. In the former case, the log-likelihood ratio converges to 0. In the latter case, the log-likelihood ratio has atypical limit distributions, which depend on the contiguity orders. The contiguity orders corresponding to the variances of random effects and disturbances can be equal to or greater than one, respectively, and that corresponding to the grand mean can be equal to or greater than one half. Consequently, we cannot use the ordinary optimal theory based on the LAN property. Meanwhile, the test based on the log-likelihood ratio is shown to be asymptotically most powerful with the benefit of the classical Neymann--Pearson framework.

Multi-rate Gaussian Bayesian network soft sensor development with noisy input and missing data

For efficient process control and monitoring, accurate real-time information of quality variables is essential. To predict these quality (or slow-rate) variables at a fast-rate, in the industry, inferential/soft sensors are often used. However, most of the conventional methods for soft sensors do not utilize prior process knowledge even if it is available. The prediction accuracy of these inferential sensors depends mainly on the quality of available data, which can be affected by significant noise and possible sensor failures. To address these issues, in this work, a generic Gaussian Bayesian network based soft-sensor framework is developed, which can account multiple hidden states and multirate/missing data. In the proposed framework, due to the presence of hidden variables and missing data, posterior probability of these variables in E-step of the EM algorithm is evaluated using Bayesian inference. Compared to the existing soft-sensors, the proposed approach will allow users to integrate prior knowledge into the BN structure. Moreover, due to the probabilistic nature of BNs, variances of measurement noises and disturbances between hidden states are simultaneously estimated. The proposed framework is generic and can be used for any multi-layered structure. Its performance is demonstrated for two different structures, two-layer and multilayered structures, on a benchmark flow-network problem and an industrial process. It is observed that the proposed Gaussian Bayesian network based soft sensors are able to give significantly better and more reliable estimates compared to the conventional approaches.

Sequential and efficient GMM estimation of dynamic short panel data models

This paper considers generalized method of moments (GMM) and sequential GMM (SGMM) estimation of dynamic short panel data models. The efficient GMM motivated from the quasi maximum likelihood (QML) can avoid the use of many instrument variables (IV) for estimation. It can be asymptotically efficient as maximum likelihood estimators (MLE) when disturbances are normal, and can be more efficient than QML estimators when disturbances are not normal. The SGMM, which also incorporates many IVs, generalizes the minimum distance estimation originated in Hsiao et al. . By focusing on the estimation of parameters of interest, the SGMM saves computational burden caused by nuisance parameters such as variances of disturbances. It is asymptotically as efficient as the corresponding GMM. In particular, the SGMM based on QML scores can generate a closed-form root estimator for the dynamic parameter, which is asymptotically as efficient as the QML estimator. Nuisance parameters can also be estimated efficiently by an additional SGMM step if they are of interest.

Involvement of Lipids in Alzheimer's Disease Pathology and Potential Therapies.

Lipids constitute the bulk of the dry mass of the brain and have been associated with healthy function as well as the most common pathological conditions of the brain. Demographic factors, genetics, and lifestyles are the major factors that influence lipid metabolism and are also the key components of lipid disruption in Alzheimer’s disease (AD). Additionally, the most common genetic risk factor of AD, APOE ϵ4 genotype, is involved in lipid transport and metabolism. We propose that lipids are at the center of Alzheimer’s disease pathology based on their involvement in the blood-brain barrier function, amyloid precursor protein (APP) processing, myelination, membrane remodeling, receptor signaling, inflammation, oxidation, and energy balance. Under healthy conditions, lipid homeostasis bestows a balanced cellular environment that enables the proper functioning of brain cells. However, under pathological conditions, dyshomeostasis of brain lipid composition can result in disturbed BBB, abnormal processing of APP, dysfunction in endocytosis/exocytosis/autophagocytosis, altered myelination, disturbed signaling, unbalanced energy metabolism, and enhanced inflammation. These lipid disturbances may contribute to abnormalities in brain function that are the hallmark of AD. The wide variance of lipid disturbances associated with brain function suggest that AD pathology may present as a complex interaction between several metabolic pathways that are augmented by risk factors such as age, genetics, and lifestyles. Herewith, we examine factors that influence brain lipid composition, review the association of lipids with all known facets of AD pathology, and offer pointers for potential therapies that target lipid pathways.

On Robust Estimation of Error Variance in (Highly) Robust Regression

Abstract The linear regression model requires robust estimation of parameters, if the measured data are contaminated by outlying measurements (outliers). While a number of robust estimators (i.e. resistant to outliers) have been proposed, this paper is focused on estimating the variance of the random regression errors. We particularly focus on the least weighted squares estimator, for which we review its properties and propose new weighting schemes together with corresponding estimates for the variance of disturbances. An illustrative example revealing the idea of the estimator to down-weight individual measurements is presented. Further, two numerical simulations presented here allow to compare various estimators. They verify the theoretical results for the least weighted squares to be meaningful. MM-estimators turn out to yield the best results in the simulations in terms of both accuracy and precision. The least weighted squares (with suitable weights) remain only slightly behind in terms of the mean square error and are able to outperform the much more popular least trimmed squares estimator, especially for smaller sample sizes.

Were There Regime Switches in China's Monetary Policy?

This paper develops and estimates the quantity based Markov-switching(MS) monetary policy rule for China. The MS regression results show that China's monetary policy rule does exist regime changes, and the best fit is with a version that allows time variation both in disturbance variance and intercept. The volatility and steady state of M2 growth rate under the volatile regime are much larger than that in the moderate regime. I then build an MS-DSGE model allowing for regime switching in monetary shock's variance and steady-state of M2 growth rate. Based on the Bayesian estimation of MS-DSGE model, I find that monetary shock becomes the main driver of the variations in GDP growth rate and inflation rate under the volatile regime,and monetary policy regime shift between moderate regime and volatile regime can lead to the time-varying volatilities of inflation and output in China's bussiness cycle.

Differentially Private Consensus With an Event-Triggered Mechanism

This paper studies the differentially private consensus problem of multiagent networks by employing a distributed event-triggered mechanism such that not only agents can protect the privacy of their initial states from information disclosure, but the execution efficiency of the whole network can be improved. First, we propose a distributed event-triggered mechanism for a differentially private consensus algorithm such that frequent real-time communication and controller updates can be avoided. Second, we propose a distributed event-triggering condition that only depends on local information and local parameters, which can effectively avoid global information collection. Third, the convergence analysis of the mean-square average consensus is given to explain the sufficiency of the proposed event-triggered mechanism and event-triggering condition. Furthermore, we establish the statistic properties of the convergent accuracy that the expectation of the convergence point converges to the average value of all agents’ initial states exactly and the disturbance variance is bounded with an explicit expression. In addition, we further give the differential privacy analysis that each agent can flexibly select its own privacy level to prevent information disclosure. Finally, simulation results are given to illustrate the effectiveness of the proposed mechanism and the correctness of the theoretical results.

Ion Friction and Quantification of the Geomagnetic Influence on Gravity Wave Propagation and Dissipation in the Thermosphere‐Ionosphere

AbstractMotions of neutrals and ions in the thermosphere‐ionosphere (TI) do not, generally, coincide due to the presence of the geomagnetic field and associated electromagnetic forces affecting plasma. Collisions of ions with gravity wave (GW)‐induced motions of neutrals impose damping on the latter. We derive a practical formula for the vertical damping rate of GW harmonics that accounts for the geometry of the geomagnetic field and the direction of GW propagation. The formula can be used in parameterizations of GW effects developed for general circulation models extending from the lower atmosphere into the mesosphere and thermosphere. Vertical damping of GW harmonics by ion‐neutral interactions in the TI depends on the geometry of the geomagnetic field but not the strength of the latter. The ion damping of harmonics propagating in the meridional direction (in the geomagnetic coordinates) maximizes over the poles and reduces to zero over the equator. Waves propagating in the zonal direction are uniformly affected by ions at all latitudes. Accounting for the anisotropy produces changes in the GW drag in the F region of more than 100 m s−1 d−1, cooling/heating rates of more than 15 K d−1, and in GW temperature variance of disturbances by more than 5 K.

THE REGIME-SWITCHING VOLATILITY OF EURO AREA BUSINESS CYCLES

We document the strong evidence of time variation in the volatility of Euro Area business cycles since 1970. Then we provide the quantitative sources of these changes using a medium-scale DSGE model allowing time variation in structural disturbance variances. We show that (1) the size of different types of shock oscillates, in a synchronized manner, between two regimes over time, with the high-volatility regime prevailing predominantly in the 1970s, sporadically in the 1980s and 1990s, and during the Great Recession; (2) their relative importance remains, however, unchanged across regimes, where neutral technology shocks and marginal efficiency of investment shocks are the dominant sources of business cycle fluctuations; and 3) these investment shocks, which affect the transformation of savings into productive capital, can be interpreted as an indicator of credit conditions.

Robust nonlinear filter for nonlinear systems with multiplicative noise uncertainties, unknown external disturbances, and packet dropouts

SummaryThis study is concerned with the robust nonlinear filtering problem for nonlinear discrete‐time stochastic system with multiplicative noise uncertainties, unknown external disturbances, and packet dropouts. The focus of this paper is to design a filter with predictor–corrector structure such that the upper bound on the state estimation error variance is minimized in the presence of multiplicative noise, unknown external disturbances, and packet dropouts. Thus, a robust nonlinear filter based on the method to obtain the upper bound on variances of multiplicative noises, unknown disturbances, and packet dropouts is designed. Further stability analysis shows that the proposed filter has robustness against multiplicative noises, unknown external disturbances, and packet dropouts. Simulation results show that the proposed filter is more effective than extended Kalman filter and other robust extended Kalman filter. Copyright © 2017 John Wiley & Sons, Ltd.

On predicting receptivity to surface roughness in a compressible infinite swept wing boundary layer

The receptivity of crossflow disturbances on an infinite swept wing is investigated using solutions of the adjoint linearised Navier-Stokes equations. The adjoint based method for predicting the magnitude of stationary disturbances generated by randomly distributed surface roughness is described, with the analysis extended to include both surface curvature and compressible flow effects. Receptivity is predicted for a broad spectrum of spanwise wavenumbers, variable freestream Reynolds numbers, and subsonic Mach numbers. Curvature is found to play a significant role in the receptivity calculations, while compressible flow effects are only found to marginally affect the initial size of the crossflow instability. A Monte Carlo type analysis is undertaken to establish the mean amplitude and variance of crossflow disturbances generated by the randomly distributed surface roughness. Mean amplitudes are determined for a range of flow parameters that are maximised for roughness distributions containing a broad spectrum of roughness wavelengths, including those that are most effective in generating stationary crossflow disturbances. A control mechanism is then developed where the short scale roughness wavelengths are damped, leading to significant reductions in the receptivity amplitude.

Examining the Conditions that will strengthen the Success of the Iterative Stein-Rule Estimator of The Disturbance Variance in Proxy Model

The aim of this study is to give the conditions, in a linear regression model with proxy variables, when is the difference of variances of two estimators getting closer to each other. One of the mentioned estimators is the iterative Stein-rule estimator (ISRE) of the disturbance variance which is obtained by taking the Stein-rule estimator of the parameters in the estimator of the disturbance variance; one is the usual ordinary least squares (OLS) estimator of the disturbance variance. For that purpose the theoretical difference of variances is derived and the numerical analysis is handled to see the pattern of given theoretical difference.

A Heterogeneous Coefficient Approach to the Knowledge Production Function

We apply a heterogeneous coefficients spatial autoregressive panel model capable of producing estimates of knowledge production function parameters for each region in the sample. Past literature has used conventional spatial autoregressive panel data models to relate patent production output to knowledge production inputs. These models summarize the relationship involving N regions over T time periods by restricting coefficients to be homogeneous across regions and time periods. Of course, we can allow for region-specific and time-specific fixed effects in an attempt to ameliorate the fact that we rely on homogeneous coefficients in the model. Introducing these allows for region- and time-specific differences in the model intercept. A heterogeneous coefficients spatial autoregressive (HSAR) panel model specification allows for variation in the level of spatial dependence/interaction as well as knowledge production function coefficients, intercepts and noise variances across each region. In many contexts (including our case of 94 French NUTS3 regions) a small group of regions within a country account for the majority of innovation activity. Some regions might exhibit knowledge production output that is sensitive to private R&D inputs, while others involve relationships where the knowledge base or public inputs are more important. Some regions might benefit from spill-out impacts of neighbors, while others do not. From an econometric viewpoint, it would be desirable to have estimates of the knowledge production function at the regional level that allow for differences in the level of interaction/spatial dependence between regions as well as knowledge production function coefficients, intercepts and disturbance variances. This type of model would allow inference regarding how knowledge output of each region responds to changing levels of own- and neighboring-region knowledge inputs, and how knowledge production is influenced by neighboring region production as well as time-invariant region-specific factors. We estimate region-specific knowledge production functions for 94 NUTS3 regions in France using a panel covering 21 years from 1988 to 2008 and 4 high-technology industries: chemistry, pharmaceutical, mechanics and materials. A great deal of regional heterogeneity in the knowledge production function relationship exists across regions, providing new insights regarding spatial spill-in and spill-out effects between regions.

Monetary and fiscal policy switching with time-varying volatilities

This paper extends the ongoing literature on regime change. The extension allows time variation in disturbance variances of interest rate rules for monetary policy and tax rules for fiscal policy that switch stochastically between two regimes. We achieve superior modelings of monetary and fiscal policy rules with quarterly US data.

Asymmetric risk of the Stein variance estimator under a misspecified linear regression model

Using LINear EXponential (LINEX) loss, we formulate the risk of a generic pre-test estimator for the disturbance variance in a misspecified regression model. The dominance and inadmissibility of the Stein estimator of disturbance variance are derived under respective loss configures.

Quantal response methods for equilibrium selection in normal form games

This paper describes a general framework for equilibrium selection by tracing the graph of the quantal response equilibrium (QRE) correspondence as a function of the variance of random disturbances. If a quantal response function satisfies C2 continuity, monotonicity and cumulativity, the graph of QRE correspondence generically includes a unique branch that starts at the centroid of the strategy simplex and converges to a unique Nash equilibrium as noises vanish. This equilibrium is called the limiting QRE of the game. We then investigate the limiting QRE in normal form games, and analyze the effects of payoff transformations and adding/eliminating dominated strategies on equilibrium selection. We find that in two-person symmetric games, any strict Nash equilibrium can be selected as the limiting QRE by appropriately adding a single strictly dominated strategy.

Bootstrap test for a structural break under possible heteroscedasticity

ABSTRACTIn this article, we consider the Wald test statistic for testing equality between the sets of regression coefficients in two linear regression models when the disturbance variances may possibly be unequal. This test can be also used as a test for a structural break. However, it is well known that the test based on the Wald test statistic suffers from severe size distortion in small sample when the disturbance variances of the two regression models are unequal. Our simulation results show that substantial improvements are made when the bootstrap methods are applied.

SUR 토빗회귀모형에서 베이지안 추정과 최대가능도 추정의 비교

Both Bayesian and maximum likelihood methods are efficient for the estimation of regression coefficients of various Tobit regression models (see. e.g. Chib, 1992; Greene, 1990; Lee and Choi, 2013); however, some researchers recognized that the maximum likelihood method tends to underestimate the disturbance variance, which has implications for the estimation of marginal effects and the asymptotic standard error of estimates. The underestimation of the maximum likelihood estimate in a seemingly unrelated Tobit regression model is examined. A Bayesian method based on an objective noninformative prior is shown to provide proper estimates of the disturbance variance as well as other regression parameters