Adaptive Unified Biased Estimators of Parameters in Linear Model
Hu Yang; Li-xing Zhu
2004-01-01
To tackle multi collinearity or ill-conditioned design matrices in linear models,adaptive biased estimators such as the time-honored Stein estimator,the ridge and the principal component estimators have been studied intensively.To study when a biased estimator uniformly outperforms the least squares estimator,some suficient conditions are proposed in the literature.In this paper,we propose a unified framework to formulate a class of adaptive biased estimators.This class includes all existing biased estimators and some new ones.A suficient condition for outperforming the least squares estimator is proposed.In terms of selecting parameters in the condition,we can obtain all double-type conditions in the literature.
Biases on cosmological parameter estimators from galaxy cluster number counts
Penna-Lima, M; Wuensche, C A
2013-01-01
The abundance of galaxy clusters is becoming a standard cosmological probe. In particular, Sunyaev-Zel'dovich (SZ) surveys are promising probes of the Dark Energy (DE) equation of state (eqos), given their ability to find distant clusters and provide estimates for their mass. However, current SZ catalogs contain tens to hundreds of objects. In this case, it is not guaranteed that maximum likelihood (ML) estimators of cosmological parameters are unbiased. In this work we study estimators from cluster abundance for some cosmological parameters. We derive an unbinned likelihood for cluster abundance, showing that it is equivalent to the one commonly used in the literature. We use the Monte Carlo (MC) approach to determine the presence of bias using this likelihood and its behavior with both area and depth of the survey, and the number of cosmological parameters fitted simultaneously. Assuming perfect knowledge on mass and redshift, we obtain that some estimators have non negligible biases. For example, the bias ...
Stealth Bias in Gravitational-Wave Parameter Estimation
Vallisneri, Michele
2013-01-01
Inspiraling binaries of compact objects are primary targets for current and future gravitational-wave observatories. Waveforms computed in General Relativity are used to search for these sources, and will probably be used to extract source parameters from detected signals. However, if a different theory of gravity happens to be correct in the strong-field regime, source-parameter estimation may be affected by a fundamental bias: that is, by systematic errors induced due to the use of waveforms derived in the incorrect theory. If the deviations from General Relativity are not large enough to be detectable on their own and yet these systematic errors remain significant (i.e., larger than the statistical uncertainties in parameter estimation), fundamental bias cannot be corrected in a single observation, and becomes stealth bias. In this article we develop a scheme to determine in which cases stealth bias could be present in gravitational-wave astronomy. For a given observation, the answer depends on the detecti...
A symptotic Bias for GMM and GEL Estimators with Estimated Nuisance Parameter
Newey, Whitney K.; Joaquim J. S. Ramalho; Smith, Richard J.
2003-01-01
This papers studies and compares the asymptotic bias of GMM and generalized empirical likelihood (GEL) estimators in the presence of estimated nuisance parameters. We consider cases in which the nuisance parameter is estimated from independent and identical samples. A simulation experiment is conducted for covariance structure models. Empirical likelihood offers much reduced mean and median bias, root mean squared error and mean absolute error, as compared with two-step GMM and other GEL meth...
BIASED BEARINGS-ONIKY PARAMETER ESTIMATION FOR BISTATIC SYSTEM
Xu Benlian; Wang Zhiquan
2007-01-01
According to the biased angles provided by the bistatic sensors,the necessary condition of observability and Cramer-Rao low bounds for the bistatic system are derived and analyzed,respectively.Additionally,a dual Kalman filter method is presented with the purpose of eliminating the effect of biased angles on the state variable estimation.Finally,Monte-Carlo simulations are conducted in the observable scenario.Simulation results show that the proposed theory holds true,and the dual Kalman filter method can estimate state variable and biased angles simultaneously.Furthermore,the estimated results can achieve their Cramer-Rao low bounds.
Bootstrap Co-integration Rank Testing: The Effect of Bias-Correcting Parameter Estimates
Cavaliere, Giuseppe; Taylor, A. M. Robert; Trenkler, Carsten
2013-01-01
In this paper we investigate bootstrap-based methods for bias-correcting the first-stage parameter estimates used in some recently developed bootstrap implementations of the co-integration rank tests of Johansen (1996). In order to do so we adapt the framework of Kilian (1998) which estimates the bias in the original parameter estimates using the average bias in the corresponding parameter esti- mates taken across a large number of auxiliary bootstrap replications. A number of possible imp...
Zhang, L.F. [Department of Industrial and Systems Engineering, National University of Singapore, 10 Kent Ridge Crescent, Singapore 119260 (Singapore); Xie, M. [Department of Industrial and Systems Engineering, National University of Singapore, 10 Kent Ridge Crescent, Singapore 119260 (Singapore)]. E-mail: mxie@nus.edu.sg; Tang, L.C. [Department of Industrial and Systems Engineering, National University of Singapore, 10 Kent Ridge Crescent, Singapore 119260 (Singapore)
2006-08-15
Estimation of the Weibull shape parameter is important in reliability engineering. However, commonly used methods such as the maximum likelihood estimation (MLE) and the least squares estimation (LSE) are known to be biased. Bias correction methods for MLE have been studied in the literature. This paper investigates the methods for bias correction when model parameters are estimated with LSE based on probability plot. Weibull probability plot is very simple and commonly used by practitioners and hence such a study is useful. The bias of the LS shape parameter estimator for multiple censored data is also examined. It is found that the bias can be modeled as the function of the sample size and the censoring level, and is mainly dependent on the latter. A simple bias function is introduced and bias correcting formulas are proposed for both complete and censored data. Simulation results are also presented. The bias correction methods proposed are very easy to use and they can typically reduce the bias of the LSE of the shape parameter to less than half percent.
Correcting the bias of empirical frequency parameter estimators in codon models.
Sergei Kosakovsky Pond
Full Text Available Markov models of codon substitution are powerful inferential tools for studying biological processes such as natural selection and preferences in amino acid substitution. The equilibrium character distributions of these models are almost always estimated using nucleotide frequencies observed in a sequence alignment, primarily as a matter of historical convention. In this note, we demonstrate that a popular class of such estimators are biased, and that this bias has an adverse effect on goodness of fit and estimates of substitution rates. We propose a "corrected" empirical estimator that begins with observed nucleotide counts, but accounts for the nucleotide composition of stop codons. We show via simulation that the corrected estimates outperform the de facto standard estimates not just by providing better estimates of the frequencies themselves, but also by leading to improved estimation of other parameters in the evolutionary models. On a curated collection of sequence alignments, our estimators show a significant improvement in goodness of fit compared to the approach. Maximum likelihood estimation of the frequency parameters appears to be warranted in many cases, albeit at a greater computational cost. Our results demonstrate that there is little justification, either statistical or computational, for continued use of the -style estimators.
Parameter Estimation with BEAMS in the presence of biases and correlations
Newling, James; Hlozek, Renée; Kunz, Martin; Smith, Mathew; Varughese, Melvin
2011-01-01
The original formulation of BEAMS - Bayesian Estimation Applied to Multiple Species - showed how to use a dataset contaminated by points of multiple underlying types to perform unbiased parameter estimation. An example is cosmological parameter estimation from a photometric supernova sample contaminated by unknown Type Ibc and II supernovae. Where other methods require data cuts to increase purity, BEAMS uses all of the data points in conjunction with their probabilities of being each type. Here we extend the BEAMS formalism to allow for correlations between the data and the type probabilities of the objects as can occur in realistic cases. We show with simple simulations that this extension can be crucial, providing a 50% reduction in parameter estimation variance when such correlations do exist. We then go on to perform tests to quantify the importance of the type probabilities, one of which illustrates the effect of biasing the probabilities in various ways. Finally, a general presentation of the selection...
Systematic Biases in Parameter Estimation of Binary Black-Hole Mergers
Littenberg, Tyson B.; Baker, John G.; Buonanno, Alessandra; Kelly, Bernard J.
2012-01-01
Parameter estimation of binary-black-hole merger events in gravitational-wave data relies on matched filtering techniques, which, in turn, depend on accurate model waveforms. Here we characterize the systematic biases introduced in measuring astrophysical parameters of binary black holes by applying the currently most accurate effective-one-body templates to simulated data containing non-spinning numerical-relativity waveforms. For advanced ground-based detectors, we find that the systematic biases are well within the statistical error for realistic signal-to-noise ratios (SNR). These biases grow to be comparable to the statistical errors at high signal-to-noise ratios for ground-based instruments (SNR approximately 50) but never dominate the error budget. At the much larger signal-to-noise ratios expected for space-based detectors, these biases will become large compared to the statistical errors but are small enough (at most a few percent in the black-hole masses) that we expect they should not affect broad astrophysical conclusions that may be drawn from the data.
Xue, Junchen; Song, Shuli; Liao, Xinhao; Zhu, Wenyao
2016-04-01
With the increased number of Galileo navigation satellites joining the Global Navigation Satellite Systems (GNSS) service, there is a strong need for estimating their differential code biases (DCBs) for high-precision GNSS applications. There have been studies for estimating DCBs based on an external global ionospheric model (GIM) proposed by Montenbruck et al. (2014). In this study, we take a different approach by joining the construction of a GIM and estimating DCB together with multi-GNSS observations, including GPS, the BeiDou navigation system, and the Galileo navigation system (GAL). This approach takes full advantage of the collective strength of the individual systems while maintaining high solution consistency. Daily GAL DCBs were estimated simultaneously with ionospheric model parameters from 3 months' multi-GNSS observations. The stability of the resulting GAL DCB estimates was analyzed in detail. It was found that the standard deviations (STDs) of all satellite DCBs were less than 0.17 ns. For GAL receivers, the STDs were greater than for the satellites, with most values <2 ns. Comparison of the statistics of time-ranged stability of satellite DCBs over different time intervals revealed that the difference in STD between 28 and 7 day intervals was small, with the maximum not exceeding 0.01 ns. In almost all cases, the difference in GAL satellite DCBs between two consecutive days was <0.8 ns. The main conclusion is that based on the stability of the GAL DCBs, only occasional calibration is required. Furthermore, the 30 day-averaged satellite DCBs may satisfy the requirement of high-precision applications depending on the GAL satellite DCBs.
Sales-Cruz, Mauricio; Heitzig, Martina; Cameron, Ian;
2011-01-01
In this chapter the importance of parameter estimation in model development is illustrated through various applications related to reaction systems. In particular, rate constants in a reaction system are obtained through parameter estimation methods. These approaches often require the application...... of optimisation techniques coupled with dynamic solution of the underlying model. Linear and nonlinear approaches to parameter estimation are investigated. There is also the application of maximum likelihood principles in the estimation of parameters, as well as the use of orthogonal collocation to...... generate a set of algebraic equations as the basis for parameter estimation.These approaches are illustrated using estimations of kinetic constants from reaction system models....
Bias and Systematic Change in the Parameter Estimates of Macro-Level Diffusion Models
Christophe Van den Bulte; Lilien, Gary L.
1997-01-01
Studies estimating the Bass model and other macro-level diffusion models with an unknown ceiling feature three curious empirical regularities: (i) the estimated ceiling is often close to the cumulative number of adopters in the last observation period, (ii) the estimated coefficient of social contagion or imitation tends to decrease as one adds later observations to the data set, and (iii) the estimated coefficient of social contagion or imitation tends to decrease systematically as the estim...
Rau, Markus Michael; Paech, Kerstin; Seitz, Stella
2016-01-01
Photometric redshift uncertainties are a major source of systematic error for ongoing and future photometric surveys. We study different sources of redshift error caused by common suboptimal binning techniques and propose methods to resolve them. The selection of a too large bin width is shown to oversmooth small scale structure of the radial distribution of galaxies. This systematic error can significantly shift cosmological parameter constraints by up to $6 \\, \\sigma$ for the dark energy equation of state parameter $w$. Careful selection of bin width can reduce this systematic by a factor of up to 6 as compared with commonly used current binning approaches. We further discuss a generalised resampling method that can correct systematic and statistical errors in cosmological parameter constraints caused by uncertainties in the redshift distribution. This can be achieved without any prior assumptions about the shape of the distribution or the form of the redshift error. Our methodology allows photometric surve...
How serious can the stealth bias be in gravitational wave parameter estimation?
Vitale, Salvatore
2013-01-01
The upcoming direct detection of gravitational waves will open a window to probing the strong-field regime of general relativity (GR). As a consequence, waveforms that include the presence of deviations from GR have been developed (e.g. in the parametrized post-Einsteinian approach). TIGER, a data analysis pipeline which builds Bayesian evidence to support or question the validity of GR, has been written and tested. In particular, it was shown recently that data from the LIGO and Virgo detectors will allow to detect deviations from GR smaller than can be probed with Solar System tests and pulsar timing measurements or not accessible with conventional tests of GR. However, evidence from several detections is required before a deviation from GR can be confidently claimed. An interesting consequence is that, should GR not be the correct theory of gravity in its strong field regime, using standard GR templates for the matched filter analysis of interferometer data will introduce biases in the gravitational wave m...
Maximum likelihood estimation of ancestral codon usage bias parameters in Drosophila
Nielsen, Rasmus; Bauer DuMont, Vanessa L; Hubisz, Melissa J;
2007-01-01
selection coefficient for optimal codon usage (S), allowing joint maximum likelihood estimation of S and the dN/dS ratio. We apply the method to previously published data from Drosophila melanogaster, Drosophila simulans, and Drosophila yakuba and show, in accordance with previous results, that the D....... melanogaster lineage has experienced a reduction in the selection for optimal codon usage. However, the D. melanogaster lineage has also experienced a change in the biological mutation rates relative to D. simulans, in particular, a relative reduction in the mutation rate from A to G and an increase in the...... mutation rate from C to T. However, neither a reduction in the strength of selection nor a change in the mutational pattern can alone explain all of the data observed in the D. melanogaster lineage. For example, we also confirm previous results showing that the Notch locus has experienced positive...
Bootstrap bias-adjusted GMM estimators
Ramalho, Joaquim J.S.
2005-01-01
The ability of six alternative bootstrap methods to reduce the bias of GMM parameter estimates is examined in an instrumental variable framework using Monte Carlo analysis. Promising results were found for the two bootstrap estimators suggested in the paper.
Hamann, Jan; Hannestad, Steen; Melchiorri, Alessandro; Wong, Yvonne Y. Y.
2008-07-01
We explore and compare the performances of two non-linear correction and scale-dependent biasing models for the extraction of cosmological information from galaxy power spectrum data, especially in the context of beyond-ΛCDM (CDM: cold dark matter) cosmologies. The first model is the well known Q model, first applied in the analysis of Two-degree Field Galaxy Redshift Survey data. The second, the P model, is inspired by the halo model, in which non-linear evolution and scale-dependent biasing are encapsulated in a single non-Poisson shot noise term. We find that while the two models perform equally well in providing adequate correction for a range of galaxy clustering data in standard ΛCDM cosmology and in extensions with massive neutrinos, the Q model can give unphysical results in cosmologies containing a subdominant free-streaming dark matter whose temperature depends on the particle mass, e.g., relic thermal axions, unless a suitable prior is imposed on the correction parameter. This last case also exposes the danger of analytic marginalization, a technique sometimes used in the marginalization of nuisance parameters. In contrast, the P model suffers no undesirable effects, and is the recommended non-linear correction model also because of its physical transparency.
We explore and compare the performances of two non-linear correction and scale-dependent biasing models for the extraction of cosmological information from galaxy power spectrum data, especially in the context of beyond-ΛCDM (CDM: cold dark matter) cosmologies. The first model is the well known Q model, first applied in the analysis of Two-degree Field Galaxy Redshift Survey data. The second, the P model, is inspired by the halo model, in which non-linear evolution and scale-dependent biasing are encapsulated in a single non-Poisson shot noise term. We find that while the two models perform equally well in providing adequate correction for a range of galaxy clustering data in standard ΛCDM cosmology and in extensions with massive neutrinos, the Q model can give unphysical results in cosmologies containing a subdominant free-streaming dark matter whose temperature depends on the particle mass, e.g., relic thermal axions, unless a suitable prior is imposed on the correction parameter. This last case also exposes the danger of analytic marginalization, a technique sometimes used in the marginalization of nuisance parameters. In contrast, the P model suffers no undesirable effects, and is the recommended non-linear correction model also because of its physical transparency
Hamann, Jan; Melchiorri, Alessandro; Wong, Yvonne Y Y
2008-01-01
We explore and compare the performances of two nonlinear correction and scale-dependent biasing models for the extraction of cosmological information from galaxy power spectrum data, especially in the context of beyond-LCDM cosmologies. The first model is the well known Q model, first applied in the analysis of 2dFGRS data. The second, the P model, is inspired by the halo model, in which nonlinear evolution and scale-dependent biasing are encapsulated in a single non-Poisson shot noise term. We find that while both models perform equally well in providing adequate correction for a range of galaxy clustering data in standard LCDM cosmology and in extensions with massive neutrinos, the Q model can give unphysical results in cosmologies containing a subdominant free-streaming dark matter whose temperature depends on the particle mass, e.g., relic thermal axions, unless a suitable prior is imposed on the correction parameter. This last case also exposes the danger of analytic marginalisation, a technique sometime...
Recursive bias estimation for high dimensional smoothers
Hengartner, Nicolas W [Los Alamos National Laboratory; Matzner-lober, Eric [UHB, FRANCE; Cornillon, Pierre - Andre [INRA
2008-01-01
In multivariate nonparametric analysis, sparseness of the covariates also called curse of dimensionality, forces one to use large smoothing parameters. This leads to biased smoothers. Instead of focusing on optimally selecting the smoothing parameter, we fix it to some reasonably large value to ensure an over-smoothing of the data. The resulting smoother has a small variance but a substantial bias. In this paper, we propose to iteratively correct the bias initial estimator by an estimate of the latter obtained by smoothing the residuals. We examine in detail the convergence of the iterated procedure for classical smoothers and relate our procedure to L{sub 2}-Boosting. We apply our method to simulated and real data and show that our method compares favorably with existing procedures.
The estimation method of GPS instrumental biases
无
2001-01-01
A model of estimating the global positioning system (GPS) instrumental biases and the methods to calculate the relative instrumental biases of satellite and receiver are presented. The calculated results of GPS instrumental biases, the relative instrumental biases of satellite and receiver, and total electron content (TEC) are also shown. Finally, the stability of GPS instrumental biases as well as that of satellite and receiver instrumental biases are evaluated, indicating that they are very stable during a period of two months and a half.
Spatial Bias in Field-Estimated Unsaturated Hydraulic Properties
HOLT,ROBERT M.; WILSON,JOHN L.; GLASS JR.,ROBERT J.
2000-12-21
Hydraulic property measurements often rely on non-linear inversion models whose errors vary between samples. In non-linear physical measurement systems, bias can be directly quantified and removed using calibration standards. In hydrologic systems, field calibration is often infeasible and bias must be quantified indirectly. We use a Monte Carlo error analysis to indirectly quantify spatial bias in the saturated hydraulic conductivity, K{sub s}, and the exponential relative permeability parameter, {alpha}, estimated using a tension infiltrometer. Two types of observation error are considered, along with one inversion-model error resulting from poor contact between the instrument and the medium. Estimates of spatial statistics, including the mean, variance, and variogram-model parameters, show significant bias across a parameter space representative of poorly- to well-sorted silty sand to very coarse sand. When only observation errors are present, spatial statistics for both parameters are best estimated in materials with high hydraulic conductivity, like very coarse sand. When simple contact errors are included, the nature of the bias changes dramatically. Spatial statistics are poorly estimated, even in highly conductive materials. Conditions that permit accurate estimation of the statistics for one of the parameters prevent accurate estimation for the other; accurate regions for the two parameters do not overlap in parameter space. False cross-correlation between estimated parameters is created because estimates of K{sub s} also depend on estimates of {alpha} and both parameters are estimated from the same data.
Bias in parametric estimation: Reduction and useful side-effects
Kosmidis, I.
2014-01-01
The bias of an estimator is defined as the difference of its expected value from the parameter to be estimated, where the expectation is with respect to the model. Loosely speaking, small bias reflects the desire that if an experiment is repeated indefinitely then the average of all the resultant estimates will be close to the parameter value that is estimated. The current article is a review of the still-expanding repository of methods that have been developed to reduce bias in the estimatio...
A generic algorithm for reducing bias in parametric estimation
Kosmidis, I.; Firth, D
2010-01-01
A general iterative algorithm is developed for the computation of reduced-bias parameter estimates in regular statistical models through adjustments to the score function. The algorithm unifies and provides appealing new interpretation for iterative methods that have been published previously for some specific model classes. The new algorithm can use fully be viewed as a series of iterative bias corrections, thus facilitating the adjusted score approach to bias reduction in any model for whic...
Statistical framework for estimating GNSS bias
Vierinen, Juha; Coster, Anthea J.; Rideout, William C.; Erickson, Philip J.; Norberg, Johannes
2016-03-01
We present a statistical framework for estimating global navigation satellite system (GNSS) non-ionospheric differential time delay bias. The biases are estimated by examining differences of measured line-integrated electron densities (total electron content: TEC) that are scaled to equivalent vertical integrated densities. The spatiotemporal variability, instrumentation-dependent errors, and errors due to inaccurate ionospheric altitude profile assumptions are modeled as structure functions. These structure functions determine how the TEC differences are weighted in the linear least-squares minimization procedure, which is used to produce the bias estimates. A method for automatic detection and removal of outlier measurements that do not fit into a model of receiver bias is also described. The same statistical framework can be used for a single receiver station, but it also scales to a large global network of receivers. In addition to the Global Positioning System (GPS), the method is also applicable to other dual-frequency GNSS systems, such as GLONASS (Globalnaya Navigazionnaya Sputnikovaya Sistema). The use of the framework is demonstrated in practice through several examples. A specific implementation of the methods presented here is used to compute GPS receiver biases for measurements in the MIT Haystack Madrigal distributed database system. Results of the new algorithm are compared with the current MIT Haystack Observatory MAPGPS (MIT Automated Processing of GPS) bias determination algorithm. The new method is found to produce estimates of receiver bias that have reduced day-to-day variability and more consistent coincident vertical TEC values.
Statistical framework for estimating GNSS bias
Vierinen, Juha; Rideout, William C; Erickson, Philip J; Norberg, Johannes
2015-01-01
We present a statistical framework for estimating global navigation satellite system (GNSS) non-ionospheric differential time delay bias. The biases are estimated by examining differences of measured line integrated electron densities (TEC) that are scaled to equivalent vertical integrated densities. The spatio-temporal variability, instrumentation dependent errors, and errors due to inaccurate ionospheric altitude profile assumptions are modeled as structure functions. These structure functions determine how the TEC differences are weighted in the linear least-squares minimization procedure, which is used to produce the bias estimates. A method for automatic detection and removal of outlier measurements that do not fit into a model of receiver bias is also described. The same statistical framework can be used for a single receiver station, but it also scales to a large global network of receivers. In addition to the Global Positioning System (GPS), the method is also applicable to other dual frequency GNSS s...
Parameter Estimation Through Ignorance
Du, Hailiang
2015-01-01
Dynamical modelling lies at the heart of our understanding of physical systems. Its role in science is deeper than mere operational forecasting, in that it allows us to evaluate the adequacy of the mathematical structure of our models. Despite the importance of model parameters, there is no general method of parameter estimation outside linear systems. A new relatively simple method of parameter estimation for nonlinear systems is presented, based on variations in the accuracy of probability forecasts. It is illustrated on the Logistic Map, the Henon Map and the 12-D Lorenz96 flow, and its ability to outperform linear least squares in these systems is explored at various noise levels and sampling rates. As expected, it is more effective when the forecast error distributions are non-Gaussian. The new method selects parameter values by minimizing a proper, local skill score for continuous probability forecasts as a function of the parameter values. This new approach is easier to implement in practice than alter...
Parameter estimation through ignorance.
Du, Hailiang; Smith, Leonard A
2012-07-01
Dynamical modeling lies at the heart of our understanding of physical systems. Its role in science is deeper than mere operational forecasting, in that it allows us to evaluate the adequacy of the mathematical structure of our models. Despite the importance of model parameters, there is no general method of parameter estimation outside linear systems. A relatively simple method of parameter estimation for nonlinear systems is introduced, based on variations in the accuracy of probability forecasts. It is illustrated on the logistic map, the Henon map, and the 12-dimensional Lorenz96 flow, and its ability to outperform linear least squares in these systems is explored at various noise levels and sampling rates. As expected, it is more effective when the forecast error distributions are non-Gaussian. The method selects parameter values by minimizing a proper, local skill score for continuous probability forecasts as a function of the parameter values. This approach is easier to implement in practice than alternative nonlinear methods based on the geometry of attractors or the ability of the model to shadow the observations. Direct measures of inadequacy in the model, the "implied ignorance," and the information deficit are introduced. PMID:23005513
Phenological Parameters Estimation Tool
McKellip, Rodney D.; Ross, Kenton W.; Spruce, Joseph P.; Smoot, James C.; Ryan, Robert E.; Gasser, Gerald E.; Prados, Donald L.; Vaughan, Ronald D.
2010-01-01
The Phenological Parameters Estimation Tool (PPET) is a set of algorithms implemented in MATLAB that estimates key vegetative phenological parameters. For a given year, the PPET software package takes in temporally processed vegetation index data (3D spatio-temporal arrays) generated by the time series product tool (TSPT) and outputs spatial grids (2D arrays) of vegetation phenological parameters. As a precursor to PPET, the TSPT uses quality information for each pixel of each date to remove bad or suspect data, and then interpolates and digitally fills data voids in the time series to produce a continuous, smoothed vegetation index product. During processing, the TSPT displays NDVI (Normalized Difference Vegetation Index) time series plots and images from the temporally processed pixels. Both the TSPT and PPET currently use moderate resolution imaging spectroradiometer (MODIS) satellite multispectral data as a default, but each software package is modifiable and could be used with any high-temporal-rate remote sensing data collection system that is capable of producing vegetation indices. Raw MODIS data from the Aqua and Terra satellites is processed using the TSPT to generate a filtered time series data product. The PPET then uses the TSPT output to generate phenological parameters for desired locations. PPET output data tiles are mosaicked into a Conterminous United States (CONUS) data layer using ERDAS IMAGINE, or equivalent software package. Mosaics of the vegetation phenology data products are then reprojected to the desired map projection using ERDAS IMAGINE
Inflation and cosmological parameter estimation
Hamann, J.
2007-05-15
In this work, we focus on two aspects of cosmological data analysis: inference of parameter values and the search for new effects in the inflationary sector. Constraints on cosmological parameters are commonly derived under the assumption of a minimal model. We point out that this procedure systematically underestimates errors and possibly biases estimates, due to overly restrictive assumptions. In a more conservative approach, we analyse cosmological data using a more general eleven-parameter model. We find that regions of the parameter space that were previously thought ruled out are still compatible with the data; the bounds on individual parameters are relaxed by up to a factor of two, compared to the results for the minimal six-parameter model. Moreover, we analyse a class of inflation models, in which the slow roll conditions are briefly violated, due to a step in the potential. We show that the presence of a step generically leads to an oscillating spectrum and perform a fit to CMB and galaxy clustering data. We do not find conclusive evidence for a step in the potential and derive strong bounds on quantities that parameterise the step. (orig.)
Revisiting Cosmological parameter estimation
Prasad, Jayanti
2014-01-01
Constraining theoretical models with measuring the parameters of those from cosmic microwave background (CMB) anisotropy data is one of the most active areas in cosmology. WMAP, Planck and other recent experiments have shown that the six parameters standard $\\Lambda$CDM cosmological model still best fits the data. Bayesian methods based on Markov-Chain Monte Carlo (MCMC) sampling have been playing leading role in parameter estimation from CMB data. In one of the recent studies \\cite{2012PhRvD..85l3008P} we have shown that particle swarm optimization (PSO) which is a population based search procedure can also be effectively used to find the cosmological parameters which are best fit to the WMAP seven year data. In the present work we show that PSO not only can find the best-fit point, it can also sample the parameter space quite effectively, to the extent that we can use the same analysis pipeline to process PSO sampled points which is used to process the points sampled by Markov Chains, and get consistent res...
Elimination of Estimation biases in the Software Development
Thamarai . I.
2015-04-01
Full Text Available The software effort estimations are usually too low and the prediction is also a very difficult task as software is intangible in nature. Also the estimation is based on the parameters that are usually partial in nature. It is an important management activity. Despite much research in this area, the accuracy of effort estimation is very low. This results in poor project planning and failure of many software projects. One of the reasons for this poor estimation is that the estimation given by the software developers are affected by some information which do not have any relevance to the calculation of effort. To avoid this, we have proposed a new methodology in which we analyze the relationship between the estimation bias and the various features of developers such as the role in the company, thinking style, experience, education, software development skills, etc. It is found that the estimation bias increases with higher levels of interdependence.
A MORET tool to assist code bias estimation
This new Graphical User Interface (GUI) developed in JAVA is one of the post-processing tools for MORET4 code. It aims to help users to estimate the importance of the keff bias due to the code in order to better define the upper safety limit. Moreover, it allows visualizing the distance between an actual configuration case and evaluated critical experiments. This tool depends on a validated experiments database, on sets of physical parameters and on various statistical tools allowing interpolating the calculation bias of the database or displaying the projections of experiments on a reduced base of parameters. The development of this tool is still in progress. (author)
Correcting for bias in estimation of quantitative trait loci effects
Ron Micha
2005-09-01
Full Text Available Abstract Estimates of quantitative trait loci (QTL effects derived from complete genome scans are biased, if no assumptions are made about the distribution of QTL effects. Bias should be reduced if estimates are derived by maximum likelihood, with the QTL effects sampled from a known distribution. The parameters of the distributions of QTL effects for nine economic traits in dairy cattle were estimated from a daughter design analysis of the Israeli Holstein population including 490 marker-by-sire contrasts. A separate gamma distribution was derived for each trait. Estimates for both the α and β parameters and their SE decreased as a function of heritability. The maximum likelihood estimates derived for the individual QTL effects using the gamma distributions for each trait were regressed relative to the least squares estimates, but the regression factor decreased as a function of the least squares estimate. On simulated data, the mean of least squares estimates for effects with nominal 1% significance was more than twice the simulated values, while the mean of the maximum likelihood estimates was slightly lower than the mean of the simulated values. The coefficient of determination for the maximum likelihood estimates was five-fold the corresponding value for the least squares estimates.
Simultaneous quaternion estimation (QUEST) and bias determination
Markley, F. Landis
1989-01-01
Tests of a new method for the simultaneous estimation of spacecraft attitude and sensor biases, based on a quaternion estimation algorithm minimizing Wahba's loss function are presented. The new method is compared with a conventional batch least-squares differential correction algorithm. The estimates are based on data from strapdown gyros and star trackers, simulated with varying levels of Gaussian noise for both inertially-fixed and Earth-pointing reference attitudes. Both algorithms solve for the spacecraft attitude and the gyro drift rate biases. They converge to the same estimates at the same rate for inertially-fixed attitude, but the new algorithm converges more slowly than the differential correction for Earth-pointing attitude. The slower convergence of the new method for non-zero attitude rates is believed to be due to the use of an inadequate approximation for a partial derivative matrix. The new method requires about twice the computational effort of the differential correction. Improving the approximation for the partial derivative matrix in the new method is expected to improve its convergence at the cost of increased computational effort.
Bayesian Estimation of Combined Accuracy for Tests with Verification Bias
Lyle D. Broemeling
2011-12-01
Full Text Available This presentation will emphasize the estimation of the combined accuracy of two or more tests when verification bias is present. Verification bias occurs when some of the subjects are not subject to the gold standard. The approach is Bayesian where the estimation of test accuracy is based on the posterior distribution of the relevant parameter. Accuracy of two combined binary tests is estimated employing either “believe the positive” or “believe the negative” rule, then the true and false positive fractions for each rule are computed for two tests. In order to perform the analysis, the missing at random assumption is imposed, and an interesting example is provided by estimating the combined accuracy of CT and MRI to diagnose lung cancer. The Bayesian approach is extended to two ordinal tests when verification bias is present, and the accuracy of the combined tests is based on the ROC area of the risk function. An example involving mammography with two readers with extreme verification bias illustrates the estimation of the combined test accuracy for ordinal tests.
Blind estimation of compartmental model parameters
Computation of physiologically relevant kinetic parameters from dynamic PET or SPECT imaging requires knowledge of the blood input function. This work is concerned with developing methods to accurately estimate these kinetic parameters blindly; that is, without use of a directly measured blood input function. Instead, only measurements of the output functions - the tissue time-activity curves - are used. The blind estimation method employed here minimizes a set of cross-relation equations, from which the blood term has been factored out, to determine compartmental model parameters. The method was tested with simulated data appropriate for dynamic SPECT cardiac perfusion imaging with 99mTc-teboroxime and for dynamic PET cerebral blood flow imaging with 15O water. The simulations did not model the tomographic process. Noise levels typical of the respective modalities were employed. From three to eight different regions were simulated, each with different time-activity curves. The time-activity curve (24 or 70 time points) for each region was simulated with a compartment model. The simulation used a biexponential blood input function and washin rates between 0.2 and 1.3 min-1 and washout rates between 0.2 and 1.0 min-1. The system of equations was solved numerically and included constraints to bound the range of possible solutions. From the cardiac simulations, washin was determined to within a scale factor of the true washin parameters with less than 6% bias and 12% variability. 99mTc-teboroxime washout results had less than 5% bias, but variability ranged from 14% to 43%. The cerebral blood flow washin parameters were determined with less than 5% bias and 4% variability. The washout parameters were determined with less than 4% bias, but had 15-30% variability. Since washin is often the parameter of most use in clinical studies, the blind estimation approach may eliminate the current necessity of measuring the input function when performing certain dynamic studies
Blind estimation of compartmental model parameters.
Di Bella, E V; Clackdoyle, R; Gullberg, G T
1999-03-01
Computation of physiologically relevant kinetic parameters from dynamic PET or SPECT imaging requires knowledge of the blood input function. This work is concerned with developing methods to accurately estimate these kinetic parameters blindly; that is, without use of a directly measured blood input function. Instead, only measurements of the output functions--the tissue time-activity curves--are used. The blind estimation method employed here minimizes a set of cross-relation equations, from which the blood term has been factored out, to determine compartmental model parameters. The method was tested with simulated data appropriate for dynamic SPECT cardiac perfusion imaging with 99mTc-teboroxime and for dynamic PET cerebral blood flow imaging with 15O water. The simulations did not model the tomographic process. Noise levels typical of the respective modalities were employed. From three to eight different regions were simulated, each with different time-activity curves. The time-activity curve (24 or 70 time points) for each region was simulated with a compartment model. The simulation used a biexponential blood input function and washin rates between 0.2 and 1.3 min(-1) and washout rates between 0.2 and 1.0 min(-1). The system of equations was solved numerically and included constraints to bound the range of possible solutions. From the cardiac simulations, washin was determined to within a scale factor of the true washin parameters with less than 6% bias and 12% variability. 99mTc-teboroxime washout results had less than 5% bias, but variability ranged from 14% to 43%. The cerebral blood flow washin parameters were determined with less than 5% bias and 4% variability. The washout parameters were determined with less than 4% bias, but had 15-30% variability. Since washin is often the parameter of most use in clinical studies, the blind estimation approach may eliminate the current necessity of measuring the input function when performing certain dynamic studies
Estimating Ancestral Population Parameters
Wakeley, J.; Hey, J.
1997-01-01
The expected numbers of different categories of polymorphic sites are derived for two related models of population history: the isolation model, in which an ancestral population splits into two descendents, and the size-change model, in which a single population undergoes an instantaneous change in size. For the isolation model, the observed numbers of shared, fixed, and exclusive polymorphic sites are used to estimate the relative sizes of the three populations, ancestral plus two descendent...
Aswath Damodaran
1999-01-01
Over the last three decades, the capital asset pricing model has occupied a central and often controversial place in most corporate finance analysts’ tool chests. The model requires three inputs to compute expected returns – a riskfree rate, a beta for an asset and an expected risk premium for the market portfolio (over and above the riskfree rate). Betas are estimated, by most practitioners, by regressing returns on an asset against a stock index, with the slope of the regression being the b...
Recursive bias estimation for high dimensional regression smoothers
Hengartner, Nicolas W [Los Alamos National Laboratory; Cornillon, Pierre - Andre [AGROSUP, FRANCE; Matzner - Lober, Eric [UNIV OF RENNES, FRANCE
2009-01-01
In multivariate nonparametric analysis, sparseness of the covariates also called curse of dimensionality, forces one to use large smoothing parameters. This leads to biased smoother. Instead of focusing on optimally selecting the smoothing parameter, we fix it to some reasonably large value to ensure an over-smoothing of the data. The resulting smoother has a small variance but a substantial bias. In this paper, we propose to iteratively correct of the bias initial estimator by an estimate of the latter obtained by smoothing the residuals. We examine in details the convergence of the iterated procedure for classical smoothers and relate our procedure to L{sub 2}-Boosting, For multivariate thin plate spline smoother, we proved that our procedure adapts to the correct and unknown order of smoothness for estimating an unknown function m belonging to H({nu}) (Sobolev space where m should be bigger than d/2). We apply our method to simulated and real data and show that our method compares favorably with existing procedures.
A Polynomial Prediction Filter Method for Estimating Multisensor Dynamically Varying Biases
无
2007-01-01
The estimation of the sensor measurement biases in a multisensor system is vital for the sensor data fusion. A solution is provided for the estimation of dynamically varying multiple sensor biases without any knowledge of the dynamic bias model parameters. It is shown that the sensor bias pseudomeasurement can be dynamically obtained via a parity vector. This is accomplished by multiplying the sensor uncalibrated measurement equations by a projection matrix so that the measured variable is eliminated from the equations. Once the state equations of the dynamically varying sensor biases are modeled by a polynomial prediction filter, the dynamically varying multisensor biases can be obtained by Kalman filter. Simulation results validate that the proposed method can estimate the constant biases and dynamic biases of multisensors and outperforms the methods reported in literature.
Longman, Richard W.; Bergmann, Martin; Juang, Jer-Nan
1988-01-01
For the ERA system identification algorithm, perturbation methods are used to develop expressions for variance and bias of the identified modal parameters. Based on the statistics of the measurement noise, the variance results serve as confidence criteria by indicating how likely the true parameters are to lie within any chosen interval about their identified values. This replaces the use of expensive and time-consuming Monte Carlo computer runs to obtain similar information. The bias estimates help guide the ERA user in his choice of which data points to use and how much data to use in order to obtain the best results, performing the trade-off between the bias and scatter. Also, when the uncertainty in the bias is sufficiently small, the bias information can be used to correct the ERA results. In addition, expressions for the variance and bias of the singular values serve as tools to help the ERA user decide the proper modal order.
Toward unbiased estimations of the statefinder parameters
Aviles, Alejandro; Luongo, Orlando
2016-01-01
With the use of simulated supernova catalogs, we show that the statefinder parameters turn out to be poorly and biased estimated by standard cosmography. To this end, we compute their standard deviations and several bias statistics on cosmologies near the concordance model, demonstrating that these are very large, making standard cosmography unsuitable for future and wider compilations of data. To overcome this issue, we propose a new method that consists in introducing the series of the Hubble function into the luminosity distance, instead of considering the usual direct Taylor expansions of the luminosity distance. Moreover, in order to speed up the numerical computations, we estimate the coefficients of our expansions in a hierarchical manner, in which the order of the expansion depends on the redshift of every single piece of data. In addition, we propose two hybrids methods that incorporates standard cosmography at low redshifts. The methods presented here perform better than the standard approach of cos...
Sensitivity of hydrologic simulations to bias corrected driving parameters
Papadimitriou, Lamprini; Grillakis, Manolis; Koutroulis, Aristeidis; Tsanis, Ioannis
2016-04-01
Climate model outputs feature systematic errors and biases that render them unsuitable for direct use by the impact models. To deal with this issue many bias correction techniques have been developed to adjust the modelled variables against observations. For the most common applications adjustment concerns only precipitation and temperature whilst for others all the driving parameters (including radiation, wind speed, humidity, air pressure) are bias adjusted. Bias adjusting only part of the variables required as biophysical model input could affect the physical consistency among input variables and is poorly studied. It is important to determine and quantify the effect that bias adjusting each climate variable has on the impact model's simulation and identify parameters that could be treated as raw outputs for specific model applications. In this work, the sensitivity of climate simulations to bias adjusted driving parameters is tested by conducting a series of model runs, for which the impact model JULES is forced with: i) not bias corrected input variables, ii) all bias corrected input variables, iii-viii) all input variables bias corrected except for: iii) precipitation, iv) temperature, v) radiation, vi) specific humidity, vii) air pressure and viii) wind speed. This set of runs is conducted for three climate models of different equilibrium climate sensitivity: GFDL-ESM2M, MIROC-ESM-CHEM and IPSL-CM5A-LR. The baseline for the comparison of the experimental runs is a JULES run forced with the WFDEI dataset, the dataset that was used as the observational dataset for adjusting biases. The comparative analysis is performed using the time period 1981-2010 and focusing on output variables of the hydrological cycle (runoff, evapotranspiration, soil moisture).
Estimating and Correcting Bias in Stereo Visual Odometry
Farboud-Sheshdeh, Sara
Stereo visual odometry (VO) is a common technique for estimating a camera's motion; features are tracked across frames and the pose change is subsequently inferred. This method can play a particularly important role in environments where the global positioning system (GPS) is not available (e.g., Mars rovers). Recently, some authors have noticed a bias in VO position estimates that grows with distance travelled; this can cause the resulting estimate to become highly inaccurate. In this thesis, two effects are identified at play in stereo VO bias: first, the inherent bias in the maximum-likelihood estimation framework, and second, the disparity threshold used to discard far-away and erroneous observations. To estimate the bias, the sigma-point method (with modification) combined with the concept of bootstrap bias estimation is proposed. This novel method achieves similar accuracy to Monte Carlo experiments, but at a fraction of the computational cost. The approach is validated through simulations.
Photo-z Estimation: An Example of Nonparametric Conditional Density Estimation under Selection Bias
Izbicki, Rafael; Freeman, Peter E
2016-01-01
Redshift is a key quantity for inferring cosmological model parameters. In photometric redshift estimation, cosmologists use the coarse data collected from the vast majority of galaxies to predict the redshift of individual galaxies. To properly quantify the uncertainty in the predictions, however, one needs to go beyond standard regression and instead estimate the full conditional density f(z|x) of a galaxy's redshift z given its photometric covariates x. The problem is further complicated by selection bias: usually only the rarest and brightest galaxies have known redshifts, and these galaxies have characteristics and measured covariates that do not necessarily match those of more numerous and dimmer galaxies of unknown redshift. Unfortunately, there is not much research on how to best estimate complex multivariate densities in such settings. Here we describe a general framework for properly constructing and assessing nonparametric conditional density estimators under selection bias, and for combining two o...
Why is "S" a Biased Estimate of [sigma]?
Sanqui, Jose Almer T.; Arnholt, Alan T.
2011-01-01
This article describes a simulation activity that can be used to help students see that the estimator "S" is a biased estimator of [sigma]. The activity can be implemented using either a statistical package such as R, Minitab, or a Web applet. In the activity, the students investigate and compare the bias of "S" when sampling from different…
Recursive bias estimation and L2 boosting
Hengartner, Nicolas W [Los Alamos National Laboratory; Cornillon, Pierre - Andre [INRA, FRANCE; Matzner - Lober, Eric [RENNE, FRANCE
2009-01-01
This paper presents a general iterative bias correction procedure for regression smoothers. This bias reduction schema is shown to correspond operationally to the L{sub 2} Boosting algorithm and provides a new statistical interpretation for L{sub 2} Boosting. We analyze the behavior of the Boosting algorithm applied to common smoothers S which we show depend on the spectrum of I - S. We present examples of common smoother for which Boosting generates a divergent sequence. The statistical interpretation suggest combining algorithm with an appropriate stopping rule for the iterative procedure. Finally we illustrate the practical finite sample performances of the iterative smoother via a simulation study.
Bayesian parameter estimation for effective field theories
Wesolowski, S; Furnstahl, R J; Phillips, D R; Thapaliya, A
2015-01-01
We present procedures based on Bayesian statistics for effective field theory (EFT) parameter estimation from data. The extraction of low-energy constants (LECs) is guided by theoretical expectations that supplement such information in a quantifiable way through the specification of Bayesian priors. A prior for natural-sized LECs reduces the possibility of overfitting, and leads to a consistent accounting of different sources of uncertainty. A set of diagnostic tools are developed that analyze the fit and ensure that the priors do not bias the EFT parameter estimation. The procedures are illustrated using representative model problems and the extraction of LECs for the nucleon mass expansion in SU(2) chiral perturbation theory from synthetic lattice data.
Bayesian parameter estimation for effective field theories
Wesolowski, S.; Klco, N.; Furnstahl, R. J.; Phillips, D. R.; Thapaliya, A.
2016-07-01
We present procedures based on Bayesian statistics for estimating, from data, the parameters of effective field theories (EFTs). The extraction of low-energy constants (LECs) is guided by theoretical expectations in a quantifiable way through the specification of Bayesian priors. A prior for natural-sized LECs reduces the possibility of overfitting, and leads to a consistent accounting of different sources of uncertainty. A set of diagnostic tools is developed that analyzes the fit and ensures that the priors do not bias the EFT parameter estimation. The procedures are illustrated using representative model problems, including the extraction of LECs for the nucleon-mass expansion in SU(2) chiral perturbation theory from synthetic lattice data.
A New Bias Corrected Version of Heteroscedasticity Consistent Covariance Estimator
Munir Ahmed
2016-06-01
Full Text Available In the presence of heteroscedasticity, different available flavours of the heteroscedasticity consistent covariance estimator (HCCME are used. However, the available literature shows that these estimators can be considerably biased in small samples. Cribari–Neto et al. (2000 introduce a bias adjustment mechanism and give the modified White estimator that becomes almost bias-free even in small samples. Extending these results, Cribari-Neto and Galvão (2003 present a similar bias adjustment mechanism that can be applied to a wide class of HCCMEs’. In the present article, we follow the same mechanism as proposed by Cribari-Neto and Galvão to give bias-correction version of HCCME but we use adaptive HCCME rather than the conventional HCCME. The Monte Carlo study is used to evaluate the performance of our proposed estimators.
Bias in Estimation and Hypothesis Testing of Correlation
Zimmerman D. W.; Zumbo B. D.; Williams R. H.
2003-01-01
This study examined bias in the sample correlation coefficient, r, and its correction by unbiased estimators. Computer simulations revealed that the expected value of correlation coefficients in samples from a normal population is slightly less than the population correlation, ρ, and that the bias is almost eliminated by an estimator suggested by R.A. Fisher and is more completely eliminated by a related estimator recommended by Olkin and Pratt. Transfor...
Parameter estimation and inverse problems
Aster, Richard C; Thurber, Clifford H
2005-01-01
Parameter Estimation and Inverse Problems primarily serves as a textbook for advanced undergraduate and introductory graduate courses. Class notes have been developed and reside on the World Wide Web for faciliting use and feedback by teaching colleagues. The authors'' treatment promotes an understanding of fundamental and practical issus associated with parameter fitting and inverse problems including basic theory of inverse problems, statistical issues, computational issues, and an understanding of how to analyze the success and limitations of solutions to these probles. The text is also a practical resource for general students and professional researchers, where techniques and concepts can be readily picked up on a chapter-by-chapter basis.Parameter Estimation and Inverse Problems is structured around a course at New Mexico Tech and is designed to be accessible to typical graduate students in the physical sciences who may not have an extensive mathematical background. It is accompanied by a Web site that...
Parameter Estimation Using VLA Data
Venter, Willem C.
The main objective of this dissertation is to extract parameters from multiple wavelength images, on a pixel-to-pixel basis, when the images are corrupted with noise and a point spread function. The data used are from the field of radio astronomy. The very large array (VLA) at Socorro in New Mexico was used to observe planetary nebula NGC 7027 at three different wavelengths, 2 cm, 6 cm and 20 cm. A temperature model, describing the temperature variation in the nebula as a function of optical depth, is postulated. Mathematical expressions for the brightness distribution (flux density) of the nebula, at the three observed wavelengths, are obtained. Using these three equations and the three data values available, one from the observed flux density map at each wavelength, it is possible to solve for two temperature parameters and one optical depth parameter at each pixel location. Due to the fact that the number of unknowns equal the number of equations available, estimation theory cannot be used to smooth any noise present in the data values. It was found that a direct solution of the three highly nonlinear flux density equations is very sensitive to noise in the data. Results obtained from solving for the three unknown parameters directly, as discussed above, were not physical realizable. This was partly due to the effect of incomplete sampling at the time when the data were gathered and to noise in the system. The application of rigorous digital parameter estimation techniques result in estimated parameters that are also not physically realizable. The estimated values for the temperature parameters are for example either too high or negative, which is not physically possible. Simulation studies have shown that a "double smoothing" technique improves the results by a large margin. This technique consists of two parts: in the first part the original observed data are smoothed using a running window and in the second part a similar smoothing of the estimated parameters
GEODYN- ORBITAL AND GEODETIC PARAMETER ESTIMATION
Putney, B.
1994-01-01
The Orbital and Geodetic Parameter Estimation program, GEODYN, possesses the capability to estimate that set of orbital elements, station positions, measurement biases, and a set of force model parameters such that the orbital tracking data from multiple arcs of multiple satellites best fits the entire set of estimation parameters. The estimation problem can be divided into two parts: the orbit prediction problem, and the parameter estimation problem. GEODYN solves these two problems by employing Cowell's method for integrating the orbit and a Bayesian least squares statistical estimation procedure for parameter estimation. GEODYN has found a wide range of applications including determination of definitive orbits, tracking instrumentation calibration, satellite operational predictions, and geodetic parameter estimation, such as the estimations for global networks of tracking stations. The orbit prediction problem may be briefly described as calculating for some later epoch the new conditions of state for the satellite, given a set of initial conditions of state for some epoch, and the disturbing forces affecting the motion of the satellite. The user is required to supply only the initial conditions of state and GEODYN will provide the forcing function and integrate the equations of motion of the satellite. Additionally, GEODYN performs time and coordinate transformations to insure the continuity of operations. Cowell's method of numerical integration is used to solve the satellite equations of motion and the variational partials for force model parameters which are to be adjusted. This method uses predictor-corrector formulas for the equations of motion and corrector formulas only for the variational partials. The parameter estimation problem is divided into three separate parts: 1) instrument measurement modeling and partial derivative computation, 2) data error correction, and 3) statistical estimation of the parameters. Since all of the measurements modeled by
Load Estimation from Modal Parameters
Aenlle, Manuel López; Brincker, Rune; Fernández, Pelayo Fernández; Canteli, Alfonso Fernández
In Natural Input Modal Analysis the modal parameters are estimated just from the responses while the loading is not recorded. However, engineers are sometimes interested in knowing some features of the loading acting on a structure. In this paper, a procedure to determine the loading from a FRF...... matrix assembled from modal parameters and the experimental responses recorded using standard sensors, is presented. The method implies the inversion of the FRF which, in general, is not full rank matrix due to the truncation of the modal space. Furthermore, some ecommendations are included to improve...
Guerre, Emmanuel; Sabbah, Camille
2011-01-01
This paper investigates the bias and the weak Bahadur representation of a local polynomial estimator of the conditional quantile function and its derivatives. The bias and Bahadur remainder term are studied uniformly with respect to the quantile level, the covariates and the smoothing parameter. The order of the local polynomial estimator can be higher than the differentiability order of the conditional quantile function. Applications of the results deal with global optimal consistency rates ...
Cosmological parameter estimation: impact of CMB aberration
The peculiar motion of an observer with respect to the CMB rest frame induces an apparent deflection of the observed CMB photons, i.e. aberration, and a shift in their frequency, i.e. Doppler effect. Both effects distort the temperature multipoles alm's via a mixing matrix at any l. The common lore when performing a CMB based cosmological parameter estimation is to consider that Doppler affects only the l = 1 multipole, and neglect any other corrections. In this paper we reconsider the validity of this assumption, showing that it is actually not robust when sky cuts are included to model CMB foreground contaminations. Assuming a simple fiducial cosmological model with five parameters, we simulated CMB temperature maps of the sky in a WMAP-like and in a Planck-like experiment and added aberration and Doppler effects to the maps. We then analyzed with a MCMC in a Bayesian framework the maps with and without aberration and Doppler effects in order to assess the ability of reconstructing the parameters of the fiducial model. We find that, depending on the specific realization of the simulated data, the parameters can be biased up to one standard deviation for WMAP and almost two standard deviations for Planck. Therefore we conclude that in general it is not a solid assumption to neglect aberration in a CMB based cosmological parameter estimation
Bias-corrected estimation of stable tail dependence function
Beirlant, Jan; Escobar-Bach, Mikael; Goegebeur, Yuri;
2016-01-01
We consider the estimation of the stable tail dependence function. We propose a bias-corrected estimator and we establish its asymptotic behaviour under suitable assumptions. The finite sample performance of the proposed estimator is evaluated by means of an extensive simulation study where a...
Estimation and adjustment of self-selection bias in volunteer panel web surveys
Niu, Chengying
2016-06-01
By using propensity score matching method of random sample, we matched simple random sample units and volunteer panel Web survey sample units based on the equal or similar propensity score. The unbiased estimators of the population parameters are constructed by using the matching simple random sample, and the self-selection bias is estimated. We propose propensity score weighted and matching sample post stratification weighted methods to estimate the population parameters, and the self-selection bias in volunteer panel Web Surveys are adjusted.
Assessment of bias in US waterfowl harvest estimates
Padding, Paul I.; Royle, J. Andrew
2012-01-01
Context. North American waterfowl managers have long suspected that waterfowl harvest estimates derived from national harvest surveys in the USA are biased high. Survey bias can be evaluated by comparing survey results with like estimates from independent sources. Aims. We used band-recovery data to assess the magnitude of apparent bias in duck and goose harvest estimates, using mallards (Anas platyrhynchos) and Canada geese (Branta canadensis) as representatives of ducks and geese, respectively. Methods. We compared the number of reported mallard and Canada goose band recoveries, adjusted for band reporting rates, with the estimated harvests of banded mallards and Canada geese from the national harvest surveys. Weused the results of those comparisons to develop correction factors that can be applied to annual duck and goose harvest estimates of the national harvest survey. Key results. National harvest survey estimates of banded mallards harvested annually averaged 1.37 times greater than those calculated from band-recovery data, whereas Canada goose harvest estimates averaged 1.50 or 1.63 times greater than comparable band-recovery estimates, depending on the harvest survey methodology used. Conclusions. Duck harvest estimates produced by the national harvest survey from 1971 to 2010 should be reduced by a factor of 0.73 (95% CI = 0.71–0.75) to correct for apparent bias. Survey-specific correction factors of 0.67 (95% CI = 0.65–0.69) and 0.61 (95% CI = 0.59–0.64) should be applied to the goose harvest estimates for 1971–2001 (duck stamp-based survey) and 1999–2010 (HIP-based survey), respectively. Implications. Although this apparent bias likely has not influenced waterfowl harvest management policy in the USA, it does have negative impacts on some applications of harvest estimates, such as indirect estimation of population size. For those types of analyses, we recommend applying the appropriate correction factor to harvest estimates.
Estimation of Synchronous Machine Parameters
Oddvar Hallingstad
1980-01-01
Full Text Available The present paper gives a short description of an interactive estimation program based on the maximum likelihood (ML method. The program may also perform identifiability analysis by calculating sensitivity functions and the Hessian matrix. For the short circuit test the ML method is able to estimate the q-axis subtransient reactance x''q, which is not possible by means of the conventional graphical method (another set of measurements has to be used. By means of the synchronization and close test, the ML program can estimate the inertial constant (M, the d-axis transient open circuit time constant (T'do, the d-axis subtransient o.c.t.c (T''do and the q-axis subtransient o.c.t.c (T''qo. In particular, T''qo is difficult to estimate by any of the methods at present in use. Parameter identifiability is thoroughly examined both analytically and by numerical methods. Measurements from a small laboratory machine are used.
Parameter estimation and inverse problems
Aster, Richard C; Thurber, Clifford H
2011-01-01
Parameter Estimation and Inverse Problems, 2e provides geoscience students and professionals with answers to common questions like how one can derive a physical model from a finite set of observations containing errors, and how one may determine the quality of such a model. This book takes on these fundamental and challenging problems, introducing students and professionals to the broad range of approaches that lie in the realm of inverse theory. The authors present both the underlying theory and practical algorithms for solving inverse problems. The authors' treatment is approp
Network Structure and Biased Variance Estimation in Respondent Driven Sampling
Verdery, Ashton M.; Mouw, Ted; Bauldry, Shawn; Mucha, Peter J.
2015-01-01
This paper explores bias in the estimation of sampling variance in Respondent Driven Sampling (RDS). Prior methodological work on RDS has focused on its problematic assumptions and the biases and inefficiencies of its estimators of the population mean. Nonetheless, researchers have given only slight attention to the topic of estimating sampling variance in RDS, despite the importance of variance estimation for the construction of confidence intervals and hypothesis tests. In this paper, we show that the estimators of RDS sampling variance rely on a critical assumption that the network is First Order Markov (FOM) with respect to the dependent variable of interest. We demonstrate, through intuitive examples, mathematical generalizations, and computational experiments that current RDS variance estimators will always underestimate the population sampling variance of RDS in empirical networks that do not conform to the FOM assumption. Analysis of 215 observed university and school networks from Facebook and Add Health indicates that the FOM assumption is violated in every empirical network we analyze, and that these violations lead to substantially biased RDS estimators of sampling variance. We propose and test two alternative variance estimators that show some promise for reducing biases, but which also illustrate the limits of estimating sampling variance with only partial information on the underlying population social network. PMID:26679927
Bias correction of satellite rainfall estimation using a radar-gauge product
K. Tesfagiorgis
2010-11-01
Full Text Available Satellite rainfall estimates can be used in operational hydrologic prediction, but are prone to systematic errors. The goal of this study is to seamlessly blend a radar-gauge product with a corrected satellite product that fills gaps in radar coverage. To blend different rainfall products, they should have similar bias features. The paper presents a pixel by pixel method, which aims to correct biases in hourly satellite rainfall products using a radar-gauge rainfall product. Bias factors are calculated for corresponding rainy pixels, and a desired number of them are randomly selected for the analysis. Bias fields are generated using the selected bias factors. The method takes into account spatial variation and random errors in biases. Bias field parameters were determined on a daily basis using the Shuffled Complex Evolution optimization algorithm. To include more sources of errors, ensembles of bias factors were generated and applied before bias field generation. The procedure of the method was demonstrated using a satellite and a radar-gauge rainfall data for several rainy events in 2006 for the Oklahoma region. The method was compared with bias corrections using interpolation without ensembles, the ratio of mean and maximum ratio. Results show the method outperformed the other techniques such as mean ratio, maximum ratio and bias field generation by interpolation.
Attitude and gyro bias estimation for a VTOL UAV
METNI, N; PFLIMLIN, JM; Hamel, T.; SOUERES, P
2006-01-01
In this paper, a nonlinear complementary filter (x-estimator) is presented to estimate the attitude of a vertical take off and landing unmanned aerial vehicle (VTOL UAV). The measurements are taken from a low-cost IMU (inertial measurement unit) which consists of 3-axis accelerometers and 3-axis gyroscopes. The gyro bias are estimated online. A second nonlinear complementary filter (z-estimator) which combines 3-axis gyroscope readings with 3-axis magnetometer measurements, is also designed. ...
Weighted Mixed Regression Estimation Under Biased Stochastic Restrictions
---, Shalabh; Heumann, Christian
2007-01-01
The paper considers the construction of estimators of regression coefficients in a linear regression model when some stochastic and biased apriori information is available. Such apriori information is framed as stochastic restrictions. The dominance conditions of the estimators are derived under the criterion of mean squared error matrix.
Applied parameter estimation for chemical engineers
Englezos, Peter
2000-01-01
Formulation of the parameter estimation problem; computation of parameters in linear models-linear regression; Gauss-Newton method for algebraic models; other nonlinear regression methods for algebraic models; Gauss-Newton method for ordinary differential equation (ODE) models; shortcut estimation methods for ODE models; practical guidelines for algorithm implementation; constrained parameter estimation; Gauss-Newton method for partial differential equation (PDE) models; statistical inferences; design of experiments; recursive parameter estimation; parameter estimation in nonlinear thermodynam
Bayesian estimation of one-parameter qubit gates
Teklu, Berihu; Olivares, Stefano; Paris, Matteo G. A.
2008-01-01
We address estimation of one-parameter unitary gates for qubit systems and seek for optimal probes and measurements. Single- and two-qubit probes are analyzed in details focusing on precision and stability of the estimation procedure. Bayesian inference is employed and compared with the ultimate quantum limits to precision, taking into account the biased nature of Bayes estimator in the non asymptotic regime. Besides, through the evaluation of the asymptotic a posteriori distribution for the ...
SURFACE VOLUME ESTIMATES FOR INFILTRATION PARAMETER ESTIMATION
Volume balance calculations used in surface irrigation engineering analysis require estimates of surface storage. These calculations are often performed by estimating upstream depth with a normal depth formula. That assumption can result in significant volume estimation errors when upstream flow d...
Data Handling and Parameter Estimation
Sin, Gürkan; Gernaey, Krist
2016-01-01
literature that are mostly based on the ActivatedSludge Model (ASM) framework and their appropriate extensions (Henze et al., 2000).The chapter presents an overview of the most commonly used methods in the estimation of parameters from experimental batch data, namely: (i) data handling and validation, (ii...... spatial scales. At full-scale wastewater treatment plants (WWTPs),mechanistic modelling using the ASM framework and concept (e.g. Henze et al., 2000) has become an important part of the engineering toolbox for process engineers. It supports plant design, operation, optimization and control applications......). Models have also been used as an integral part of the comprehensive analysis and interpretation of data obtained from a range of experimental methods from the laboratory, as well as pilot-scale studies to characterise and study wastewater treatment plants. In this regard, models help to properly explain...
Weak Lensing Peak Finding: Estimators, Filters, and Biases
Schmidt, Fabian
2010-01-01
Large catalogs of shear-selected peaks have recently become a reality. In order to properly interpret the abundance and properties of these peaks, it is necessary to take into account the effects of the clustering of source galaxies, among themselves and with the lens. In addition, the preferred selection of lensed galaxies in a flux- and size-limited sample leads to fluctuations in the apparent source density which correlate with the lensing field (lensing bias). In this paper, we investigate these issues for two different choices of shear estimators which are commonly in use today: globally-normalized and locally-normalized estimators. While in principle equivalent, in practice these estimators respond differently to systematic effects such as lensing bias and cluster member dilution. Furthermore, we find that which estimator is statistically superior depends on the specific shape of the filter employed for peak finding; suboptimal choices of the estimator+filter combination can result in a suppression of t...
A Method for Estimating BeiDou Inter-frequency Satellite Clock Bias
LI Haojun
2016-02-01
Full Text Available A new method for estimating the BeiDou inter-frequency satellite clock bias is proposed, considering the shortage of the current methods. The constant and variable parts of the inter-frequency satellite clock bias are considered in the new method. The data from 10 observation stations are processed to validate the new method. The characterizations of the BeiDou inter-frequency satellite clock bias are also analyzed using the computed results. The results of the BeiDou inter-frequency satellite clock bias indicate that it is stable in the short term. The estimated BeiDou inter-frequency satellite clock bias results are molded. The model results show that the 10 parameters of model for each satellite can express the BeiDou inter-frequency satellite clock bias well and the accuracy reaches cm level. When the model parameters of the first day are used to compute the BeiDou inter-frequency satellite clock bias of the second day, the accuracy also reaches cm level. Based on the stability and modeling, a strategy for the BeiDou satellite clock service is presented to provide the reference of our BeiDou.
Review Of Parameter Estimation Using Adaptive Filtering
LALITA RANI, SHALOO KIKAN
2013-07-01
Full Text Available In this paper, a comparative study of different adaptive filter algorithm for channel parameter estimation is described. We presented different parameter estimation approaches of adaptive filtering. An extended Kalman filter is then applied as a near-optimal solution to the adaptive channel parameter estimation problem. Kalman filtering is applied for motion parameters resulting in optimal pose estimation. A parallel Kalman filter is applied for joint estimation of code delay, multipath gains and Doppler shift. In this paper, a complete review of parameter estimation using adaptive filtering is explained.
Maximum-likelihood fits to histograms for improved parameter estimation
Fowler, Joseph W
2013-01-01
Straightforward methods for adapting the familiar chi^2 statistic to histograms of discrete events and other Poisson distributed data generally yield biased estimates of the parameters of a model. The bias can be important even when the total number of events is large. For the case of estimating a microcalorimeter's energy resolution at 6 keV from the observed shape of the Mn K-alpha fluorescence spectrum, a poor choice of chi^2 can lead to biases of at least 10% in the estimated resolution when up to thousands of photons are observed. The best remedy is a Poisson maximum-likelihood fit, through a simple modification of the standard Levenberg-Marquardt algorithm for chi^2 minimization. Where the modification is not possible, another approach allows iterative approximation of the maximum-likelihood fit.
Impact of Baryonic Processes on Weak Lensing Cosmology: Higher-Order Statistics and Parameter Bias
Osato, Ken; Yoshida, Naoki
2015-01-01
We study the impact of baryonic physics on cosmological parameter estimation with weak lensing surveys. We run a set of cosmological hydrodynamics simulations with different galaxy formation models. We then perform ray-tracing simulations through the total matter density field to generate 100 independent convergence maps of 25 deg$^2$ field-of-view, and use them to examine the ability of the following three lensing statistics as cosmological probes; power spectrum, peak counts, and Minkowski Functionals. For the upcoming wide-field observations such as Subaru Hyper Suprime-Cam (HSC) survey with a sky coverage of 1400 deg$^2$, the higher-order statistics provide tight constraints on the matter density, density fluctuation amplitude, and dark energy equation of state, but appreciable parameter bias is induced by the baryonic processes such as gas cooling and stellar feedback. When we use power spectrum, peak counts, and Minkowski Functionals, the relative bias in the dark energy equation of state parameter $w$ ...
Cosmological Parameters Degeneracies and Non-Gaussian Halo Bias
Carbone, Carmelita; Verde, Licia
2010-01-01
We study the impact of the cosmological parameters uncertainties on the measurements of primordial non-Gaussianity through the large-scale non-Gaussian halo bias effect. While this is not expected to be an issue for the standard LCDM model, it may not be the case for more general models that modify the large-scale shape of the power spectrum. We consider the so-called local non-Gaussianity model and forecasts from planned surveys, alone and combined with a Planck CMB prior. In particular, we consider EUCLID- and LSST-like surveys and forecast the correlations among $f_{\\rm NL}$ and the running of the spectral index $\\alpha_s$, the dark energy equation of state $w$, the effective sound speed of dark energy perturbations $c^2_s$, the total mass of massive neutrinos $M_\
Estimation of Synchronous Machine Parameters
Oddvar Hallingstad
1980-01-01
The present paper gives a short description of an interactive estimation program based on the maximum likelihood (ML) method. The program may also perform identifiability analysis by calculating sensitivity functions and the Hessian matrix. For the short circuit test the ML method is able to estimate the q-axis subtransient reactance x''q, which is not possible by means of the conventional graphical method (another set of measurements has to be used). By means of the synchronization and close...
Joint MAP bias estimation and data association: simulations
Danford, Scott; Kragel, Bret; Poore, Aubrey
2007-09-01
The problem of joint maximum a posteriori (MAP) bias estimation and data association belongs to a class of nonconvex mixed integer nonlinear programming problems. These problems are difficult to solve due to both the combinatorial nature of the problem and the nonconvexity of the objective function or constraints. Algorithms for this class of problems have been developed in a companion paper of the authors. This paper presents simulations that compare the "all-pairs" heuristic, the k-best heuristic, and a partial A*-based branch and bound algorithm. The combination of the latter two algorithms is an excellent candidate for use in a realtime system. For an optimal algorithm that also computes the k-best solutions of the joint MAP bias estimation problem and data association problem, we investigate a branch and bound framework that employs either a depth-first algorithm or an A*-search procedure. In addition, we demonstrate the improvements due to a new gating procedure.
Reduced bias and threshold choice in the extremal index estimation through resampling techniques
Gomes, Dora Prata; Neves, Manuela
2013-10-01
In Extreme Value Analysis there are a few parameters of particular interest among which we refer to the extremal index, a measure of extreme events clustering. It is of great interest for initial dependent samples, the common situation in many practical situations. Most semi-parametric estimators of this parameter show the same behavior: nice asymptotic properties but a high variance for small values of k, the number of upper order statistics used in the estimation and a high bias for large values of k. The Mean Square Error, a measure that encompasses bias and variance, usually shows a very sharp plot, needing an adequate choice of k. Using classical extremal index estimators considered in the literature, the emphasis is now given to derive reduced bias estimators with more stable paths, obtained through resampling techniques. An adaptive algorithm for estimating the level k for obtaining a reliable estimate of the extremal index is used. This algorithm has shown good results, but some improvements are still required. A simulation study will illustrate the properties of the estimators and the performance of the adaptive algorithm proposed.
An assessment of Bayesian bias estimator for numerical weather prediction
J. Son
2008-12-01
Full Text Available Various statistical methods are used to process operational Numerical Weather Prediction (NWP products with the aim of reducing forecast errors and they often require sufficiently large training data sets. Generating such a hindcast data set for this purpose can be costly and a well designed algorithm should be able to reduce the required size of these data sets.
This issue is investigated with the relatively simple case of bias correction, by comparing a Bayesian algorithm of bias estimation with the conventionally used empirical method. As available forecast data sets are not large enough for a comprehensive test, synthetically generated time series representing the analysis (truth and forecast are used to increase the sample size. Since these synthetic time series retained the statistical characteristics of the observations and operational NWP model output, the results of this study can be extended to real observation and forecasts and this is confirmed by a preliminary test with real data.
By using the climatological mean and standard deviation of the meteorological variable in consideration and the statistical relationship between the forecast and the analysis, the Bayesian bias estimator outperforms the empirical approach in terms of the accuracy of the estimated bias, and it can reduce the required size of the training sample by a factor of 3. This advantage of the Bayesian approach is due to the fact that it is less liable to the sampling error in consecutive sampling. These results suggest that a carefully designed statistical procedure may reduce the need for the costly generation of large hindcast datasets.
Sharma, Swati; Rousseau, François; Heitz, Fabrice; Rumbach, Lucien; Armspach, Jean-Paul
2013-01-01
Brain atrophy is considered an important marker of disease progression in many chronic neuro-degenerative diseases such as multiple sclerosis (MS). A great deal of attention is being paid toward developing tools that manipulate magnetic resonance (MR) images for obtaining an accurate estimate of atrophy. Nevertheless, artifacts in MR images, inaccuracies of intermediate steps and inadequacies of the mathematical model representing the physical brain volume change, make it rather difficult to obtain a precise and unbiased estimate. This work revolves around the nature and magnitude of bias in atrophy estimations as well as a potential way of correcting them. First, we demonstrate that for different atrophy estimation methods, bias estimates exhibit varying relations to the expected atrophy and these bias estimates are of the order of the expected atrophies for standard algorithms, stressing the need for bias correction procedures. Next, a framework for estimating uncertainty in longitudinal brain atrophy by means of constructing confidence intervals is developed. Errors arising from MRI artifacts and bias in estimations are learned from example atrophy simulations and anatomies. Results are discussed for three popular non-rigid registration approaches with the help of simulated localized brain atrophy in real MR images. PMID:23988649
Lash Timothy L
2007-11-01
Full Text Available Abstract Background The associations of pesticide exposure with disease outcomes are estimated without the benefit of a randomized design. For this reason and others, these studies are susceptible to systematic errors. I analyzed studies of the associations between alachlor and glyphosate exposure and cancer incidence, both derived from the Agricultural Health Study cohort, to quantify the bias and uncertainty potentially attributable to systematic error. Methods For each study, I identified the prominent result and important sources of systematic error that might affect it. I assigned probability distributions to the bias parameters that allow quantification of the bias, drew a value at random from each assigned distribution, and calculated the estimate of effect adjusted for the biases. By repeating the draw and adjustment process over multiple iterations, I generated a frequency distribution of adjusted results, from which I obtained a point estimate and simulation interval. These methods were applied without access to the primary record-level dataset. Results The conventional estimates of effect associating alachlor and glyphosate exposure with cancer incidence were likely biased away from the null and understated the uncertainty by quantifying only random error. For example, the conventional p-value for a test of trend in the alachlor study equaled 0.02, whereas fewer than 20% of the bias analysis iterations yielded a p-value of 0.02 or lower. Similarly, the conventional fully-adjusted result associating glyphosate exposure with multiple myleoma equaled 2.6 with 95% confidence interval of 0.7 to 9.4. The frequency distribution generated by the bias analysis yielded a median hazard ratio equal to 1.5 with 95% simulation interval of 0.4 to 8.9, which was 66% wider than the conventional interval. Conclusion Bias analysis provides a more complete picture of true uncertainty than conventional frequentist statistical analysis accompanied by a
Bias-corrected estimation in potentially mildly explosive autoregressive models
Haufmann, Hendrik; Kruse, Robinson
This paper provides a comprehensive Monte Carlo comparison of different finite-sample bias-correction methods for autoregressive processes. We consider classic situations where the process is either stationary or exhibits a unit root. Importantly, the case of mildly explosive behaviour is studied...... indirect inference approach oers a valuable alternative to other existing techniques. Its performance (measured by its bias and root mean squared error) is balanced and highly competitive across many different settings. A clear advantage is its applicability for mildly explosive processes. In an empirical...... application to a long annual US Debt/GDP series we consider rolling window estimation of autoregressive models. We find substantial evidence for time-varying persistence and periods of explosiveness during the Civil War and World War II. During the recent years, the series is nearly explosive again. Further...
Improving uncertainty estimation in urban hydrological modeling by statistically describing bias
D. Del Giudice
2013-04-01
Full Text Available Hydrodynamic models are useful tools for urban water management. Unfortunately, it is still challenging to obtain accurate results and plausible uncertainty estimates when using these models. In particular, with the currently applied statistical techniques, flow predictions are usually overconfident and biased. In this study, we present a flexible and computationally efficient methodology (i to obtain more reliable hydrological simulations in terms of coverage of validation data by the uncertainty bands and (ii to separate prediction uncertainty into its components. Our approach acknowledges that urban drainage predictions are biased. This is mostly due to input errors and structural deficits of the model. We address this issue by describing model bias in a Bayesian framework. The bias becomes an autoregressive term additional to white measurement noise, the only error type accounted for in traditional uncertainty analysis in urban hydrology. To allow for bigger discrepancies during wet weather, we make the variance of bias dependent on the input (rainfall or/and output (runoff of the system. Specifically, we present a structured approach to select, among five variants, the optimal bias description for a given urban or natural case study. We tested the methodology in a small monitored stormwater system described by means of a parsimonious model. Our results clearly show that flow simulations are much more reliable when bias is accounted for than when it is neglected. Furthermore, our probabilistic predictions can discriminate between three uncertainty contributions: parametric uncertainty, bias (due to input and structural errors, and measurement errors. In our case study, the best performing bias description was the output-dependent bias using a log-sinh transformation of data and model results. The limitations of the framework presented are some ambiguity due to the subjective choice of priors for bias parameters and its inability to directly
Estimation of physical parameters in induction motors
Børsting, H.; Knudsen, Morten; Rasmussen, Henrik;
1994-01-01
Parameter estimation in induction motors is a field of great interest, because accurate models are needed for robust dynamic control of induction motors......Parameter estimation in induction motors is a field of great interest, because accurate models are needed for robust dynamic control of induction motors...
Postprocessing MPEG based on estimated quantization parameters
Forchhammer, Søren
2009-01-01
the case where the coded stream is not accessible, or from an architectural point of view not desirable to use, and instead estimate some of the MPEG stream parameters based on the decoded sequence. The I-frames are detected and the quantization parameters are estimated from the coded stream and used...
Estimation for large non-centrality parameters
Inácio, Sónia; Mexia, João; Fonseca, Miguel; Carvalho, Francisco
2016-06-01
We introduce the concept of estimability for models for which accurate estimators can be obtained for the respective parameters. The study was conducted for model with almost scalar matrix using the study of estimability after validation of these models. In the validation of these models we use F statistics with non centrality parameter τ =‖λ/‖2 σ2 when this parameter is sufficiently large we obtain good estimators for λ and α so there is estimability. Thus, we are interested in obtaining a lower bound for the non-centrality parameter. In this context we use for the statistical inference inducing pivot variables, see Ferreira et al. 2013, and asymptotic linearity, introduced by Mexia & Oliveira 2011, to derive confidence intervals for large non-centrality parameters (see Inácio et al. 2015). These results enable us to measure relevance of effects and interactions in multifactors models when we get highly statistically significant the values of F tests statistics.
ESTIMATION ACCURACY OF EXPONENTIAL DISTRIBUTION PARAMETERS
muhammad zahid rashid
2011-04-01
Full Text Available The exponential distribution is commonly used to model the behavior of units that have a constant failure rate. The two-parameter exponential distribution provides a simple but nevertheless useful model for the analysis of lifetimes, especially when investigating reliability of technical equipment.This paper is concerned with estimation of parameters of the two parameter (location and scale exponential distribution. We used the least squares method (LSM, relative least squares method (RELS, ridge regression method (RR, moment estimators (ME, modified moment estimators (MME, maximum likelihood estimators (MLE and modified maximum likelihood estimators (MMLE. We used the mean square error MSE, and total deviation TD, as measurement for the comparison between these methods. We determined the best method for estimation using different values for the parameters and different sample sizes
Bias, precision, and parameter redundancy in complex multistate models with unobservable states.
Bailey, Larissa L; Converse, Sarah J; Kendall, William L
2010-06-01
Multistate mark-recapture models with unobservable states can yield unbiased estimators of survival probabilities in the presence of temporary emigration (i.e., in cases where some individuals are temporarily unavailable for capture). In addition, these models permit the estimation of transition probabilities between states, which may themselves be of interest; for example, when only breeding animals are available for capture. However, parameter redundancy is frequently a problem in these models, yielding biased parameter estimates and influencing model selection. Using numerical methods, we examine complex multistate mark-recapture models involving two observable and two unobservable states. This model structure was motivated by two different biological systems: one involving island-nesting albatross, and another involving pond-breeding amphibians. We found that, while many models are theoretically identifiable given appropriate constraints, obtaining accurate and precise parameter estimates in practice can be difficult. Practitioners should consider ways to increase detection probabilities or adopt robust design sampling in order to improve the properties of estimates obtained from these models. We suggest that investigators interested in using these models explore both theoretical identifiability and possible near-singularity for likely parameter values using a combination of available methods. PMID:20583702
Distributed Parameter Estimation in Probabilistic Graphical Models
Mizrahi, Yariv Dror; Denil, Misha; De Freitas, Nando
2014-01-01
This paper presents foundational theoretical results on distributed parameter estimation for undirected probabilistic graphical models. It introduces a general condition on composite likelihood decompositions of these models which guarantees the global consistency of distributed estimators, provided the local estimators are consistent.
Joint MAP bias estimation and data association: algorithms
Danford, Scott; Kragel, Bret; Poore, Aubrey
2007-09-01
The problem of joint maximum a posteriori (MAP) bias estimation and data association belongs to a class of nonconvex mixed integer nonlinear programming problems. These problems are difficult to solve due to both the combinatorial nature of the problem and the nonconvexity of the objective function or constraints. A specific problem that has received some attention in the tracking literature is that of the target object map problem in which one tries match a set of tracks as observed by two different sensors in the presence of biases, which are modeled here as a translation between the track states. The general framework also applies to problems in which the costs are general nonlinear functions of the biases. The goal of this paper is to present a class of algorithms based on the branch and bound framework and the "all-pairs" and k-best heuristics that provide a good initial upper bound for a branch and bound algorithm. These heuristics can be used as part of a real-time algorithm or as part of an "anytime algorithm" within the branch and bound framework. In addition, we consider both the A*-search and depth-first search procedures as well as several efficiency improvements such as gating. While this paper focuses on the algorithms, a second paper will focus on simulations.
Estimating Sampling Selection Bias in Human Genetics: A Phenomenological Approach
Risso, Davide; Taglioli, Luca; De Iasio, Sergio; Gueresi, Paola; Alfani, Guido; Nelli, Sergio; Rossi, Paolo; Paoli, Giorgio; Tofanelli, Sergio
2015-01-01
This research is the first empirical attempt to calculate the various components of the hidden bias associated with the sampling strategies routinely-used in human genetics, with special reference to surname-based strategies. We reconstructed surname distributions of 26 Italian communities with different demographic features across the last six centuries (years 1447–2001). The degree of overlapping between "reference founding core" distributions and the distributions obtained from sampling the present day communities by probabilistic and selective methods was quantified under different conditions and models. When taking into account only one individual per surname (low kinship model), the average discrepancy was 59.5%, with a peak of 84% by random sampling. When multiple individuals per surname were considered (high kinship model), the discrepancy decreased by 8–30% at the cost of a larger variance. Criteria aimed at maximizing locally-spread patrilineages and long-term residency appeared to be affected by recent gene flows much more than expected. Selection of the more frequent family names following low kinship criteria proved to be a suitable approach only for historically stable communities. In any other case true random sampling, despite its high variance, did not return more biased estimates than other selective methods. Our results indicate that the sampling of individuals bearing historically documented surnames (founders' method) should be applied, especially when studying the male-specific genome, to prevent an over-stratification of ancient and recent genetic components that heavily biases inferences and statistics. PMID:26452043
Cosmological parameter estimation using Particle Swarm Optimization
Constraining parameters of a theoretical model from observational data is an important exercise in cosmology. There are many theoretically motivated models, which demand greater number of cosmological parameters than the standard model of cosmology uses, and make the problem of parameter estimation challenging. It is a common practice to employ Bayesian formalism for parameter estimation for which, in general, likelihood surface is probed. For the standard cosmological model with six parameters, likelihood surface is quite smooth and does not have local maxima, and sampling based methods like Markov Chain Monte Carlo (MCMC) method are quite successful. However, when there are a large number of parameters or the likelihood surface is not smooth, other methods may be more effective. In this paper, we have demonstrated application of another method inspired from artificial intelligence, called Particle Swarm Optimization (PSO) for estimating cosmological parameters from Cosmic Microwave Background (CMB) data taken from the WMAP satellite
Analytical propagation of errors in dynamic SPECT: estimators, degrading factors, bias and noise
Dynamic SPECT is a relatively new technique that may potentially benefit many imaging applications. Though similar to dynamic PET, the accuracy and precision of dynamic SPECT parameter estimates are degraded by factors that differ from those encountered in PET. In this work we formulate a methodology for analytically studying the propagation of errors from dynamic projection data to kinetic parameter estimates. This methodology is used to study the relationships between reconstruction estimators, image degrading factors, bias and statistical noise for the application of dynamic cardiac imaging with 99mTc-teboroxime. Dynamic data were simulated for a torso phantom, and the effects of attenuation, detector response and scatter were successively included to produce several data sets. The data were reconstructed to obtain both weighted and unweighted least squares solutions, and the kinetic rate parameters for a two- compartment model were estimated. The expected values and standard deviations describing the statistical distribution of parameters that would be estimated from noisy data were calculated analytically. The results of this analysis present several interesting implications for dynamic SPECT. Statistically weighted estimators performed only marginally better than unweighted ones, implying that more computationally efficient unweighted estimators may be appropriate. This also suggests that it may be beneficial to focus future research efforts upon regularization methods with beneficial bias-variance trade-offs. Other aspects of the study describe the fundamental limits of the bias-variance trade-off regarding physical degrading factors and their compensation. The results characterize the effects of attenuation, detector response and scatter, and they are intended to guide future research into dynamic SPECT reconstruction and compensation methods. (author)
An enhanced algorithm to estimate BDS satellite's differential code biases
Shi, Chuang; Fan, Lei; Li, Min; Liu, Zhizhao; Gu, Shengfeng; Zhong, Shiming; Song, Weiwei
2016-02-01
This paper proposes an enhanced algorithm to estimate the differential code biases (DCB) on three frequencies of the BeiDou Navigation Satellite System (BDS) satellites. By forming ionospheric observables derived from uncombined precise point positioning and geometry-free linear combination of phase-smoothed range, satellite DCBs are determined together with ionospheric delay that is modeled at each individual station. Specifically, the DCB and ionospheric delay are estimated in a weighted least-squares estimator by considering the precision of ionospheric observables, and a misclosure constraint for different types of satellite DCBs is introduced. This algorithm was tested by GNSS data collected in November and December 2013 from 29 stations of Multi-GNSS Experiment (MGEX) and BeiDou Experimental Tracking Stations. Results show that the proposed algorithm is able to precisely estimate BDS satellite DCBs, where the mean value of day-to-day scattering is about 0.19 ns and the RMS of the difference with respect to MGEX DCB products is about 0.24 ns. In order to make comparison, an existing algorithm based on IGG: Institute of Geodesy and Geophysics, China (IGGDCB), is also used to process the same dataset. Results show that, the DCB difference between results from the enhanced algorithm and the DCB products from Center for Orbit Determination in Europe (CODE) and MGEX is reduced in average by 46 % for GPS satellites and 14 % for BDS satellites, when compared with DCB difference between the results of IGGDCB algorithm and the DCB products from CODE and MGEX. In addition, we find the day-to-day scattering of BDS IGSO satellites is obviously lower than that of GEO and MEO satellites, and a significant bias exists in daily DCB values of GEO satellites comparing with MGEX DCB product. This proposed algorithm also provides a new approach to estimate the satellite DCBs of multiple GNSS systems.
The power spectrum of systematics in cosmic shear tomography and the bias on cosmological parameters
Cardone, V F; Calabrese, E; Galli, S; Huang, Z; Maoli, R; Melchiorri, A; Scaramella, R
2013-01-01
Cosmic shear tomography has emerged as one of the most promising tools to both investigate the nature of dark energy and discriminate between General Relativity and modified gravity theories. In order to successfully achieve these goals, systematics in shear measurements have to be taken into account; their impact on the weak lensing power spectrum has to be carefully investigated in order to estimate the bias induced on the inferred cosmological parameters. To this end, we develop here an efficient tool to compute the power spectrum of systematics by propagating, in a realistic way, shear measurement, source properties and survey setup uncertainties. Starting from analytical results for unweighted moments and general assumptions on the relation between measured and actual shear, we derive analytical expressions for the multiplicative and additive bias, showing how these terms depend not only on the shape measurement errors, but also on the properties of the source galaxies (namely, size, magnitude and spectr...
Estimation of distances to stars with stellar parameters from LAMOST
Carlin, Jeffrey L; Newberg, Heidi Jo; Beers, Timothy C; Chen, Li; Deng, Licai; Guhathakurta, Puragra; Hou, Jinliang; Hou, Yonghui; Lepine, Sebastien; Li, Guangwei; Luo, A-Li; Smith, Martin C; Wu, Yue; Yang, Ming; Yanny, Brian; Zhang, Haotong; Zheng, Zheng
2015-01-01
We present a method to estimate distances to stars with spectroscopically derived stellar parameters. The technique is a Bayesian approach with likelihood estimated via comparison of measured parameters to a grid of stellar isochrones, and returns a posterior probability density function for each star's absolute magnitude. This technique is tailored specifically to data from the Large Sky Area Multi-object Fiber Spectroscopic Telescope (LAMOST) survey. Because LAMOST obtains roughly 3000 stellar spectra simultaneously within each ~5-degree diameter "plate" that is observed, we can use the stellar parameters of the observed stars to account for the stellar luminosity function and target selection effects. This removes biasing assumptions about the underlying populations, both due to predictions of the luminosity function from stellar evolution modeling, and from Galactic models of stellar populations along each line of sight. Using calibration data of stars with known distances and stellar parameters, we show ...
State and parameter estimation in bio processes
Maher, M.; Roux, G.; Dahhou, B. [Centre National de la Recherche Scientifique (CNRS), 31 - Toulouse (France)]|[Institut National des Sciences Appliquees (INSA), 31 - Toulouse (France)
1994-12-31
A major difficulty in monitoring and control of bio-processes is the lack of reliable and simple sensors for following the evolution of the main state variables and parameters such as biomass, substrate, product, growth rate, etc... In this article, an adaptive estimation algorithm is proposed to recover the state and parameters in bio-processes. This estimator utilizes the physical process model and the reference model approach. Experimentations concerning estimation of biomass and product concentrations and specific growth rate, during batch, fed-batch and continuous fermentation processes are presented. The results show the performance of this adaptive estimation approach. (authors) 12 refs.
Aggregation Bias in Estimating European Money Demand Functions
Wesche, Katrin
1996-01-01
Recently, money demand functions for a group of European countries have been estimated and generally have been found to perform better than most national money demand functions. While parameter equality is a sufficient condition for valid aggregation of linear equations, in money demand estimation often log-linear specifications are used, so that aggregation is in effect nonlinear. This makes the relation between the aggregate and the individual equations more complicated. To investigate if t...
DEB parameters estimation for Mytilus edulis
Saraiva, S.; van der Meer, J.; Kooijman, S. A. L. M.; Sousa, T.
2011-11-01
The potential of DEB theory to simulate an organism life-cycle has been demonstrated at numerous occasions. However, its applicability requires parameter estimates that are not easily obtained by direct observations. During the last years various attempts were made to estimate the main DEB parameters for bivalve species. The estimation procedure was by then, however, rather ad-hoc and based on additional assumptions that were not always consistent with the DEB theory principles. A new approach has now been developed - the covariation method - based on simultaneous minimization of the weighted sum of squared deviations between data sets and model predictions in one single procedure. This paper presents the implementation of this method to estimate the DEB parameters for the blue mussel Mytilus edulis, using several data sets from the literature. After comparison with previous trials we conclude that the parameter set obtained by the covariation method leads to a better fit between model and observations, with potentially more consistency and robustness.
On Carleman estimates with two large parameters
We provide a general framework for the analysis and the derivation of Carleman estimates with two large parameters. For an appropriate form of weight functions strong pseudo-convexity conditions are shown to be necessary and sufficient.
Error covariance calculation for forecast bias estimation in hydrologic data assimilation
Pauwels, Valentijn R. N.; De Lannoy, Gabriëlle J. M.
2015-12-01
To date, an outstanding issue in hydrologic data assimilation is a proper way of dealing with forecast bias. A frequently used method to bypass this problem is to rescale the observations to the model climatology. While this approach improves the variability in the modeled soil wetness and discharge, it is not designed to correct the results for any bias. Alternatively, attempts have been made towards incorporating dynamic bias estimates into the assimilation algorithm. Persistent bias models are most often used to propagate the bias estimate, where the a priori forecast bias error covariance is calculated as a constant fraction of the unbiased a priori state error covariance. The latter approach is a simplification to the explicit propagation of the bias error covariance. The objective of this paper is to examine to which extent the choice for the propagation of the bias estimate and its error covariance influence the filter performance. An Observation System Simulation Experiment (OSSE) has been performed, in which ground water storage observations are assimilated into a biased conceptual hydrologic model. The magnitudes of the forecast bias and state error covariances are calibrated by optimizing the innovation statistics of groundwater storage. The obtained bias propagation models are found to be identical to persistent bias models. After calibration, both approaches for the estimation of the forecast bias error covariance lead to similar results, with a realistic attribution of error variances to the bias and state estimate, and significant reductions of the bias in both the estimates of groundwater storage and discharge. Overall, the results in this paper justify the use of the traditional approach for online bias estimation with a persistent bias model and a simplified forecast bias error covariance estimation.
Parameter Estimation of Partial Differential Equation Models
Xun, Xiaolei
2013-09-01
Partial differential equation (PDE) models are commonly used to model complex dynamic systems in applied sciences such as biology and finance. The forms of these PDE models are usually proposed by experts based on their prior knowledge and understanding of the dynamic system. Parameters in PDE models often have interesting scientific interpretations, but their values are often unknown and need to be estimated from the measurements of the dynamic system in the presence of measurement errors. Most PDEs used in practice have no analytic solutions, and can only be solved with numerical methods. Currently, methods for estimating PDE parameters require repeatedly solving PDEs numerically under thousands of candidate parameter values, and thus the computational load is high. In this article, we propose two methods to estimate parameters in PDE models: a parameter cascading method and a Bayesian approach. In both methods, the underlying dynamic process modeled with the PDE model is represented via basis function expansion. For the parameter cascading method, we develop two nested levels of optimization to estimate the PDE parameters. For the Bayesian method, we develop a joint model for data and the PDE and develop a novel hierarchical model allowing us to employ Markov chain Monte Carlo (MCMC) techniques to make posterior inference. Simulation studies show that the Bayesian method and parameter cascading method are comparable, and both outperform other available methods in terms of estimation accuracy. The two methods are demonstrated by estimating parameters in a PDE model from long-range infrared light detection and ranging data. Supplementary materials for this article are available online. © 2013 American Statistical Association.
Bias-adjusted satellite-based rainfall estimates for predicting floods: Narayani Basin
Shrestha, M.S.; Artan, G.A.; Bajracharya, S.R.; Gautam, D.K.; Tokar, S.A.
2011-01-01
In Nepal, as the spatial distribution of rain gauges is not sufficient to provide detailed perspective on the highly varied spatial nature of rainfall, satellite-based rainfall estimates provides the opportunity for timely estimation. This paper presents the flood prediction of Narayani Basin at the Devghat hydrometric station (32000km2) using bias-adjusted satellite rainfall estimates and the Geospatial Stream Flow Model (GeoSFM), a spatially distributed, physically based hydrologic model. The GeoSFM with gridded gauge observed rainfall inputs using kriging interpolation from 2003 was used for calibration and 2004 for validation to simulate stream flow with both having a Nash Sutcliff Efficiency of above 0.7. With the National Oceanic and Atmospheric Administration Climate Prediction Centre's rainfall estimates (CPC-RFE2.0), using the same calibrated parameters, for 2003 the model performance deteriorated but improved after recalibration with CPC-RFE2.0 indicating the need to recalibrate the model with satellite-based rainfall estimates. Adjusting the CPC-RFE2.0 by a seasonal, monthly and 7-day moving average ratio, improvement in model performance was achieved. Furthermore, a new gauge-satellite merged rainfall estimates obtained from ingestion of local rain gauge data resulted in significant improvement in flood predictability. The results indicate the applicability of satellite-based rainfall estimates in flood prediction with appropriate bias correction. ?? 2011 The Authors. Journal of Flood Risk Management ?? 2011 The Chartered Institution of Water and Environmental Management.
Statistics of Parameter Estimates: A Concrete Example
Aguilar, Oscar
2015-01-01
© 2015 Society for Industrial and Applied Mathematics. Most mathematical models include parameters that need to be determined from measurements. The estimated values of these parameters and their uncertainties depend on assumptions made about noise levels, models, or prior knowledge. But what can we say about the validity of such estimates, and the influence of these assumptions? This paper is concerned with methods to address these questions, and for didactic purposes it is written in the context of a concrete nonlinear parameter estimation problem. We will use the results of a physical experiment conducted by Allmaras et al. at Texas A&M University [M. Allmaras et al., SIAM Rev., 55 (2013), pp. 149-167] to illustrate the importance of validation procedures for statistical parameter estimation. We describe statistical methods and data analysis tools to check the choices of likelihood and prior distributions, and provide examples of how to compare Bayesian results with those obtained by non-Bayesian methods based on different types of assumptions. We explain how different statistical methods can be used in complementary ways to improve the understanding of parameter estimates and their uncertainties.
Hall, Peter
1992-01-01
The bootstrap is a poor estimator of bias in problems of curve estimation, and so bias must be corrected by other means when the bootstrap is used to construct confidence intervals for a probability density. Bias may either be estimated explicitly, or allowed for by undersmoothing the curve estimator. Which of these two approaches is to be preferred? In the present paper we address this question from the viewpoint of coverage accuracy, assuming a given number of derivatives of the unknown den...
LISA parameter estimation using numerical merger waveforms
Thorpe, J I; Kelly, B J; Fahey, R P; Arnaud, K; Baker, J G
2008-01-01
Recent advances in numerical relativity provide a detailed description of the waveforms of coalescing massive black hole binaries (MBHBs), expected to be the strongest detectable LISA sources. We present a preliminary study of LISA's sensitivity to MBHB parameters using a hybrid numerical/analytic waveform for equal-mass, non-spinning holes. The Synthetic LISA software package is used to simulate the instrument response and the Fisher information matrix method is used to estimate errors in the parameters. Initial results indicate that inclusion of the merger signal can significantly improve the precision of some parameter estimates. For example, the median parameter errors for an ensemble of systems with total redshifted mass of one million Solar masses at a redshift of one were found to decrease by a factor of slightly more than two for signals with merger as compared to signals truncated at the Schwarzchild ISCO.
LISA parameter estimation using numerical merger waveforms
Thorpe, J I; McWilliams, S T; Kelly, B J; Fahey, R P; Arnaud, K; Baker, J G, E-mail: James.I.Thorpe@nasa.go [NASA Goddard Space Flight Center, 8800 Greenbelt Rd, Greenbelt, MD 20771 (United States)
2009-05-07
Recent advances in numerical relativity provide a detailed description of the waveforms of coalescing massive black hole binaries (MBHBs), expected to be the strongest detectable LISA sources. We present a preliminary study of LISA's sensitivity to MBHB parameters using a hybrid numerical/analytic waveform for equal-mass, non-spinning holes. The Synthetic LISA software package is used to simulate the instrument response, and the Fisher information matrix method is used to estimate errors in the parameters. Initial results indicate that inclusion of the merger signal can significantly improve the precision of some parameter estimates. For example, the median parameter errors for an ensemble of systems with total redshifted mass of 10{sup 6} M{sub o-dot} at a redshift of z approx 1 were found to decrease by a factor of slightly more than two for signals with merger as compared to signals truncated at the Schwarzchild ISCO.
LISA parameter estimation using numerical merger waveforms
Recent advances in numerical relativity provide a detailed description of the waveforms of coalescing massive black hole binaries (MBHBs), expected to be the strongest detectable LISA sources. We present a preliminary study of LISA's sensitivity to MBHB parameters using a hybrid numerical/analytic waveform for equal-mass, non-spinning holes. The Synthetic LISA software package is used to simulate the instrument response, and the Fisher information matrix method is used to estimate errors in the parameters. Initial results indicate that inclusion of the merger signal can significantly improve the precision of some parameter estimates. For example, the median parameter errors for an ensemble of systems with total redshifted mass of 106 Mo-dot at a redshift of z ∼ 1 were found to decrease by a factor of slightly more than two for signals with merger as compared to signals truncated at the Schwarzchild ISCO.
Parameter Estimation of Noise Corrupted Sinusoids
O'Brien, Francis J; Johnnie, Nathan
2011-01-01
Existing algorithms for fitting the parameters of a sinusoid to noisy discrete time observations are not always successful due to initial value sensitivity and other issues. This paper demonstrates the techniques of FIR filtering, Fast Fourier Transform, and nonlinear least squares minimization as useful in the parameter estimation of amplitude, frequency and phase exemplified for a low-frequency time-delayed sinusoid describing simple harmonic motion. Alternative means are described for estimating frequency and phase angle. An autocorrelation function for harmonic motion is also derived.
Modelling and parameter estimation of dynamic systems
Raol, JR; Singh, J
2004-01-01
Parameter estimation is the process of using observations from a system to develop mathematical models that adequately represent the system dynamics. The assumed model consists of a finite set of parameters, the values of which are calculated using estimation techniques. Most of the techniques that exist are based on least-square minimization of error between the model response and actual system response. However, with the proliferation of high speed digital computers, elegant and innovative techniques like filter error method, H-infinity and Artificial Neural Networks are finding more and mor
Parameter estimation of the WMTD model
LUO Ji; QIU Hong-bing
2009-01-01
The MTD (mixture transition distribution) model based on Weibull distribution (WMTD model) is proposed in this paper, which is aimed at its parameter estimation. An EM algorithm for estimation is given and shown to work well by some simulations. And bootstrap method is used to obtain confidence regions for the parameters. Finally, the results of a real example--predicting stock prices--show that the WMTD model proposed is able to capture the features of the data from thick-tailed distribution better than GMTD (mixture transition distribution) model.
Hurst Parameter Estimation Using Artificial Neural Networks
S..Ledesma-Orozco
2011-08-01
Full Text Available The Hurst parameter captures the amount of long-range dependence (LRD in a time series. There are severalmethods to estimate the Hurst parameter, being the most popular: the variance-time plot, the R/S plot, theperiodogram, and Whittle’s estimator. The first three are graphical methods, and the estimation accuracy depends onhow the plot is interpreted and calculated. In contrast, Whittle’s estimator is based on a maximum likelihood techniqueand does not depend on a graph reading; however, it is computationally expensive. A new method to estimate theHurst parameter is proposed. This new method is based on an artificial neural network. Experimental results showthat this method outperforms traditional approaches, and can be used on applications where a fast and accurateestimate of the Hurst parameter is required, i.e., computer network traffic control. Additionally, the Hurst parameterwas computed on series of different length using several methods. The simulation results show that the proposedmethod is at least ten times faster than traditional methods.
Multi-Parameter Estimation for Orthorhombic Media
Masmoudi, Nabil
2015-08-19
Building reliable anisotropy models is crucial in seismic modeling, imaging and full waveform inversion. However, estimating anisotropy parameters is often hampered by the trade off between inhomogeneity and anisotropy. For instance, one way to estimate the anisotropy parameters is to relate them analytically to traveltimes, which is challenging in inhomogeneous media. Using perturbation theory, we develop travel-time approximations for orthorhombic media as explicit functions of the anellipticity parameters η1, η2 and a parameter Δγ in inhomogeneous background media. Specifically, our expansion assumes inhomogeneous ellipsoidal anisotropic background model, which can be obtained from well information and stacking velocity analysis. This approach has two main advantages: in one hand, it provides a computationally efficient tool to solve the orthorhombic eikonal equation, on the other hand, it provides a mechanism to scan for the best fitting anisotropy parameters without the need for repetitive modeling of traveltimes, because the coefficients of the traveltime expansion are independent of the perturbed parameters. Furthermore, the coefficients of the traveltime expansion provide insights on the sensitivity of the traveltime with respect to the perturbed parameters. We show the accuracy of the traveltime approximations as well as an approach for multi-parameter scanning in orthorhombic media.
Performance Analysis of Parameter Estimation Using LASSO
Panahi, Ashkan; Viberg, Mats
2012-01-01
The Least Absolute Shrinkage and Selection Operator (LASSO) has gained attention in a wide class of continuous parametric estimation problems with promising results. It has been a subject of research for more than a decade. Due to the nature of LASSO, the previous analyses have been non-parametric. This ignores useful information and makes it difficult to compare LASSO to traditional estimators. In particular, the role of the regularization parameter and super-resolution properties of LASSO h...
Biosorption Parameter Estimation with Genetic Algorithm
Yung-Tse Hung; Eui Yong Kim; Xiao Feng; Khim Hoong Chu
2011-01-01
In biosorption research, a fairly broad range of mathematical models are used to correlate discrete data points obtained from batch equilibrium, batch kinetic or fixed bed breakthrough experiments. Most of these models are inherently nonlinear in their parameters. Some of the models have enjoyed widespread use, largely because they can be linearized to allow the estimation of parameters by least-squares linear regression. Selecting a model for data correlation appears to be dictated by the ea...
Spin bath narrowing with adaptive parameter estimation
Cappellaro, Paola
2012-01-01
We present a measurement scheme capable of achieving the quantum limit of parameter estimation using an adaptive strategy that minimizes the parameter's variance at each step. The adaptive rule we propose makes the scheme robust against errors, in particular imperfect readouts, a critical requirement to extend adaptive schemes from quantum optics to solid-state sensors. Thanks to recent advances in single-shot readout capabilities for electronic spins in the solid state (such as Nitrogen Vaca...
Parameter Estimation of Noise Corrupted Sinusoids
O'Brien, Jr., W.,; Johnnie, Nathan
2011-01-01
Existing algorithms for fitting the parameters of a sinusoid to noisy discrete time observations are not always successful due to initial value sensitivity and other issues. This paper demonstrates the techniques of FIR filtering, Fast Fourier Transform, circular autocorreltion, and nonlinear least squares minimization as useful in the parameter estimation of amplitude, frequency and phase exemplified for a low-frequency time-delayed sinusoid describing simple harmonic motion. Alternative mea...
Robust estimation of hydrological model parameters
A. Bárdossy
2008-11-01
Full Text Available The estimation of hydrological model parameters is a challenging task. With increasing capacity of computational power several complex optimization algorithms have emerged, but none of the algorithms gives a unique and very best parameter vector. The parameters of fitted hydrological models depend upon the input data. The quality of input data cannot be assured as there may be measurement errors for both input and state variables. In this study a methodology has been developed to find a set of robust parameter vectors for a hydrological model. To see the effect of observational error on parameters, stochastically generated synthetic measurement errors were applied to observed discharge and temperature data. With this modified data, the model was calibrated and the effect of measurement errors on parameters was analysed. It was found that the measurement errors have a significant effect on the best performing parameter vector. The erroneous data led to very different optimal parameter vectors. To overcome this problem and to find a set of robust parameter vectors, a geometrical approach based on Tukey's half space depth was used. The depth of the set of N randomly generated parameters was calculated with respect to the set with the best model performance (Nash-Sutclife efficiency was used for this study for each parameter vector. Based on the depth of parameter vectors, one can find a set of robust parameter vectors. The results show that the parameters chosen according to the above criteria have low sensitivity and perform well when transfered to a different time period. The method is demonstrated on the upper Neckar catchment in Germany. The conceptual HBV model was used for this study.
Kreif, N.; Gruber, S.; Radice, Rosalba; Grieve, R; J S Sekhon
2014-01-01
Statistical approaches for estimating treatment effectiveness commonly model the endpoint, or the propensity score, using parametric regressions such as generalised linear models. Misspecification of these models can lead to biased parameter estimates. We compare two approaches that combine the propensity score and the endpoint regression, and can make weaker modelling assumptions, by using machine learning approaches to estimate the regression function and the propensity score. Targeted maxi...
Estimation of accuracy and bias in genetic evaluations with genetic groups using sampling
Hickey, J.M.; Keane, M.G.; Kenny, D.A.; Cromie, A.R.; Mulder, H.A.; Veerkamp, R.F.
2008-01-01
Accuracy and bias of estimated breeding values are important measures of the quality of genetic evaluations. A sampling method that accounts for the uncertainty in the estimation of genetic group effects was used to calculate accuracy and bias of estimated effects. The method works by repeatedly sim
Pressler, Taylor R.; Kaizar, Eloise E.
2013-01-01
While randomized controlled trials (RCT) are considered the “gold standard” for clinical studies, the use of exclusion criteria may impact the external validity of the results. It is unknown whether estimators of effect size are biased by excluding a portion of the target population from enrollment. We propose to use observational data to estimate the bias due to enrollment restrictions, which we term generalizability bias. In this paper we introduce a class of estimators for the generalizabi...
Aquifer parameter estimation from surface resistivity data.
Niwas, Sri; de Lima, Olivar A L
2003-01-01
This paper is devoted to the additional use, other than ground water exploration, of surface geoelectrical sounding data for aquifer hydraulic parameter estimation. In a mesoscopic framework, approximated analytical equations are developed separately for saline and for fresh water saturations. A few existing useful aquifer models, both for clean and shaley sandstones, are discussed in terms of their electrical and hydraulic effects, along with the linkage between the two. These equations are derived for insight and physical understanding of the phenomenon. In a macroscopic scale, a general aquifer model is proposed and analytical relations are derived for meaningful estimation, with a higher level of confidence, of hydraulic parameter from electrical parameters. The physical reasons for two different equations at the macroscopic level are explicitly explained to avoid confusion. Numerical examples from existing literature are reproduced to buttress our viewpoint. PMID:12533080
A class of shrinkage estimators for the shape parameter of the Weibull lifetime model
Zuhair Alhemyari
2012-03-01
Full Text Available In this paper, we propose two classes of shrinkage estimators for the shape parameter of the Weibull distribution in censored samples. The proposed estimators are studied theoretically and have been compared numerically with existing estimators. Computer intensive calculations for bias and relative efficiency show that for, different values of levels of significance and for varying constants involved in the proposed estimators, the proposed testimators fare better than classical and existing estimators
Multi-Sensor Consensus Estimation of State, Sensor Biases and Unknown Input.
Zhou, Jie; Liang, Yan; Yang, Feng; Xu, Linfeng; Pan, Quan
2016-01-01
This paper addresses the problem of the joint estimation of system state and generalized sensor bias (GSB) under a common unknown input (UI) in the case of bias evolution in a heterogeneous sensor network. First, the equivalent UI-free GSB dynamic model is derived and the local optimal estimates of system state and sensor bias are obtained in each sensor node; Second, based on the state and bias estimates obtained by each node from its neighbors, the UI is estimated via the least-squares method, and then the state estimates are fused via consensus processing; Finally, the multi-sensor bias estimates are further refined based on the consensus estimate of the UI. A numerical example of distributed multi-sensor target tracking is presented to illustrate the proposed filter. PMID:27598156
Parameter estimation in channel network flow simulation
Han Longxi
2008-01-01
Simulations of water flow in channel networks require estimated values of roughness for all the individual channel segments that make up a network. When the number of individual channel segments is large, the parameter calibration workload is substantial and a high level of uncertainty in estimated roughness cannot be avoided. In this study, all the individual channel segments are graded according to the factors determining the value of roughness. It is assumed that channel segments with the same grade have the same value of roughness. Based on observed hydrological data, an optimal model for roughness estimation is built. The procedure of solving the optimal problem using the optimal model is described. In a test of its efficacy, this estimation method was applied successfully in the simulation of tidal water flow in a large complicated channel network in the lower reach of the Yangtze River in China.
Nonparametric estimation of location and scale parameters
Potgieter, C.J.
2012-12-01
Two random variables X and Y belong to the same location-scale family if there are constants μ and σ such that Y and μ+σX have the same distribution. In this paper we consider non-parametric estimation of the parameters μ and σ under minimal assumptions regarding the form of the distribution functions of X and Y. We discuss an approach to the estimation problem that is based on asymptotic likelihood considerations. Our results enable us to provide a methodology that can be implemented easily and which yields estimators that are often near optimal when compared to fully parametric methods. We evaluate the performance of the estimators in a series of Monte Carlo simulations. © 2012 Elsevier B.V. All rights reserved.
Multiple Parameter Estimation With Quantized Channel Output
Mezghani, Amine; Nossek, Josef A
2010-01-01
We present a general problem formulation for optimal parameter estimation based on quantized observations, with application to antenna array communication and processing (channel estimation, time-of-arrival (TOA) and direction-of-arrival (DOA) estimation). The work is of interest in the case when low resolution A/D-converters (ADCs) have to be used to enable higher sampling rate and to simplify the hardware. An Expectation-Maximization (EM) based algorithm is proposed for solving this problem in a general setting. Besides, we derive the Cramer-Rao Bound (CRB) and discuss the effects of quantization and the optimal choice of the ADC characteristic. Numerical and analytical analysis reveals that reliable estimation may still be possible even when the quantization is very coarse.
Sensor Placement for Modal Parameter Subset Estimation
Ulriksen, Martin Dalgaard; Bernal, Dionisio; Damkilde, Lars
2016-01-01
The present paper proposes an approach for deciding on sensor placements in the context of modal parameter estimation from vibration measurements. The approach is based on placing sensors, of which the amount is determined a priori, such that the minimum Fisher information that the frequency...... responses carry on the selected modal parameter subset is, in some sense, maximized. The approach is validated in the context of a simple 10-DOF mass-spring-damper system by computing the variance of a set of identified modal parameters in a Monte Carlo setting for a set of sensor configurations, whose......). It is shown that the widely used Effective Independence (EI) method, which uses the modal amplitudes as surrogates for the parameters of interest, provides sensor configurations yielding theoretical lower bound variances whose maxima are up to 30 % larger than those obtained by use of the max-min approach....
Visser, P.N.A.M.
2008-01-01
A method has been implemented and tested for estimating bias and scale factor parameters for all six individual accelerometers that will fly on-board of GOCE and together form the so-called gradiometer. The method is based on inclusion of the individual accelerometer observations in precise orbit de
On closure parameter estimation in chaotic systems
J. Hakkarainen
2012-02-01
Full Text Available Many dynamical models, such as numerical weather prediction and climate models, contain so called closure parameters. These parameters usually appear in physical parameterizations of sub-grid scale processes, and they act as "tuning handles" of the models. Currently, the values of these parameters are specified mostly manually, but the increasing complexity of the models calls for more algorithmic ways to perform the tuning. Traditionally, parameters of dynamical systems are estimated by directly comparing the model simulations to observed data using, for instance, a least squares approach. However, if the models are chaotic, the classical approach can be ineffective, since small errors in the initial conditions can lead to large, unpredictable deviations from the observations. In this paper, we study numerical methods available for estimating closure parameters in chaotic models. We discuss three techniques: off-line likelihood calculations using filtering methods, the state augmentation method, and the approach that utilizes summary statistics from long model simulations. The properties of the methods are studied using a modified version of the Lorenz 95 system, where the effect of fast variables are described using a simple parameterization.
Estimating Production Potentials: Expert Bias in Applied Decision Making
A study was conducted to evaluate how workers predict manufacturing production potentials given positively and negatively framed information. Findings indicate the existence of a bias toward positive information and suggest that this bias may be reduced with experience but is never the less maintained. Experts err in the same way non experts do in differentially processing negative and positive information. Additionally, both experts and non experts tend to overestimate production potentials in a positive direction. The authors propose that these biases should be addressed with further research including cross domain analyses and consideration in training, workplace design, and human performance modeling
CosmoSIS: modular cosmological parameter estimation
Zuntz, Joe; Jennings, Elise; Rudd, Douglas; Manzotti, Alessandro; Dodelson, Scott; Bridle, Sarah; Sehrish, Saba; Kowalkowski, James
2014-01-01
Cosmological parameter estimation is entering a new era. Large collaborations need to coordinate high-stakes analyses using multiple methods; furthermore such analyses have grown in complexity due to sophisticated models of cosmology and systematic uncertainties. In this paper we argue that modularity is the key to addressing these challenges: calculations should be broken up into interchangeable modular units with inputs and outputs clearly defined. We present a new framework for cosmological parameter estimation, CosmoSIS, designed to connect together, share, and advance development of inference tools across the community. We describe the modules already available in CosmoSIS, including CAMB, Planck, cosmic shear calculations, and a suite of samplers. We illustrate it using demonstration code that you can run out-of-the-box with the installer available at http://bitbucket.org/joezuntz/cosmosis
Measurement Data Modeling and Parameter Estimation
Wang, Zhengming; Yao, Jing; Gu, Defeng
2011-01-01
Measurement Data Modeling and Parameter Estimation integrates mathematical theory with engineering practice in the field of measurement data processing. Presenting the first-hand insights and experiences of the authors and their research group, it summarizes cutting-edge research to facilitate the application of mathematical theory in measurement and control engineering, particularly for those interested in aeronautics, astronautics, instrumentation, and economics. Requiring a basic knowledge of linear algebra, computing, and probability and statistics, the book illustrates key lessons with ta
Clustering of dark matter tracers: renormalizing the bias parameters
McDonald, Patrick
2006-01-01
A commonly used perturbative method for computing large-scale clustering of tracers of mass density, like galaxies, is to model the tracer density field as a Taylor series in the local smoothed mass density fluctuations, possibly adding a stochastic component. I suggest a set of parameter redefinitions, eliminating problematic perturbative correction terms, that should represent a modest improvement, at least, to this method. As presented here, my method can be used to compute the power spect...
Estimating Earthen Dam-Breach Parameters
Mahdi Moharrampour
2013-12-01
Full Text Available Dam failure leads to release of high volume of water which causes huge waves in downstream. Failure in dam may cause financial loss but life losses depend on the drowned zone, population residing in downstream of the danger zone and warning time. Therefore, it is inevitable to predict dam failure and its resulting dangers to reduce life and financial losses. Flood simulation models simulate flood caused by dam failure (such as DAMBRK and FLDWAV, the resulting outflow in dam failure and its route in downstream of the river. Such models mostly focused on outflow hydrographs. In these models, physical development of failure is not simulated but they describe dam failure process as parametric process which is defined as form of failure, final size and time required for developing it (destruction time. Therefore, dam failure parameters should be estimated for simulating dam failure and applied as input information in simulation model. For this reason, failure of Bidakan earth dam located in Chahar Mahal va Bakhtiari Province has been simulated by estimating parameters of failure, analyzing uncertainty of experimental methods for estimating parameters of failure and applying SMPDBK model.
Taking Variable Correlation into Consideration during Parameter Estimation
T.J. Santos; Pinto, J C.
1998-01-01
Variable correlations are usually neglected during parameter estimation. Very frequently these are gross assumptions and may potentially lead to inadequate interpretation of final estimation results. For this reason, variable correlation and model parameters are sometimes estimated simultaneously in certain parameter estimation procedures. It is shown, however, that usually taking variable correlation into consideration during parameter estimation may be inadequate and unnecessary, unless ind...
Antoni Margalida; Daniel Oro; Ainara Cortés-Avizanda; Rafael Heredia; Donázar, José A.
2011-01-01
Conservation strategies for long-lived vertebrates require accurate estimates of parameters relative to the populations' size, numbers of non-breeding individuals (the "cryptic" fraction of the population) and the age structure. Frequently, visual survey techniques are used to make these estimates but the accuracy of these approaches is questionable, mainly because of the existence of numerous potential biases. Here we compare data on population trends and age structure in a bearded vulture (...
A two parameter ratio-product-ratio estimator using auxiliary information
Chami, Peter S; Thomas, Doneal
2012-01-01
We propose a two parameter ratio-product-ratio estimator for a finite population mean in a simple random sample without replacement following the methodology in Ray and Sahai (1980), Sahai and Ray (1980), Sahai and Sahai (1985) and Singh and Ruiz Espejo (2003). The bias and mean square error of our proposed estimator are obtained to the first degree of approximation. We derive conditions for the parameters under which the proposed estimator has smaller mean square error than the sample mean, ratio and product estimators. We carry out an application showing that the proposed estimator outperforms the traditional estimators using groundwater data taken from a geological site in the state of Florida.
PARAMETER ESTIMATION IN BREAD BAKING MODEL
Hadiyanto Hadiyanto
2012-05-01
Full Text Available Bread product quality is highly dependent to the baking process. A model for the development of product quality, which was obtained by using quantitative and qualitative relationships, was calibrated by experiments at a fixed baking temperature of 200°C alone and in combination with 100 W microwave powers. The model parameters were estimated in a stepwise procedure i.e. first, heat and mass transfer related parameters, then the parameters related to product transformations and finally product quality parameters. There was a fair agreement between the calibrated model results and the experimental data. The results showed that the applied simple qualitative relationships for quality performed above expectation. Furthermore, it was confirmed that the microwave input is most meaningful for the internal product properties and not for the surface properties as crispness and color. The model with adjusted parameters was applied in a quality driven food process design procedure to derive a dynamic operation pattern, which was subsequently tested experimentally to calibrate the model. Despite the limited calibration with fixed operation settings, the model predicted well on the behavior under dynamic convective operation and on combined convective and microwave operation. It was expected that the suitability between model and baking system could be improved further by performing calibration experiments at higher temperature and various microwave power levels. Abstrak PERKIRAAN PARAMETER DALAM MODEL UNTUK PROSES BAKING ROTI. Kualitas produk roti sangat tergantung pada proses baking yang digunakan. Suatu model yang telah dikembangkan dengan metode kualitatif dan kuantitaif telah dikalibrasi dengan percobaan pada temperatur 200oC dan dengan kombinasi dengan mikrowave pada 100 Watt. Parameter-parameter model diestimasi dengan prosedur bertahap yaitu pertama, parameter pada model perpindahan masa dan panas, parameter pada model transformasi, dan
Squared visibility estimator. Calibrating biases to reach very high dynamic range
Perrin, G
2005-01-01
In the near infrared where detectors are limited by read-out noise, most interferometers have been operated in wide band in order to benefit from larger photon rates. We analyze in this paper the biases caused by instrumental and turbulent effects to $V^2$ estimators for both narrow and wide band cases. Visibilities are estimated from samples of the interferogram using two different estimators, $V^{2}_1$ which is the classical sum of the squared modulus of Fourier components and a new estimator $V^{2}_2$ for which complex Fourier components are summed prior to taking the square. We present an approach for systematically evaluating the performance and limits of each estimator, and to optimizing observing parameters for each. We include the effects of spectral bandwidth, chromatic dispersion, scan length, and differential piston. We also establish the expression of the Signal-to-Noise Ratio of the two estimators with respect to detector and photon noise. The $V^{2}_1$ estimator is insensitive to dispersion and ...
Composite likelihood estimation of demographic parameters
Garrigan Daniel
2009-11-01
Full Text Available Abstract Background Most existing likelihood-based methods for fitting historical demographic models to DNA sequence polymorphism data to do not scale feasibly up to the level of whole-genome data sets. Computational economies can be achieved by incorporating two forms of pseudo-likelihood: composite and approximate likelihood methods. Composite likelihood enables scaling up to large data sets because it takes the product of marginal likelihoods as an estimator of the likelihood of the complete data set. This approach is especially useful when a large number of genomic regions constitutes the data set. Additionally, approximate likelihood methods can reduce the dimensionality of the data by summarizing the information in the original data by either a sufficient statistic, or a set of statistics. Both composite and approximate likelihood methods hold promise for analyzing large data sets or for use in situations where the underlying demographic model is complex and has many parameters. This paper considers a simple demographic model of allopatric divergence between two populations, in which one of the population is hypothesized to have experienced a founder event, or population bottleneck. A large resequencing data set from human populations is summarized by the joint frequency spectrum, which is a matrix of the genomic frequency spectrum of derived base frequencies in two populations. A Bayesian Metropolis-coupled Markov chain Monte Carlo (MCMCMC method for parameter estimation is developed that uses both composite and likelihood methods and is applied to the three different pairwise combinations of the human population resequence data. The accuracy of the method is also tested on data sets sampled from a simulated population model with known parameters. Results The Bayesian MCMCMC method also estimates the ratio of effective population size for the X chromosome versus that of the autosomes. The method is shown to estimate, with reasonable
Parameter estimation using B-Trees
Schmidt, Albrecht; Bøhlen, Michael H.
2004-01-01
This paper presents a method for accelerating algorithms for computing common statistical operations like parameter estimation or sampling on B-Tree indexed data; the work was carried out in the context of visualisation of large scientific data sets. The underlying idea is the following: the shape...... of balanced data structures like B-Trees encodes and reflects data semantics according to the balance criterion. For example, clusters in the index attribute are somewhat likely to be present not only on the data or leaf level of the tree but should propagate up into the interior levels. The paper...... also hints at opportunities and limitations of this approach for visualisation of large data sets. The advantages of the method are manifold. Not only does it enable advanced algorithms through a performance boost for basic operations like density estimation, but it also builds on functionality that is...
Parameter Estimation in Active Plate Structures
Araujo, A. L.; Lopes, H. M. R.; Vaz, M. A. P.;
2006-01-01
In this paper two non-destructive methods for elastic and piezoelectric parameter estimation in active plate structures with surface bonded piezoelectric patches are presented. These methods rely on experimental undamped natural frequencies of free vibration. The first solves the inverse problem...... through gradient based optimization techniques, while the second is based on a metamodel of the inverse problem, using artificial neural networks. A numerical higher order finite element laminated plate model is used in both methods and results are compared and discussed through a simulated and an...
Emad Habib
2014-07-01
Full Text Available Results of numerous evaluation studies indicated that satellite-rainfall products are contaminated with significant systematic and random errors. Therefore, such products may require refinement and correction before being used for hydrologic applications. In the present study, we explore a rainfall-runoff modeling application using the Climate Prediction Center-MORPHing (CMORPH satellite rainfall product. The study area is the Gilgel Abbay catchment situated at the source basin of the Upper Blue Nile basin in Ethiopia, Eastern Africa. Rain gauge networks in such area are typically sparse. We examine different bias correction schemes applied locally to the CMORPH product. These schemes vary in the degree to which spatial and temporal variability in the CMORPH bias fields are accounted for. Three schemes are tested: space and time-invariant, time-variant and spatially invariant, and space and time variant. Bias-corrected CMORPH products were used to calibrate and drive the Hydrologiska Byråns Vattenbalansavdelning (HBV rainfall-runoff model. Applying the space and time-fixed bias correction scheme resulted in slight improvement of the CMORPH-driven runoff simulations, but in some instances caused deterioration. Accounting for temporal variation in the bias reduced the rainfall bias by up to 50%. Additional improvements were observed when both the spatial and temporal variability in the bias was accounted for. The rainfall bias was found to have a pronounced effect on model calibration. The calibrated model parameters changed significantly when using rainfall input from gauges alone, uncorrected, and bias-corrected CMORPH estimates. Changes of up to 81% were obtained for model parameters controlling the stream flow volume.
Error and bias in size estimates of whale sharks: implications for understanding demography
Sequeira, Ana M M; Thums, Michele; Brooks, Kim; Meekan, Mark G.
2016-01-01
Body size and age at maturity are indicative of the vulnerability of a species to extinction. However, they are both difficult to estimate for large animals that cannot be restrained for measurement. For very large species such as whale sharks, body size is commonly estimated visually, potentially resulting in the addition of errors and bias. Here, we investigate the errors and bias associated with total lengths of whale sharks estimated visually by comparing them with measurements collected ...
Bombrun, Lionel; Pascal, Frédéric; Tourneret, Jean-Yves; Berthoumieu, Yannick
2012-01-01
This paper studies the performance of the maximum likelihood estimators (MLE) for the parameters of multivariate generalized Gaussian distributions. When the shape parameter belongs to ]0,1[, we have proved that the scatter matrix MLE exists and is unique up to a scalar factor. After providing some elements about this proof, an estimation algorithm based on a Newton-Raphson recursion is investigated. Some experiments illustrate the convergence speed of this algorithm. The bias and consistency...
Fast cosmological parameter estimation using neural networks
Auld, T; Hobson, M P; Gull, S F
2006-01-01
We present a method for accelerating the calculation of CMB power spectra, matter power spectra and likelihood functions for use in cosmological parameter estimation. The algorithm, called CosmoNet, is based on training a multilayer perceptron neural network and shares all the advantages of the recently released Pico algorithm of Fendt & Wandelt, but has several additional benefits in terms of simplicity, computational speed, memory requirements and ease of training. We demonstrate the capabilities of CosmoNet by computing CMB power spectra over a box in the parameter space of flat \\Lambda CDM models containing the 3\\sigma WMAP1 confidence region. We also use CosmoNet to compute the WMAP3 likelihood for flat \\Lambda CDM models and show that marginalised posteriors on parameters derived are very similar to those obtained using CAMB and the WMAP3 code. We find that the average error in the power spectra is typically 2-3% of cosmic variance, and that CosmoNet is \\sim 7 \\times 10^4 faster than CAMB (for flat ...
Health Indicators: Eliminating bias from convenience sampling estimators
HEDT, Bethany L.; Pagano, Marcello
2011-01-01
Public health practitioners are often called upon to make inference about a health indicator for a population at large when the sole available information are data gathered from a convenience sample, such as data gathered on visitors to a clinic. These data may be of the highest quality and quite extensive, but the biases inherent in a convenience sample preclude the legitimate use of powerful inferential tools that are usually associated with a random sample. In general, we know nothing abou...
Multifrequency SAR data for estimating hydrological parameters
The sensitivity of backscattering coefficients to some geophysical parameters which play a significant role in hydrological processes (vegetation biomass, soil moisture and surface roughness) is discussed. Experimental results show that P-band makes it possible the monitoring of forest biomass, L-band appears to be good for wide-leaf crops, and C- and X-bands for small-leaf crops. Moreover, L-band backscattering makes the highest contribution in estimating soil moisture and surface roughness. The sensitivity to spatial distribution of soil moisture and surface roughness is rather low, since both quantities affect the radar signal. However, observing data collected at different dates and averaged over several fields, the correlation to soil moisture is significant, since the effects of spatial roughness variations are smoothed. The retrieval of both soil moisture and surface roughness has been performed by means of a semiempirical model
Wood, Lesley; Egger, Matthias; Gluud, Lise Lotte;
2008-01-01
To examine whether the association of inadequate or unclear allocation concealment and lack of blinding with biased estimates of intervention effects varies with the nature of the intervention or outcome....
An Inhomogeneous Bayesian Texture Model for Spatially Varying Parameter Estimation
Dharmagunawardhana, Chathurika; Mahmoodi, Sasan; Bennett, Michael; Niranjan, Mahesan
2014-01-01
In statistical model based texture feature extraction, features based on spatially varying parameters achieve higher discriminative performances compared to spatially constant parameters. In this paper we formulate a novel Bayesian framework which achieves texture characterization by spatially varying parameters based on Gaussian Markov random fields. The parameter estimation is carried out by Metropolis-Hastings algorithm. The distributions of estimated spatially varying paramete...
Systematic biases on galaxy haloes parameters from Yukawa-like gravitational potentials
Cardone, V F
2011-01-01
A viable alternative to the dark energy as a solution of the cosmic speed up problem is represented by Extended Theories of Gravity. Should this be indeed the case, there will be an impact not only on cosmological scales, but also at any scale, from the Solar System to extragalactic ones. In particular, the gravitational potential can be different from the Newtonian one commonly adopted when computing the circular velocity fitted to spiral galaxies rotation curves. Phenomenologically modelling the modified point mass potential as the sum of a Newtonian and a Yukawa like correction, we simulate observed rotation curves for a spiral galaxy described as the sum of an exponential disc and a NFW dark matter halo. We then fit these curves assuming parameterized halo models (either with an inner cusp or a core) and using the Newtonian potential to estimate the theoretical rotation curve. Such a study allows us to investigate the bias on the disc and halo model parameters induced by the systematic error induced by fo...
Learning effect on survey data: high leverage and estimation bias
Mazbahul Golam Ahamad
2010-01-01
Method of survey data collection, especially at household or personal interview, responders frequently answers extreme, because of their pre-assumption on questionnaire to get financial or food aid. This reduces data consistency and advances leverage that affects estimation procedure and estimated predictors. This study analysis the impact of learning effect on research hypothesis using mailed interview of different cluster of researcher such as expertise, mid-level research assistants and en...
Bias in estimating food consumption of fish from stomach-content analysis
Rindorf, Anna; Lewy, Peter
2004-01-01
This study presents an analysis of the bias introduced by using simplified methods to calculate food intake of fish from stomach contents. Three sources of bias were considered: (1) the effect of estimating consumption based on a limited number of stomach samples, (2) the effect of using average...... contents derived from pooled stomach samples rather than individual stomachs, and (3) the effect of ignoring biological factors that affect the evacuation of prey. Estimating consumption from only two stomach samples yielded results close to the actual intake rate in a simulation study. In contrast to this......, a serious positive bias was introduced by estimating food intake from the contents of pooled stomach samples. An expression is given that can be used to correct analytically for this bias. A new method, which takes into account the distribution and evacuation of individual prey types as well as the...
Ocean wave parameters estimation using backpropagation neural networks
Mandal, S.; SubbaRao; Raju, D.H.
In the present study, various ocean wave parameters are estimated from theoretical Pierson-Moskowitz spectra as well as measured ocean wave spectra using back propagation neural networks (BNN). Ocean wave parameters estimation by BNN shows...
James O Lloyd-Smith
Full Text Available BACKGROUND: The negative binomial distribution is used commonly throughout biology as a model for overdispersed count data, with attention focused on the negative binomial dispersion parameter, k. A substantial literature exists on the estimation of k, but most attention has focused on datasets that are not highly overdispersed (i.e., those with k>or=1, and the accuracy of confidence intervals estimated for k is typically not explored. METHODOLOGY: This article presents a simulation study exploring the bias, precision, and confidence interval coverage of maximum-likelihood estimates of k from highly overdispersed distributions. In addition to exploring small-sample bias on negative binomial estimates, the study addresses estimation from datasets influenced by two types of event under-counting, and from disease transmission data subject to selection bias for successful outbreaks. CONCLUSIONS: Results show that maximum likelihood estimates of k can be biased upward by small sample size or under-reporting of zero-class events, but are not biased downward by any of the factors considered. Confidence intervals estimated from the asymptotic sampling variance tend to exhibit coverage below the nominal level, with overestimates of k comprising the great majority of coverage errors. Estimation from outbreak datasets does not increase the bias of k estimates, but can add significant upward bias to estimates of the mean. Because k varies inversely with the degree of overdispersion, these findings show that overestimation of the degree of overdispersion is very rare for these datasets.
Estimating demographic parameters using a combination of known-fate and open N-mixture models
Schmidt, Joshua H.; Johnson, Devin S.; Lindberg, Mark S.; Adams, Layne G.
2015-01-01
Accurate estimates of demographic parameters are required to infer appropriate ecological relationships and inform management actions. Known-fate data from marked individuals are commonly used to estimate survival rates, whereas N-mixture models use count data from unmarked individuals to estimate multiple demographic parameters. However, a joint approach combining the strengths of both analytical tools has not been developed. Here we develop an integrated model combining known-fate and open N-mixture models, allowing the estimation of detection probability, recruitment, and the joint estimation of survival. We demonstrate our approach through both simulations and an applied example using four years of known-fate and pack count data for wolves (Canis lupus). Simulation results indicated that the integrated model reliably recovered parameters with no evidence of bias, and survival estimates were more precise under the joint model. Results from the applied example indicated that the marked sample of wolves was biased toward individuals with higher apparent survival rates than the unmarked pack mates, suggesting that joint estimates may be more representative of the overall population. Our integrated model is a practical approach for reducing bias while increasing precision and the amount of information gained from mark–resight data sets. We provide implementations in both the BUGS language and an R package.
System and method for motor parameter estimation
Luhrs, Bin; Yan, Ting
2014-03-18
A system and method for determining unknown values of certain motor parameters includes a motor input device connectable to an electric motor having associated therewith values for known motor parameters and an unknown value of at least one motor parameter. The motor input device includes a processing unit that receives a first input from the electric motor comprising values for the known motor parameters for the electric motor and receive a second input comprising motor data on a plurality of reference motors, including values for motor parameters corresponding to the known motor parameters of the electric motor and values for motor parameters corresponding to the at least one unknown motor parameter value of the electric motor. The processor determines the unknown value of the at least one motor parameter from the first input and the second input and determines a motor management strategy for the electric motor based thereon.
The influence of geomagnetic storms on the estimation of GPS instrumental biases
W. Zhang
2009-04-01
Full Text Available An algorithm has been developed to derive the ionospheric total electron content (TEC and to estimate the resulting instrumental biases in Global Positioning System (GPS data from measurements made with a single receiver. The algorithm assumes that the TEC is identical at any point within a mesh and that the GPS instrumental biases do not vary within a day. We present some results obtained using the algorithm and a study of the characteristics of the instrumental biases during active geomagnetic periods. The deviations of the TEC during an ionospheric storm (induced by a geomagnetic storm, compared to the quiet ionosphere, typically result in severe fluctuations in the derived GPS instrumental biases. Based on the analysis of three ionospheric storm events, we conclude that different kinds of ionospheric storms have differing influences on the measured biases of GPS satellites and receivers. We find that the duration of severe ionospheric storms is the critical factor that adversely impacts the estimation of GPS instrumental biases. Large deviations in the TEC can produce inaccuracies in the estimation of GPS instrumental biases for the satellites that pass over the receiver during that period. We also present a semi quantitative analysis of the duration of the influence of the storm.
Health indicators: eliminating bias from convenience sampling estimators.
Hedt, Bethany L; Pagano, Marcello
2011-02-28
Public health practitioners are often called upon to make inference about a health indicator for a population at large when the sole available information are data gathered from a convenience sample, such as data gathered on visitors to a clinic. These data may be of the highest quality and quite extensive, but the biases inherent in a convenience sample preclude the legitimate use of powerful inferential tools that are usually associated with a random sample. In general, we know nothing about those who do not visit the clinic beyond the fact that they do not visit the clinic. An alternative is to take a random sample of the population. However, we show that this solution would be wasteful if it excluded the use of available information. Hence, we present a simple annealing methodology that combines a relatively small, and presumably far less expensive, random sample with the convenience sample. This allows us to not only take advantage of powerful inferential tools, but also provides more accurate information than that available from just using data from the random sample alone. PMID:21290401
Estimating Climatological Bias Errors for the Global Precipitation Climatology Project (GPCP)
Adler, Robert; Gu, Guojun; Huffman, George
2012-01-01
A procedure is described to estimate bias errors for mean precipitation by using multiple estimates from different algorithms, satellite sources, and merged products. The Global Precipitation Climatology Project (GPCP) monthly product is used as a base precipitation estimate, with other input products included when they are within +/- 50% of the GPCP estimates on a zonal-mean basis (ocean and land separately). The standard deviation s of the included products is then taken to be the estimated systematic, or bias, error. The results allow one to examine monthly climatologies and the annual climatology, producing maps of estimated bias errors, zonal-mean errors, and estimated errors over large areas such as ocean and land for both the tropics and the globe. For ocean areas, where there is the largest question as to absolute magnitude of precipitation, the analysis shows spatial variations in the estimated bias errors, indicating areas where one should have more or less confidence in the mean precipitation estimates. In the tropics, relative bias error estimates (s/m, where m is the mean precipitation) over the eastern Pacific Ocean are as large as 20%, as compared with 10%-15% in the western Pacific part of the ITCZ. An examination of latitudinal differences over ocean clearly shows an increase in estimated bias error at higher latitudes, reaching up to 50%. Over land, the error estimates also locate regions of potential problems in the tropics and larger cold-season errors at high latitudes that are due to snow. An empirical technique to area average the gridded errors (s) is described that allows one to make error estimates for arbitrary areas and for the tropics and the globe (land and ocean separately, and combined). Over the tropics this calculation leads to a relative error estimate for tropical land and ocean combined of 7%, which is considered to be an upper bound because of the lack of sign-of-the-error canceling when integrating over different areas with a
Parameter estimation with Sandage-Loeb test
The Sandage-Loeb (SL) test directly measures the expansion rate of the universe in the redshift range of 2 ∼< z ∼< 5 by detecting redshift drift in the spectra of Lyman-α forest of distant quasars. We discuss the impact of the future SL test data on parameter estimation for the ΛCDM, the wCDM, and the w0waCDM models. To avoid the potential inconsistency with other observational data, we take the best-fitting dark energy model constrained by the current observations as the fiducial model to produce 30 mock SL test data. The SL test data provide an important supplement to the other dark energy probes, since they are extremely helpful in breaking the existing parameter degeneracies. We show that the strong degeneracy between Ωm and H0 in all the three dark energy models is well broken by the SL test. Compared to the current combined data of type Ia supernovae, baryon acoustic oscillation, cosmic microwave background, and Hubble constant, the 30-yr observation of SL test could improve the constraints on Ωm and H0 by more than 60% for all the three models. But the SL test can only moderately improve the constraint on the equation of state of dark energy. We show that a 30-yr observation of SL test could help improve the constraint on constant w by about 25%, and improve the constraints on w0 and wa by about 20% and 15%, respectively. We also quantify the constraining power of the SL test in the future high-precision joint geometric constraints on dark energy. The mock future supernova and baryon acoustic oscillation data are simulated based on the space-based project JDEM. We find that the 30-yr observation of SL test would help improve the measurement precision of Ωm, H0, and wa by more than 70%, 20%, and 60%, respectively, for the w0waCDM model
Parameter estimation with Sandage-Loeb test
Geng, Jia-Jia; Zhang, Jing-Fei; Zhang, Xin, E-mail: gengjiajia163@163.com, E-mail: jfzhang@mail.neu.edu.cn, E-mail: zhangxin@mail.neu.edu.cn [Department of Physics, College of Sciences, Northeastern University, Shenyang 110004 (China)
2014-12-01
The Sandage-Loeb (SL) test directly measures the expansion rate of the universe in the redshift range of 2 ∼< z ∼< 5 by detecting redshift drift in the spectra of Lyman-α forest of distant quasars. We discuss the impact of the future SL test data on parameter estimation for the ΛCDM, the wCDM, and the w{sub 0}w{sub a}CDM models. To avoid the potential inconsistency with other observational data, we take the best-fitting dark energy model constrained by the current observations as the fiducial model to produce 30 mock SL test data. The SL test data provide an important supplement to the other dark energy probes, since they are extremely helpful in breaking the existing parameter degeneracies. We show that the strong degeneracy between Ω{sub m} and H{sub 0} in all the three dark energy models is well broken by the SL test. Compared to the current combined data of type Ia supernovae, baryon acoustic oscillation, cosmic microwave background, and Hubble constant, the 30-yr observation of SL test could improve the constraints on Ω{sub m} and H{sub 0} by more than 60% for all the three models. But the SL test can only moderately improve the constraint on the equation of state of dark energy. We show that a 30-yr observation of SL test could help improve the constraint on constant w by about 25%, and improve the constraints on w{sub 0} and w{sub a} by about 20% and 15%, respectively. We also quantify the constraining power of the SL test in the future high-precision joint geometric constraints on dark energy. The mock future supernova and baryon acoustic oscillation data are simulated based on the space-based project JDEM. We find that the 30-yr observation of SL test would help improve the measurement precision of Ω{sub m}, H{sub 0}, and w{sub a} by more than 70%, 20%, and 60%, respectively, for the w{sub 0}w{sub a}CDM model.
Estimation of high altitude Martian dust parameters
Pabari, Jayesh; Bhalodi, Pinali
2016-07-01
Dust devils are known to occur near the Martian surface mostly during the mid of Southern hemisphere summer and they play vital role in deciding background dust opacity in the atmosphere. The second source of high altitude Martian dust could be due to the secondary ejecta caused by impacts on Martian Moons, Phobos and Deimos. Also, the surfaces of the Moons are charged positively due to ultraviolet rays from the Sun and negatively due to space plasma currents. Such surface charging may cause fine grains to be levitated, which can easily escape the Moons. It is expected that the escaping dust form dust rings within the orbits of the Moons and therefore also around the Mars. One more possible source of high altitude Martian dust is interplanetary in nature. Due to continuous supply of the dust from various sources and also due to a kind of feedback mechanism existing between the ring or tori and the sources, the dust rings or tori can sustain over a period of time. Recently, very high altitude dust at about 1000 km has been found by MAVEN mission and it is expected that the dust may be concentrated at about 150 to 500 km. However, it is mystery how dust has reached to such high altitudes. Estimation of dust parameters before-hand is necessary to design an instrument for the detection of high altitude Martian dust from a future orbiter. In this work, we have studied the dust supply rate responsible primarily for the formation of dust ring or tori, the life time of dust particles around the Mars, the dust number density as well as the effect of solar radiation pressure and Martian oblateness on dust dynamics. The results presented in this paper may be useful to space scientists for understanding the scenario and designing an orbiter based instrument to measure the dust surrounding the Mars for solving the mystery. The further work is underway.
Attitude and gyro bias estimation by the rotation of an inertial measurement unit
In navigation applications, the presence of an unknown bias in the measurement of rate gyros is a key performance-limiting factor. In order to estimate the gyro bias and improve the accuracy of attitude measurement, we proposed a new method which uses the rotation of an inertial measurement unit, which is independent from rigid body motion. By actively changing the orientation of the inertial measurement unit (IMU), the proposed method generates sufficient relations between the gyro bias and tilt angle (roll and pitch) error via ridge body dynamics, and the gyro bias, including the bias that causes the heading error, can be estimated and compensated. The rotation inertial measurement unit method makes the gravity vector measured from the IMU continuously change in a body-fixed frame. By theoretically analyzing the mathematic model, the convergence of the attitude and gyro bias to the true values is proven. The proposed method provides a good attitude estimation using only measurements from an IMU, when other sensors such as magnetometers and GPS are unreliable. The performance of the proposed method is illustrated under realistic robotic motions and the results demonstrate an improvement in the accuracy of the attitude estimation. (paper)
Attitude and gyro bias estimation by the rotation of an inertial measurement unit
Wu, Zheming; Sun, Zhenguo; Zhang, Wenzeng; Chen, Qiang
2015-12-01
In navigation applications, the presence of an unknown bias in the measurement of rate gyros is a key performance-limiting factor. In order to estimate the gyro bias and improve the accuracy of attitude measurement, we proposed a new method which uses the rotation of an inertial measurement unit, which is independent from rigid body motion. By actively changing the orientation of the inertial measurement unit (IMU), the proposed method generates sufficient relations between the gyro bias and tilt angle (roll and pitch) error via ridge body dynamics, and the gyro bias, including the bias that causes the heading error, can be estimated and compensated. The rotation inertial measurement unit method makes the gravity vector measured from the IMU continuously change in a body-fixed frame. By theoretically analyzing the mathematic model, the convergence of the attitude and gyro bias to the true values is proven. The proposed method provides a good attitude estimation using only measurements from an IMU, when other sensors such as magnetometers and GPS are unreliable. The performance of the proposed method is illustrated under realistic robotic motions and the results demonstrate an improvement in the accuracy of the attitude estimation.
Effect of percent non-detects on estimation bias in censored distributions
Zhang, Z.; Lennox, W. C.; Panu, U. S.
2004-09-01
Uniqueness of the problem surrounding non-detects has been a concern alike to researchers and statisticians dealing with summary statistics while analyzing censored data. To incorporate non-detects in the estimation process, a simple substitution by the MDL (method detection limit) and the maximum likelihood estimation method are routinely implemented as standard methods by US-EPA laboratories. In situations where numerical standards are set at or near the MDL by regulatory agencies, it is prudent and important to closely investigate both the variability in test measurements and the estimation bias, because an inference based on biased estimates could entail significant liabilities. Variability is understood to be not only inevitable but also an inherent and integral part of any chemical analysis or test. In situations where regulatory agencies fail to account for the inherently present variability of test measurements, there is a need for regulated facilities to seek remedial action merely as a consequence of inadequate statistical procedure. This paper utilizes a mathematical approach to derive the bias functions and resulting bias curves are developed to investigate the censored samples from a variety of probability distributions such as normal, log-normal, gamma, and Gumbel distributions. Finally, the bias functions and bias curves are also compared to the results obtained by using Monte Carlo simulations.
Understanding the physics driving the values of Lyman-alpha forest bias parameters
Cieplak, Agnieszka M.; Slosar, Anze
2016-01-01
With the advancement of Lyman-alpha forest power spectrum measurements to larger scales and to greater precision, it is crucial that we also improve our understanding of the bias between the measured flux and the underlying matter power spectrum, especially for future percent level cosmology constraints. In order to develop an intuition for the physics driving the values of the density and velocity bias parameters of the Lyman-alpha forest, we have run a series of hydrodynamic SPH simulations to test existing approximations found in the literature. Through a series of progressively more realistic scenarios, we first introduce flux based on the Fluctuating Gunn Peterson Approximation, just using the density fields, then introduce redshift space distortions, as well as thermal broadening, and finally, analyzing the full hydrodynamic part of the simulations. We find surprising agreement between the analytical approximations developed by Seljak (2012) and the numerical methods in the limit of linear redshift space-distortions and no thermal broadening. Specifically, we find that the prediction of the analytical velocity bias expression is exact in the limit of no thermal broadening, and speculate that the measurement of this bias along with a small-scale measurement of the flux PDF, could yield a possible probe of the thermal state of the IGM. A deeper understanding of the large-scale Lyman-alpha biasing will also help us in using the large-scale clustering of the forest as a cosmological probe beyond baryon acoustic oscillations.
Impact of Road Vehicle Accelerations on SAR-GMTI Motion Parameter Estimation
Baumgartner, Stefan; Gabele, Martina; Krieger, Gerhard; Bethke, Karl-Heinz; Zuev, Sergey
2006-01-01
In recent years many powerful techniques and algorithms have been developed to detect moving targets and estimate their motion parameters from single- or multi-channel SAR data. In case of single- and two-channel systems, most of the developed algorithms rely on analysis of the Doppler history. Nowadays it is known, that even small unconsidered across-track accelerations can bias the along-track velocity estimation. Since we want to monitor real and more complex traffic scenarios with a f...
Effects of network-average magnitude bias on yield estimates for underground nuclear explosions
The ISC body-wave magnitude, msub(b ISC), of presumed underground nuclear explosions in Kazakhstan, USSR, is shown to be systematically biased, by comparison to that recorded at the array station EKA (msub(b EKA)). This is found to be due in part to anelastic attenuation effects on msub(b EKA), but several characteristics of the ISC data demonstrate that the bias is also due to network-averaging effects. For the smaller explosions, those stations with a positive msub(b) bias dominate the data set, but the remainder of the network fails to detect the event. Conversely, for larger explosions, additional stations, with negative msub(b) bias will detect. Use of published station corrections for EKA allows estimation of an msub(b EKA): Y relationship and hence, a magnitude: yield relationship which takes account of network-average bias. (author)
Wage Premia in Employment Clusters: Does Worker Sorting Bias Estimates?
Shihe Fu; Stephen L. Ross
2007-01-01
This paper tests whether the correlation between wages and the spatial concentration of employment can be explained by unobserved worker productivity differences. Residential location is used as a proxy for a worker's unobserved productivity, and average workplace commute time is used to test whether location based productivity differences are compensated away by longer commutes. Analyses using confidential data from the 2000 Decennial Census Long Form find that the agglomeration estimates ar...
Change-in-ratio density estimator for feral pigs is less biased than closed mark-recapture estimates
Hanson, L.B.; Grand, J.B.; Mitchell, M.S.; Jolley, D.B.; Sparklin, B.D.; Ditchkoff, S.S.
2008-01-01
Closed-population capture-mark-recapture (CMR) methods can produce biased density estimates for species with low or heterogeneous detection probabilities. In an attempt to address such biases, we developed a density-estimation method based on the change in ratio (CIR) of survival between two populations where survival, calculated using an open-population CMR model, is known to differ. We used our method to estimate density for a feral pig (Sus scrofa) population on Fort Benning, Georgia, USA. To assess its validity, we compared it to an estimate of the minimum density of pigs known to be alive and two estimates based on closed-population CMR models. Comparison of the density estimates revealed that the CIR estimator produced a density estimate with low precision that was reasonable with respect to minimum known density. By contrast, density point estimates using the closed-population CMR models were less than the minimum known density, consistent with biases created by low and heterogeneous capture probabilities for species like feral pigs that may occur in low density or are difficult to capture. Our CIR density estimator may be useful for tracking broad-scale, long-term changes in species, such as large cats, for which closed CMR models are unlikely to work. ?? CSIRO 2008.
Sonntag, J. G.; Chibisov, A.; Krabill, K. A.; Linkswiler, M. A.; Swenson, C.; Yungel, J.
2015-12-01
Present-day airborne lidar surveys of polar ice, NASA's Operation IceBridge foremost among them, cover large geographical areas. They are often compared with previous surveys over the same flight lines to yield mass balance estimates. Systematic biases in the lidar system, especially those which vary from campaign to campaign, can introduce significant error into these mass balance estimates and must be minimized before the data is released by the instrument team to the larger scientific community. NASA's Airborne Topographic Mapper (ATM) team designed a thorough and novel approach in order to minimize these biases, and here we describe two major aspects of this approach. First, we conduct regular ground vehicle-based surveys of lidar calibration targets, and overfly these targets on a near-daily basis during field campaigns. We discuss our technique for conducting these surveys, in particular the measures we take specifically to minimize systematic height biases in the surveys, since these can in turn bias entire campaigns of lidar data and the mass balance estimates based on them. Second, we calibrate our GPS antennas specifically for each instrument installation in a remote-sensing aircraft. We do this because we recognize that the metallic fuselage of the aircraft can alter the electromagnetic properties of the GPS antenna mounted to it, potentially displacing its phase center by several centimeters and biasing lidar results accordingly. We describe our technique for measuring the phase centers of a GPS antenna installed atop an aircraft, and show results which demonstrate that different installations can indeed alter the phase centers significantly.
Estimates of Armington parameters for a landlocked economy
Nganou, Jean-Pascal
2005-01-01
One of the most debated issues in the Computable General Equilibrium (CGE) literature concerns the validity of the key behavioral parameters used in the calibration process. CGE modelers seldom estimate those parameters, preferring to borrow from the handful of estimates available in the literature. The lack of data is often cited as a reason for this type of modus operandi (technique). Estimating key parameters is very crucial since CGE results are quite sensitive to parameter specification....
Yang, Zhongwen; Hsu, Kuolin; Sorooshian, Soroosh; Xu, Xinyi; Braithwaite, Dan; Verbist, Koen M. J.
2016-04-01
Satellite-based precipitation estimates (SPEs) are promising alternative precipitation data for climatic and hydrological applications, especially for regions where ground-based observations are limited. However, existing satellite-based rainfall estimations are subject to systematic biases. This study aims to adjust the biases in the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS) rainfall data over Chile, using gauge observations as reference. A novel bias adjustment framework, termed QM-GW, is proposed based on the nonparametric quantile mapping approach and a Gaussian weighting interpolation scheme. The PERSIANN-CCS precipitation estimates (daily, 0.04°×0.04°) over Chile are adjusted for the period of 2009-2014. The historical data (satellite and gauge) for 2009-2013 are used to calibrate the methodology; nonparametric cumulative distribution functions of satellite and gauge observations are estimated at every 1°×1° box region. One year (2014) of gauge data was used for validation. The results show that the biases of the PERSIANN-CCS precipitation data are effectively reduced. The spatial patterns of adjusted satellite rainfall show high consistency to the gauge observations, with reduced root-mean-square errors and mean biases. The systematic biases of the PERSIANN-CCS precipitation time series, at both monthly and daily scales, are removed. The extended validation also verifies that the proposed approach can be applied to adjust SPEs into the future, without further need for ground-based measurements. This study serves as a valuable reference for the bias adjustment of existing SPEs using gauge observations worldwide.
Allen Rodrigo
2006-01-01
Full Text Available Using the structured serial coalescent with Bayesian MCMC and serial samples, we estimate population size when some demes are not sampled or are hidden, ie ghost demes. It is found that even with the presence of a ghost deme, accurate inference was possible if the parameters are estimated with the true model. However with an incorrect model, estimates were biased and can be positively misleading. We extend these results to the case where there are sequences from the ghost at the last time sample. This case can arise in HIV patients, when some tissue samples and viral sequences only become available after death. When some sequences from the ghost deme are available at the last sampling time, estimation bias is reduced and accurate estimation of parameters associated with the ghost deme is possible despite sampling bias. Migration rates for this case are also shown to be good estimates when migration values are low.
THEORETICAL ANALYSIS AND PRACTICE ON THE SELECTION OF KEY PARAMETERS FOR HORIZONTAL BIAS BURNER
刘泰生; 许晋源
2003-01-01
The air flow ratio and the pulverized-coal mass flux ratio between the rich and lean sides are the key parameters of horizontal bias burner. In order to realize high combustion efficiency, excellent stability of ignition, low NOx emission and safe operation, six principal demands are presented on the selection of key parameters. An analytical model is established on the basis of the demands, the fundamentals of combustion and the operation results. An improved horizontal bias burner is also presented and applied. The experiment and numerical simulation results show the improved horizontal bias burner can realize proper key parameters, lower NOx emission, high combustion efficiency and excellent performance of part load operation without oil support. It also can reduce the circumfluence and low velocity zone existing at the downstream sections of vanes, and avoid the burnout of the lean primary-air nozzle and the jam in the lean primary-air channel. The operation and test results verify the reasonableness and feasibility of the analytical model.
Closed-form kinetic parameter estimation solution to the truncated data problem
In a dedicated cardiac single photon emission computed tomography (SPECT) system, the detectors are focused on the heart and the background is truncated in the projections. Reconstruction using truncated data results in biased images, leading to inaccurate kinetic parameter estimates. This paper has developed a closed-form kinetic parameter estimation solution to the dynamic emission imaging problem. This solution is insensitive to the bias in the reconstructed images that is caused by the projection data truncation. This paper introduces two new ideas: (1) it includes background bias as an additional parameter to estimate, and (2) it presents a closed-form solution for compartment models. The method is based on the following two assumptions: (i) the amount of the bias is directly proportional to the truncated activities in the projection data, and (ii) the background concentration is directly proportional to the concentration in the myocardium. In other words, the method assumes that the image slice contains only the heart and the background, without other organs, that the heart is not truncated, and that the background radioactivity is directly proportional to the radioactivity in the blood pool. As long as the background activity can be modeled, the proposed method is applicable regardless of the number of compartments in the model. For simplicity, the proposed method is presented and verified using a single compartment model with computer simulations using both noiseless and noisy projections.
Parameter estimation for estimation of bottom hole pressure during drilling.
Vea, Hans Kristian
2009-01-01
In this thesis we examine four bottom hole pressure estimators based on adaptive estimation of the friction pressure for the drill string and the annulus. Knowledge about the bottom hole pressure is crucial to achieve security and commercial objectives. Bottom hole pressure measurements transmitted by mud pulse telemetry have limited bandwidth and it is common to use additional models to estimate the bottom hole pressure when measurements are unavailable. The motivation for an adaptive approa...
On drift parameter estimation in models with fractional Brownian motion
Kozachenko, Yuriy; Mishura, Yuliya
2011-01-01
We consider a stochastic differential equation involving standard and fractional Brownian motion with unknown drift parameter to be estimated. We investigate the standard maximum likelihood estimate of the drift parameter, two non-standard estimates and three estimates for the sequential estimation. Model strong consistency and some other properties are proved. The linear model and Ornstein-Uhlenbeck model are studied in detail. As an auxiliary result, an asymptotic behavior of the fractional derivative of the fractional Brownian motion is established.
Parameter estimation in dynamic Casimir force measurements with known periodicity
Cui, Song, E-mail: cuis@imre.a-star.edu.sg [Institute of Materials Research and Engineering, 3 Research Link, Singapore 117602 (Singapore); Soh, Yeng Chai, E-mail: eycsoh@ntu.edu.sg [School of Electrical and Electronics Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798 (Singapore)
2011-12-05
It is important to have an accurate estimate of the unknown parameters such as the separation distance between interacting materials in Casimir force measurements. Current methods tend to produce large estimation errors. In this Letter, we present a novel method based on an adaptive control approach to estimate the unknown parameters using large amplitude dynamic Casimir measurements at separation distances of below 1 μm where both electrostatic force and Casimir force are significant. The estimate is proved to be accurate and the effectiveness of our method is demonstrated via a numerical example. -- Highlights: ► Unknown parameters like separation gap are nonlinearly parameterized in Casimir force measurements ► A two-stage parameter estimation method is proposed to estimate unknown parameters accurately. ► Our method is proved to be effective by theoretical derivation and simulations. ► Our method can be applied to a broad range of nonlinear parameter estimation problems.
Adaptive on-line estimation and control of overlay tool bias
Martinez, Victor M.; Finn, Karen; Edgar, Thomas F.
2003-06-01
Modern lithographic manufacturing processes rely on various types of exposure tools, used in a mix-and-match fashion. The motivation to use older tools alongside state-of-the-art tools is lower cost and one of the tradeoffs is a degradation in overlay performance. While average prices of semiconductor products continue to fall, the cost of manufacturing equipment rises with every product generation. Lithography processing, including the cost of ownership for tools, accounts for roughly 30% of the wafer processing costs, thus the importance of mix-and-match strategies. Exponentially Weighted Moving Average (EWMA) run-by-run controllers are widely used in the semiconductor manufacturing industry. This type of controller has been implemented successfully in volume manufacturing, improving Cpk values dramatically in processes like photolithography and chemical mechanical planarization. This simple, but powerful control scheme is well suited for adding corrections to compensate for Overlay Tool Bias (OTB). We have developed an adaptive estimation technique to compensate for overlay variability due to differences in the processing tools. The OTB can be dynamically calculated for each tool, based on the most recent measurements available, and used to correct the control variables. One approach to tracking the effect of different tools is adaptive modeling and control. The basic premise of an adaptive system is to change or adapt the controller as the operating conditions of the system change. Using closed-loop data, the adaptive control algorithm estimates the controller parameters using a recursive estimation technique. Once an updated model of the system is available, modelbased control becomes feasible. In the simplest scenario, the control law can be reformulated to include the current state of the tool (or its estimate) to compensate dynamically for OTB. We have performed simulation studies to predict the impact of deploying this strategy in production. The results
Estimation of Kinetic Parameters in an Automotive SCR Catalyst Model
Åberg, Andreas; Widd, Anders; Abildskov, Jens;
2016-01-01
A challenge during the development of models for simulation of the automotive Selective Catalytic Reduction catalyst is the parameter estimation of the kinetic parameters, which can be time consuming and problematic. The parameter estimation is often carried out on small-scale reactor tests, or p...
An observation on the bias in clinic-based estimates of malnutrition rates
Margaret E. Grosh; Fox, Kristin; Jackson, Maria
1991-01-01
Clinic-based data on malnutrition are the most readily available for following malnutrition levels and trends in most countries, but there is a bias inherent in clinic-based estimates of malnutrition rates. The authors compare annual clinic-based malnutrition data and those from four household surveys in Jamaica. The clinic data give lower estimates of malnutrition than the survey data in all four cases - significantly so in three. The size of the bias was variable over time, so the clinic da...
On the shear estimation bias induced by the spatial variation of colour across galaxy profiles
Semboloni, Elisabetta; Huang, Zhuoyi; Cardone, Vincenzo; Cropper, Mark; Joachimi, Benjamin; Kitching, Thomas; Kuijken, Konrad; Lombardi, Marco; Maoli, Roberto; Mellier, Yannick; Miller, Lance; Rhodes, Jason; Scaramella, Roberto; Schrabback, Tim; Velander, Malin
2012-01-01
The spatial variation of the colour of a galaxy may introduce a bias in the measurement of its shape if the PSF profile depends on wavelength. We study how this bias depends on the properties of the PSF and the galaxies themselves. The bias depends on the scales used to estimate the shape, which may be used to optimise methods to reduce the bias. Here we develop a general approach to quantify the bias. Although applicable to any weak lensing survey, we focus on the implications for the ESA Euclid mission. Based on our study of synthetic galaxies we find that the bias is a few times 10^-3 for a typical galaxy observed by Euclid. Consequently, it cannot be neglected and needs to be accounted for. We demonstrate how one can do so using spatially resolved observations of galaxies in two filters. We show that HST observations in the F606W and F814W filters allow us to model and reduce the bias by an order of magnitude, sufficient to meet Euclid's scientific requirements. The precision of the correction is ultimate...
METHOD ON ESTIMATION OF DRUG'S PENETRATED PARAMETERS
刘宇红; 曾衍钧; 许景锋; 张梅
2004-01-01
Transdermal drug delivery system (TDDS) is a new method for drug delivery. The analysis of plenty of experiments in vitro can lead to a suitable mathematical model for the description of the process of the drug's penetration through the skin, together with the important parameters that are related to the characters of the drugs.After the research work of the experiments data,a suitable nonlinear regression model was selected. Using this model, the most important parameter-penetrated coefficient of 20 drugs was computed.In the result one can find, this work supports the theory that the skin can be regarded as singular membrane.
Estimation of motility parameters from trajectory data
Vestergaard, Christian L.; Pedersen, Jonas Nyvold; Mortensen, Kim I.;
2015-01-01
Given a theoretical model for a self-propelled particle or micro-organism, how does one optimally determine the parameters of the model from experimental data in the form of a time-lapse recorded trajectory? For very long trajectories, one has very good statistics, and optimality may matter little...... which similar results may be obtained also for self-propelled particles....
M-Testing Using Finite and Infinite Dimensional Parameter Estimators
White, Halbert; Hong, Yongmiao
1999-01-01
The m-testing approach provides a general and convenient framework in which to view and construct specification tests for econometric models. Previous m-testing frameworks only consider test statistics that involve finite dimensional parameter estimators and infinite dimensional parameter estimators affecting the limit distribution of the m-test statistics. In this paper we propose a new m-testing framework using both finite and infinite dimensional parameter estimators, where the latter may ...
On-line parameter estimation of a magnetic bearing
Delpoux, Romain; Floquet, Thierry
2011-01-01
This article presents a parameter estimation algorithm for a magnetic bearing. Such process have strongly nonlinear dynamics andare inherently unstable systems. A simplified model of the magnetic bearing is developed in order to be able to estimate certain parameters. These parameters are difficult to measure, and may slightly vary over time. The expression of the estimates is written as a function of integrals of the inputs and outputs of the system. The experiments show a fast and robust on...
Towards physics responsible for large-scale Lyman-$\\alpha$ forest bias parameters
Cieplak, Agnieszka M
2015-01-01
Using a series of carefully constructed numerical experiments based on hydrodynamic cosmological SPH simulations, we attempt to build an intuition for the relevant physics behind the large scale density ($b_\\delta$) and velocity gradient ($b_\\eta$) biases of the Lyman-$\\alpha$ forest. Starting with the fluctuating Gunn-Peterson approximation applied to the smoothed total density field in real-space, and progressing through redshift-space with no thermal broadening, redshift-space with thermal broadening and hydrodynamicaly simulated baryon fields, we investigate how approximations found in the literature fare. We find that Seljak's 2012 analytical formulae for these bias parameters work surprisingly well in the limit of no thermal broadening and linear redshift-space distortions. We also show that his $b_\\eta$ formula is exact in the limit of no thermal broadening. Since introduction of thermal broadening significantly affects its value, we speculate that a combination of large-scale measurements of $b_\\eta$ ...
Systematic Angle Random Walk Estimation of the Constant Rate Biased Ring Laser Gyro
Guohu Feng
2013-02-01
Full Text Available An actual account of the angle random walk (ARW coefficients of gyros in the constant rate biased rate ring laser gyro (RLG inertial navigation system (INS is very important in practical engineering applications. However, no reported experimental work has dealt with the issue of characterizing the ARW of the constant rate biased RLG in the INS. To avoid the need for high cost precise calibration tables and complex measuring set-ups, the objective of this study is to present a cost-effective experimental approach to characterize the ARW of the gyros in the constant rate biased RLG INS. In the system, turntable dynamics and other external noises would inevitably contaminate the measured RLG data, leading to the question of isolation of such disturbances. A practical observation model of the gyros in the constant rate biased RLG INS was discussed, and an experimental method based on the fast orthogonal search (FOS for the practical observation model to separate ARW error from the RLG measured data was proposed. Validity of the FOS-based method was checked by estimating the ARW coefficients of the mechanically dithered RLG under stationary and turntable rotation conditions. By utilizing the FOS-based method, the average ARW coefficient of the constant rate biased RLG in the postulate system is estimated. The experimental results show that the FOS-based method can achieve high denoising ability. This method estimate the ARW coefficients of the constant rate biased RLG in the postulate system accurately. The FOS-based method does not need precise calibration table with high cost and complex measuring set-up, and Statistical results of the tests will provide us references in engineering application of the constant rate biased RLG INS.
Cosmological parameter extraction and biases from type Ia supernova magnitude evolution
Linden, S.; Virey, J.-M.; Tilquin, A.
2009-11-01
We study different one-parametric models of type Ia supernova magnitude evolution on cosmic time scales. Constraints on cosmological and supernova evolution parameters are obtained by combined fits on the actual data coming from supernovae, the cosmic microwave background, and baryonic acoustic oscillations. We find that the best-fit values imply supernova magnitude evolution such that high-redshift supernovae appear some percent brighter than would be expected in a standard cosmos with a dark energy component. However, the errors on the evolution parameters are of the same order, and data are consistent with nonevolving magnitudes at the 1σ level, except for special cases. We simulate a future data scenario where SN magnitude evolution is allowed for, and neglect the possibility of such an evolution in the fit. We find the fiducial models for which the wrong model assumption of nonevolving SN magnitude is not detectable, and for which biases on the fitted cosmological parameters are introduced at the same time. Of the cosmological parameters, the overall mass density ΩM has the strongest chances to be biased due to the wrong model assumption. Whereas early-epoch models with a magnitude offset Δ m˜ z2 show up to be not too dangerous when neglected in the fitting procedure, late epoch models with Δ m˜√{z} have high chances of undetectably biasing the fit results. Centre de Physique Théorique is UMR 6207 - “Unité Mixte de Recherche” of CNRS and of the Universities “de Provence”, “de la Mediterranée”, and “du Sud Toulon-Var” - Laboratory affiliated with FRUMAM (FR2291).
Parameter estimation using compensatory neural networks
M Sinha; P K Kalra; K Kumar
2000-04-01
Proposed here is a new neuron model, a basis for Compensatory Neural Network Architecture (CNNA), which not only reduces the total number of interconnections among neurons but also reduces the total computing time for training. The suggested model has properties of the basic neuron model as well as the higher neuron model (multiplicative aggregation function). It can adapt to standard neuron and higher order neuron, as well as a combination of the two. This approach is found to estimate the orbit with accuracy significantly better than Kalman Filter (KF) and Feedforward Multilayer Neural Network (FMNN) (also simply referred to as Artificial Neural Network, ANN) with lambda-gamma learning. The typical simulation runs also bring out the superiority of the proposed scheme over Kalman filter from the standpoint of computation time and the amount of data needed for the desired degree of estimated accuracy for the specific problem of orbit determination.
Estimation of temperature impact on gamma-induced degradation parameters of N-channel MOS transistor
The physical parameters of MOS transistors can be impressed by ionizing radiation and that leads to circuit degradation and failure. These effects require analyzing the basic mechanism that results in the buildup of induced defect in radiation environments. The reliable estimation also needs to consider external factors, particularly temperature fluctuations. I–V characteristic of the device was obtained using a temperature-dependent adapted form of charge-sheet model under heating cycle during irradiation with several ionizing dose levels at different gate biases. In this work, the analytical calculation for estimating the irradiation temperature impact on gamma-induced degradation parameters of N-channel MOS transistors at different gate biases was investigated. The experimental measurement was done in order to verify and parameterize the analytical model calculations. The results indicated that inserting irradiation temperature in the calculations caused a significant variation in radiation-induced MOS transistor parameters such as threshold voltage shift and off-state leakage current. According to the results, these variations were about 10.1% and 23.4% for voltage shifts and leakage currents respectively during investigated heating cycle for total dose of 20 krad at 9 V gate bias. - Highlights: • Reliable radiation effect estimations require considering external factors. • Irradiation temperature impact on degradation parameters of N-MOS was investigated. • An analytical model was utilized based on time dependent buildup of defect charges. • Oxide and interface trapped charges varied with irradiation temperature
Muscle parameters estimation based on biplanar radiography.
Dubois, G; Rouch, P; Bonneau, D; Gennisson, J L; Skalli, W
2016-11-01
The evaluation of muscle and joint forces in vivo is still a challenge. Musculo-Skeletal (musculo-skeletal) models are used to compute forces based on movement analysis. Most of them are built from a scaled-generic model based on cadaver measurements, which provides a low level of personalization, or from Magnetic Resonance Images, which provide a personalized model in lying position. This study proposed an original two steps method to access a subject-specific musculo-skeletal model in 30 min, which is based solely on biplanar X-Rays. First, the subject-specific 3D geometry of bones and skin envelopes were reconstructed from biplanar X-Rays radiography. Then, 2200 corresponding control points were identified between a reference model and the subject-specific X-Rays model. Finally, the shape of 21 lower limb muscles was estimated using a non-linear transformation between the control points in order to fit the muscle shape of the reference model to the X-Rays model. Twelfth musculo-skeletal models were reconstructed and compared to their reference. The muscle volume was not accurately estimated with a standard deviation (SD) ranging from 10 to 68%. However, this method provided an accurate estimation the muscle line of action with a SD of the length difference lower than 2% and a positioning error lower than 20 mm. The moment arm was also well estimated with SD lower than 15% for most muscle, which was significantly better than scaled-generic model for most muscle. This method open the way to a quick modeling method for gait analysis based on biplanar radiography. PMID:27082150
Estimation of bias errors in angle-of-arrival measurements using platform motion
Grindlay, A.
1981-08-01
An algorithm has been developed to estimate the bias errors in angle-of-arrival measurements made by electromagnetic detection devices on-board a pitching and rolling platform. The algorithm assumes that continuous exact measurements of the platform's roll and pitch conditions are available. When the roll and pitch conditions are used to transform deck-plane angular measurements of a nearly fixed target's position to a stabilized coordinate system, the resulting stabilized coordinates (azimuth and elevation) should not vary with changes in the roll and pitch conditions. If changes do occur they are a result of bias errors in the measurement system and the algorithm which has been developed uses these changes to estimate the sense and magnitude of angular bias errors.
Estimating non-response bias in a survey on alcohol consumption: comparison of response waves
V.M. Lahaut; H.A.M. Jansen (Harrie); H. van de Mheen (Dike); H.F.L. Garretsen (Henk); J.E. Verdurmen; A. van Dijk (Bram)
2003-01-01
textabstractAIMS: According to 'the continuum of resistance model' late respondents can be used as a proxy for non-respondents in estimating non-response bias. In the present study, the validity of this model was explored and tested in three surveys on alcohol consumption. METHODS:
Belfadel, Djedjiga; Osborne, Richard; Bar-Shalom, Yaakov
2015-06-01
In this paper, an approach to bias estimation in the presence of measurement association uncertainty using common targets of opportunity, is developed. Data association is carried out before the estimation of sensor angle measurement biases. Consequently, the quality of data association is critical to the overall tracking performance. Data association becomes especially challenging if the sensors are passive. Mathematically, the problem can be formulated as a multidimensional optimization problem, where the objective is to maximize the generalized likelihood that the associated measurements correspond to common targets, based on target locations and sensor bias estimates. Applying gating techniques significantly reduces the size of this problem. The association likelihoods are evaluated using an exhaustive search after which an acceptance test is applied to each solution in order to obtain the optimal (correct) solution. We demonstrate the merits of this approach by applying it to a simulated tracking system, which consists of two satellites tracking a ballistic target. We assume the sensors are synchronized, their locations are known, and we estimate their orientation biases together with the unknown target locations.
The effect of beam intensity on the estimation bias of beam position
For the signals of the beam position monitor (BPM), the signal-to-noise ratio (SNR) is directly related to the beam intensity. Low beam intensity results in poor SNR. The random noise has a modulation effect on both the amplitude and phase of the BPM signals. Therefore, the beam position measurement has a certain random error. In the currently used BPM, time-averaging and waveform clipping are used to improve the measurement. The nonlinear signal processing results in a biased estimate of beam position. A statistical analysis was made to examine the effect of the SNR, which is determined by the beam intensity, on the estimation bias. The results of the analysis suggest that the estimation bias has a dependence not only on the beam position but also on beam intensity. Specifically, the dependence gets strong as the beam intensity decreases. This property has set a lower limit of the beam intensity range which the BPM's can handle. When the beam intensity is below that limit the estimation bias starts to vary dramatically, resulting in the BPMs failure. According to the analysis, the lowest beam intensity is that at which the SNR of the generated BPM signal is about 15 dB. The limit for NSEP BPM, for instance, is about 6Ell. The analysis may provide the BPM designers with some idea about the potential of the current BPM'S
Incremental parameter estimation of kinetic metabolic network models
Jia Gengjie
2012-11-01
Full Text Available Abstract Background An efficient and reliable parameter estimation method is essential for the creation of biological models using ordinary differential equation (ODE. Most of the existing estimation methods involve finding the global minimum of data fitting residuals over the entire parameter space simultaneously. Unfortunately, the associated computational requirement often becomes prohibitively high due to the large number of parameters and the lack of complete parameter identifiability (i.e. not all parameters can be uniquely identified. Results In this work, an incremental approach was applied to the parameter estimation of ODE models from concentration time profiles. Particularly, the method was developed to address a commonly encountered circumstance in the modeling of metabolic networks, where the number of metabolic fluxes (reaction rates exceeds that of metabolites (chemical species. Here, the minimization of model residuals was performed over a subset of the parameter space that is associated with the degrees of freedom in the dynamic flux estimation from the concentration time-slopes. The efficacy of this method was demonstrated using two generalized mass action (GMA models, where the method significantly outperformed single-step estimations. In addition, an extension of the estimation method to handle missing data is also presented. Conclusions The proposed incremental estimation method is able to tackle the issue on the lack of complete parameter identifiability and to significantly reduce the computational efforts in estimating model parameters, which will facilitate kinetic modeling of genome-scale cellular metabolism in the future.
Cosmological Parameter Extraction and Biases from Type Ia Supernova Magnitude Evolution
Linden, Sebastian; Tilquin, Andre
2009-01-01
We study different one-parametric models of type Ia Supernova magnitude evolution on cosmic time scales. Constraints on cosmological and Supernova evolution parameters are obtained by combined fits on the actual data coming from Supernovae, the cosmic microwave background, and baryonic acoustic oscillations. We find that data prefer a magnitude evolution such that high-redshift Supernova are brighter than would be expected in a standard cosmos with a dark energy component. Data however are consistent with non-evolving magnitudes at the one-sigma level, except special cases. We simulate a future data scenario where SN magnitude evolution is allowed for, and neglect the possibility of such an evolution in the fit. We find the fiducial models for which the wrong model assumption of non-evolving SN magnitude is not detectable, and for which at the same time biases on the fitted cosmological parameters are introduced. Of the cosmological parameters the overall mass density has the strongest chances to be biased du...
G Sasibhushana Rao
2007-10-01
The positional accuracy of the Global Positioning System (GPS)is limited due to several error sources.The major error is ionosphere.By augmenting the GPS,the Category I (CAT I)Precision Approach (PA)requirements can be achieved.The Space-Based Augmentation System (SBAS)in India is known as GPS Aided Geo Augmented Navigation (GAGAN).One of the prominent errors in GAGAN that limits the positional accuracy is instrumental biases.Calibration of these biases is particularly important in achieving the CAT I PA landings.In this paper,a new algorithm is proposed to estimate the instrumental biases by modelling the TEC using 4th order polynomial.The algorithm uses values corresponding to a single station for one month period and the results conﬁrm the validity of the algorithm.The experimental results indicate that the estimation precision of the satellite-plus-receiver instrumental bias is of the order of ± 0.17 nsec.The observed mean bias error is of the order − 3.638 nsec and − 4.71 nsec for satellite 1 and 31 respectively.It is found that results are consistent over the period.
Parameter and Uncertainty Estimation in Groundwater Modelling
Jensen, Jacob Birk
The data basis on which groundwater models are constructed is in general very incomplete, and this leads to uncertainty in model outcome. Groundwater models form the basis for many, often costly decisions and if these are to be made on solid grounds, the uncertainty attached to model results must...... be quantified. This study was motivated by the need to estimate the uncertainty involved in groundwater models.Chapter 2 presents an integrated surface/subsurface unstructured finite difference model that was developed and applied to a synthetic case study.The following two chapters concern calibration...... was applied.Capture zone modelling was conducted on a synthetic stationary 3-dimensional flow problem involving river, surface and groundwater flow. Simulated capture zones were illustrated as likelihood maps and compared with a deterministic capture zones derived from a reference model. The results showed...
Control and Estimation of Distributed Parameter Systems
Kappel, F; Kunisch, K
1998-01-01
Consisting of 23 refereed contributions, this volume offers a broad and diverse view of current research in control and estimation of partial differential equations. Topics addressed include, but are not limited to - control and stability of hyperbolic systems related to elasticity, linear and nonlinear; - control and identification of nonlinear parabolic systems; - exact and approximate controllability, and observability; - Pontryagin's maximum principle and dynamic programming in PDE; and - numerics pertinent to optimal and suboptimal control problems. This volume is primarily geared toward control theorists seeking information on the latest developments in their area of expertise. It may also serve as a stimulating reader to any researcher who wants to gain an impression of activities at the forefront of a vigorously expanding area in applied mathematics.
Towards physics responsible for large-scale Lyman-α forest bias parameters
Cieplak, Agnieszka M.; Slosar, Anže
2016-03-01
Using a series of carefully constructed numerical experiments based on hydrodynamic cosmological SPH simulations, we attempt to build an intuition for the relevant physics behind the large scale density (bδ) and velocity gradient (bη) biases of the Lyman-α forest. Starting with the fluctuating Gunn-Peterson approximation applied to the smoothed total density field in real-space, and progressing through redshift-space with no thermal broadening, redshift-space with thermal broadening and hydrodynamically simulated baryon fields, we investigate how approximations found in the literature fare. We find that Seljak's 2012 analytical formulae for these bias parameters work surprisingly well in the limit of no thermal broadening and linear redshift-space distortions. We also show that his bη formula is exact in the limit of no thermal broadening. Since introduction of thermal broadening significantly affects its value, we speculate that a combination of large-scale measurements of bη and the small scale flux PDF might be a sensitive probe of the thermal state of the IGM. We find that large-scale biases derived from the smoothed total matter field are within 10-20% to those based on hydrodynamical quantities, in line with other measurements in the literature.
FUZZY SUPERNOVA TEMPLATES. II. PARAMETER ESTIMATION
Wide-field surveys will soon be discovering Type Ia supernovae (SNe) at rates of several thousand per year. Spectroscopic follow-up can only scratch the surface for such enormous samples, so these extensive data sets will only be useful to the extent that they can be characterized by the survey photometry alone. In a companion paper we introduced the Supernova Ontology with Fuzzy Templates (SOFT) method for analyzing SNe using direct comparison to template light curves, and demonstrated its application for photometric SN classification. In this work we extend the SOFT method to derive estimates of redshift and luminosity distance for Type Ia SNe, using light curves from the Sloan Digital Sky Survey (SDSS) and Supernova Legacy Survey (SNLS) as a validation set. Redshifts determined by SOFT using light curves alone are consistent with spectroscopic redshifts, showing an rms scatter in the residuals of rmsz = 0.051. SOFT can also derive simultaneous redshift and distance estimates, yielding results that are consistent with the currently favored ΛCDM cosmological model. When SOFT is given spectroscopic information for SN classification and redshift priors, the rms scatter in Hubble diagram residuals is 0.18 mag for the SDSS data and 0.28 mag for the SNLS objects. Without access to any spectroscopic information, and even without any redshift priors from host galaxy photometry, SOFT can still measure reliable redshifts and distances, with an increase in the Hubble residuals to 0.37 mag for the combined SDSS and SNLS data set. Using Monte Carlo simulations, we predict that SOFT will be able to improve constraints on time-variable dark energy models by a factor of 2-3 with each new generation of large-scale SN surveys.
Parameter Estimation of the T-Book
This paper summarizes the statistical assumptions and methods that have been used in the work on the T-book, a reliability data handbook which is used in safety analyses of nuclear power plants in Sweden and in the Swedish design plants in Finland. The author discusses the conceptual framework for the description and handling of uncertainty. He briefly outlines the two-stage 'Bayes empirical Bayes' method. To express the inherent tail-uncertainty in the distribution of failure rate, a class of contaminated distributions with three (hyper) parameters is proposed. Attention is focused on the properties of this T-book approach with regard to how it can be used to describe the parametric uncertainties, how uncertainty distributions can be used for predictive purposes, and how distributions can be updated
Sample Size and Item Parameter Estimation Precision When Utilizing the One-Parameter "Rasch" Model
Custer, Michael
2015-01-01
This study examines the relationship between sample size and item parameter estimation precision when utilizing the one-parameter model. Item parameter estimates are examined relative to "true" values by evaluating the decline in root mean squared deviation (RMSD) and the number of outliers as sample size increases. This occurs across…
Miller, B.; O'Shaughnessy, R.; Littenberg, T. B.; Farr, B.
2015-08-01
Reliable low-latency gravitational wave parameter estimation is essential to target limited electromagnetic follow-up facilities toward astrophysically interesting and electromagnetically relevant sources of gravitational waves. In this study, we examine the trade-off between speed and accuracy. Specifically, we estimate the astrophysical relevance of systematic errors in the posterior parameter distributions derived using a fast-but-approximate waveform model, SpinTaylorF2 (stf2), in parameter estimation with lalinference_mcmc. Though efficient, the stf2 approximation to compact binary inspiral employs approximate kinematics (e.g., a single spin) and an approximate waveform (e.g., frequency domain versus time domain). More broadly, using a large astrophysically motivated population of generic compact binary merger signals, we report on the effectualness and limitations of this single-spin approximation as a method to infer parameters of generic compact binary sources. For most low-mass compact binary sources, we find that the stf2 approximation estimates compact binary parameters with biases comparable to systematic uncertainties in the waveform. We illustrate by example the effect these systematic errors have on posterior probabilities most relevant to low-latency electromagnetic follow-up: whether the secondary has a mass consistent with a neutron star (NS); whether the masses, spins, and orbit are consistent with that neutron star's tidal disruption; and whether the binary's angular momentum axis is oriented along the line of sight.
Parameter Estimates in Differential Equation Models for Chemical Kinetics
Winkel, Brian
2011-01-01
We discuss the need for devoting time in differential equations courses to modelling and the completion of the modelling process with efforts to estimate the parameters in the models using data. We estimate the parameters present in several differential equation models of chemical reactions of order n, where n = 0, 1, 2, and apply more general…
A neural network applied to estimate Burr XII distribution parameters
The Burr XII distribution can closely approximate many other well-known probability density functions such as the normal, gamma, lognormal, exponential distributions as well as Pearson type I, II, V, VII, IX, X, XII families of distributions. Considering a wide range of shape and scale parameters of the Burr XII distribution, it can have an important role in reliability modeling, risk analysis and process capability estimation. However, estimating parameters of the Burr XII distribution can be a complicated task and the use of conventional methods such as maximum likelihood estimation (MLE) and moment method (MM) is not straightforward. Some tables to estimate Burr XII parameters have been provided by Burr (1942) but they are not adequate for many purposes or data sets. Burr tables contain specific values of skewness and kurtosis and their corresponding Burr XII parameters. Using interpolation or extrapolation to estimate other values may provide inappropriate estimations. In this paper, we present a neural network to estimate Burr XII parameters for different values of skewness and kurtosis as inputs. A trained network is presented, and one can use it without previous knowledge about neural networks to estimate Burr XII distribution parameters. Accurate estimation of the Burr parameters is an extension of simulation studies.
Parameter Estimation for a Computable General Equilibrium Model
Arndt, Channing; Robinson, Sherman; Tarp, Finn
We introduce a maximum entropy approach to parameter estimation for computable general equilibrium (CGE) models. The approach applies information theory to estimating a system of nonlinear simultaneous equations. It has a number of advantages. First, it imposes all general equilibrium constraints...... to estimating a CGE model of Mozambique....... Second, it permits incorporation of prior information on parameter values. Third, it can be applied in the absence of copious data. Finally, it supplies measures of the capacity of the model to reproduce the historical record and the statistical significance of parameter estimates. The method is applied...
Li, Chunming; Gore, John C; Davatzikos, Christos
2014-09-01
This paper proposes a new energy minimization method called multiplicative intrinsic component optimization (MICO) for joint bias field estimation and segmentation of magnetic resonance (MR) images. The proposed method takes full advantage of the decomposition of MR images into two multiplicative components, namely, the true image that characterizes a physical property of the tissues and the bias field that accounts for the intensity inhomogeneity, and their respective spatial properties. Bias field estimation and tissue segmentation are simultaneously achieved by an energy minimization process aimed to optimize the estimates of the two multiplicative components of an MR image. The bias field is iteratively optimized by using efficient matrix computations, which are verified to be numerically stable by matrix analysis. More importantly, the energy in our formulation is convex in each of its variables, which leads to the robustness of the proposed energy minimization algorithm. The MICO formulation can be naturally extended to 3D/4D tissue segmentation with spatial/sptatiotemporal regularization. Quantitative evaluations and comparisons with some popular softwares have demonstrated superior performance of MICO in terms of robustness and accuracy. PMID:24928302
Parameter and State Estimator for State Space Models
Ruifeng Ding
2014-01-01
Full Text Available This paper proposes a parameter and state estimator for canonical state space systems from measured input-output data. The key is to solve the system state from the state equation and to substitute it into the output equation, eliminating the state variables, and the resulting equation contains only the system inputs and outputs, and to derive a least squares parameter identification algorithm. Furthermore, the system states are computed from the estimated parameters and the input-output data. Convergence analysis using the martingale convergence theorem indicates that the parameter estimates converge to their true values. Finally, an illustrative example is provided to show that the proposed algorithm is effective.
Estimating parameters of chaotic systems under noise-induced synchronization
Kim et al. introduced in 2002 [Kim CM, Rim S, Kye WH. Sequential synchronization of chaotic systems with an application to communication. Phys Rev Lett 2002;88:014103] a hierarchically structured communication scheme based on sequential synchronization, a modification of noise-induced synchronization (NIS). We propose in this paper an approach that can estimate the parameters of chaotic systems under NIS. In this approach, a dimensionally-expanded parameter estimating system is first constructed according to the original chaotic system. By feeding chaotic transmitted signal and external driving signal, the parameter estimating system can be synchronized with the original chaotic system. Consequently, parameters would be estimated. Numerical simulation shows that this approach can estimate all the parameters of chaotic systems under two feeding modes, which implies the potential weakness of the chaotic communication scheme under NIS or sequential synchronization.
Estimating parameters of chaotic systems under noise-induced synchronization
Wu Xiaogang [Institute of PR and AI, Huazhong University of Science and Technology, Wuhan 430074 (China)], E-mail: seanwoo@mail.hust.edu.cn; Wang Zuxi [Institute of PR and AI, Huazhong University of Science and Technology, Wuhan 430074 (China)
2009-01-30
Kim et al. introduced in 2002 [Kim CM, Rim S, Kye WH. Sequential synchronization of chaotic systems with an application to communication. Phys Rev Lett 2002;88:014103] a hierarchically structured communication scheme based on sequential synchronization, a modification of noise-induced synchronization (NIS). We propose in this paper an approach that can estimate the parameters of chaotic systems under NIS. In this approach, a dimensionally-expanded parameter estimating system is first constructed according to the original chaotic system. By feeding chaotic transmitted signal and external driving signal, the parameter estimating system can be synchronized with the original chaotic system. Consequently, parameters would be estimated. Numerical simulation shows that this approach can estimate all the parameters of chaotic systems under two feeding modes, which implies the potential weakness of the chaotic communication scheme under NIS or sequential synchronization.
The effect of heart motion on parameter bias in dynamic cardiac SPECT
Dynamic cardiac SPECT can be used to estimate kinetic rate parameters which describe the wash-in and wash-out of tracer activity between the blood and the myocardial tissue. These kinetic parameters can in turn be correlated to myocardial perfusion. There are, however, many physical aspects associated with dynamic SPECT which can introduce errors into the estimates. This paper describes a study which investigates the effect of heart motion on kinetic parameter estimates. Dynamic SPECT simulations are performed using a beating version of the MCAT phantom. The results demonstrate that cardiac motion has a significant effect on the blood, tissue, and background content of regions of interest. This in turn affects estimates of wash-in, while it has very little effect on estimates of wash-out. The effect of cardiac motion on parameter estimates appears not to be as great as effects introduced by photon noise and geometric collimator response. It is also shown that cardiac motion results in little extravascular contamination of the left ventricle blood region of interest
Accurate Parameter Estimation for Unbalanced Three-Phase System
Yuan Chen; Hing Cheung So
2014-01-01
Smart grid is an intelligent power generation and control console in modern electricity networks, where the unbalanced three-phase power system is the commonly used model. Here, parameter estimation for this system is addressed. After converting the three-phase waveforms into a pair of orthogonal signals via the α β-transformation, the nonlinear least squares (NLS) estimator is developed for accurately finding the frequency, phase, and voltage parameters. The estimator is realized by the Newt...
Efficient Estimation of Nonlinear Finite Population Parameters Using Nonparametrics
Goga, Camelia; Ruiz-Gazen, Anne
2012-01-01
Currently, the high-precision estimation of nonlinear parameters such as Gini indices, low-income proportions or other measures of inequality is particularly crucial. In the present paper, we propose a general class of estimators for such parameters that take into account univariate auxiliary information assumed to be known for every unit in the population. Through a nonparametric model-assisted approach, we construct a unique system of survey weights that can be used to estimate any nonlinea...
Quantum estimation of coupled parameters and the role of entanglement
Kok, Pieter; Dunningham, Jacob; Ralph, Jason F.
2015-01-01
The quantum Cramer-Rao bound places a limit on the mean square error of a parameter estimation procedure, and its numerical value is determined by the quantum Fisher information. For single parameters, this leads to the well- known Heisenberg limit that surpasses the classical shot-noise limit. When estimating multiple parameters, the situation is more complicated and the quantum Cramer-Rao bound is generally not attainable. In such cases, the use of entanglement typically still offers an enh...
Parameter Estimation of Photovoltaic Models via Cuckoo Search
Jieming Ma; Ting, T. O.; Ka Lok Man; Nan Zhang; Sheng-Uei Guan; Wong, Prudence W. H.
2013-01-01
Since conventional methods are incapable of estimating the parameters of Photovoltaic (PV) models with high accuracy, bioinspired algorithms have attracted significant attention in the last decade. Cuckoo Search (CS) is invented based on the inspiration of brood parasitic behavior of some cuckoo species in combination with the Lévy flight behavior. In this paper, a CS-based parameter estimation method is proposed to extract the parameters of single-diode models for commercial PV generators. S...
MLEP: an R package for exploring the maximum likelihood estimates of penetrance parameters
Sugaya Yuki
2012-08-01
Full Text Available Abstract Background Linkage analysis is a useful tool for detecting genetic variants that regulate a trait of interest, especially genes associated with a given disease. Although penetrance parameters play an important role in determining gene location, they are assigned arbitrary values according to the researcher’s intuition or as estimated by the maximum likelihood principle. Several methods exist by which to evaluate the maximum likelihood estimates of penetrance, although not all of these are supported by software packages and some are biased by marker genotype information, even when disease development is due solely to the genotype of a single allele. Findings Programs for exploring the maximum likelihood estimates of penetrance parameters were developed using the R statistical programming language supplemented by external C functions. The software returns a vector of polynomial coefficients of penetrance parameters, representing the likelihood of pedigree data. From the likelihood polynomial supplied by the proposed method, the likelihood value and its gradient can be precisely computed. To reduce the effect of the supplied dataset on the likelihood function, feasible parameter constraints can be introduced into maximum likelihood estimates, thus enabling flexible exploration of the penetrance estimates. An auxiliary program generates a perspective plot allowing visual validation of the model’s convergence. The functions are collectively available as the MLEP R package. Conclusions Linkage analysis using penetrance parameters estimated by the MLEP package enables feasible localization of a disease locus. This is shown through a simulation study and by demonstrating how the package is used to explore maximum likelihood estimates. Although the input dataset tends to bias the likelihood estimates, the method yields accurate results superior to the analysis using intuitive penetrance values for disease with low allele frequencies. MLEP is
PARAMETER ESTIMATION IN LINEAR REGRESSION MODELS FOR LONGITUDINAL CONTAMINATED DATA
QianWeimin; LiYumei
2005-01-01
The parameter estimation and the coefficient of contamination for the regression models with repeated measures are studied when its response variables are contaminated by another random variable sequence. Under the suitable conditions it is proved that the estimators which are established in the paper are strongly consistent estimators.
Anupam Pathak
2014-11-01
Full Text Available Abstract: Problem Statement: The two-parameter exponentiated Rayleigh distribution has been widely used especially in the modelling of life time event data. It provides a statistical model which has a wide variety of application in many areas and the main advantage is its ability in the context of life time event among other distributions. The uniformly minimum variance unbiased and maximum likelihood estimation methods are the way to estimate the parameters of the distribution. In this study we explore and compare the performance of the uniformly minimum variance unbiased and maximum likelihood estimators of the reliability function R(t=P(X>t and P=P(X>Y for the two-parameter exponentiated Rayleigh distribution. Approach: A new technique of obtaining these parametric functions is introduced in which major role is played by the powers of the parameter(s and the functional forms of the parametric functions to be estimated are not needed. We explore the performance of these estimators numerically under varying conditions. Through the simulation study a comparison are made on the performance of these estimators with respect to the Biasness, Mean Square Error (MSE, 95% confidence length and corresponding coverage percentage. Conclusion: Based on the results of simulation study the UMVUES of R(t and ‘P’ for the two-parameter exponentiated Rayleigh distribution found to be superior than MLES of R(t and ‘P’.
Jørgensen, Bent; Clarice G.B. Demétrio; Kristensen, Erik; Banta, Gary T; Petersen, Hans Christian; Delefosse, Matthieu
2011-01-01
Abstract Estimation of Taylor?s power law for species abundance data may be performed by linear regression of the log empirical variances on the log means, but this method suffers from a problem of bias for sparse data. We show that the bias may be reduced by using a bias-corrected Pearson estimating function. Furthermore, we investigate a more general regression model allowing for site-specific covariates. This method may be efficiently implemented using a Newton scoring algorithm...
Parameter Estimation for Generalized Brownian Motion with Autoregressive Increments
Fendick, Kerry
2011-01-01
This paper develops methods for estimating parameters for a generalization of Brownian motion with autoregressive increments called a Brownian ray with drift. We show that a superposition of Brownian rays with drift depends on three types of parameters - a drift coefficient, autoregressive coefficients, and volatility matrix elements, and we introduce methods for estimating each of these types of parameters using multidimensional times series data. We also cover parameter estimation in the contexts of two applications of Brownian rays in the financial sphere: queuing analysis and option valuation. For queuing analysis, we show how samples of queue lengths can be used to estimate the conditional expectation functions for the length of the queue and for increments in its net input and lost potential output. For option valuation, we show how the Black-Scholes-Merton formula depends on the price of the security on which the option is written through estimates not only of its volatility, but also of a coefficient ...
Impacts of Different Types of Measurements on Estimating Unsaturatedflow Parameters
Shi, L.
2015-12-01
This study evaluates the value of different types of measurements for estimating soil hydraulic parameters. A numerical method based on ensemble Kalman filter (EnKF) is presented to solely or jointly assimilate point-scale soil water head data, point-scale soil water content data, surface soil water content data and groundwater level data. This study investigates the performance of EnKF under different types of data, the potential worth contained in these data, and the factors that may affect estimation accuracy. Results show that for all types of data, smaller measurements errors lead to faster convergence to the true values. Higher accuracy measurements are required to improve the parameter estimation if a large number of unknown parameters need to be identified simultaneously. The data worth implied by the surface soil water content data and groundwater level data is prone to corruption by a deviated initial guess. Surface soil moisture data are capable of identifying soil hydraulic parameters for the top layers, but exert less or no influence on deeper layers especially when estimating multiple parameters simultaneously. Groundwater level is one type of valuable information to infer the soil hydraulic parameters. However, based on the approach used in this study, the estimates from groundwater level data may suffer severe degradation if a large number of parameters must be identified. Combined use of two or more types of data is helpful to improve the parameter estimation.
Littenberg, Tyson B.; Farr, Ben; Coughlin, Scott; Kalogera, Vicky
2016-03-01
Among the most eagerly anticipated opportunities made possible by Advanced LIGO/Virgo are multimessenger observations of compact mergers. Optical counterparts may be short-lived so rapid characterization of gravitational wave (GW) events is paramount for discovering electromagnetic signatures. One way to meet the demand for rapid GW parameter estimation is to trade off accuracy for speed, using waveform models with simplified treatment of the compact objects’ spin. We report on the systematic errors in GW parameter estimation suffered when using different spin approximations to recover generic signals. Component mass measurements can be biased by \\gt 5σ using simple-precession waveforms and in excess of 20σ when non-spinning templates are employed. This suggests that electromagnetic observing campaigns should not take a strict approach to selecting which LIGO/Virgo candidates warrant follow-up observations based on low-latency mass estimates. For sky localization, we find that searched areas are up to a factor of ∼ 2 larger for non-spinning analyses, and are systematically larger for any of the simplified waveforms considered in our analysis. Distance biases for the non-precessing waveforms can be in excess of 100% and are largest when the spin angular momenta are in the orbital plane of the binary. We confirm that spin-aligned waveforms should be used for low-latency parameter estimation at the minimum. Including simple precession, though more computationally costly, mitigates biases except for signals with extreme precession effects. Our results shine a spotlight on the critical need for development of computationally inexpensive precessing waveforms and/or massively parallel algorithms for parameter estimation.
Parameter Estimation in Epidemiology: from Simple to Complex Dynamics
Aguiar, Maíra; Ballesteros, Sebastién; Boto, João Pedro; Kooi, Bob W.; Mateus, Luís; Stollenwerk, Nico
2011-09-01
We revisit the parameter estimation framework for population biological dynamical systems, and apply it to calibrate various models in epidemiology with empirical time series, namely influenza and dengue fever. When it comes to more complex models like multi-strain dynamics to describe the virus-host interaction in dengue fever, even most recently developed parameter estimation techniques, like maximum likelihood iterated filtering, come to their computational limits. However, the first results of parameter estimation with data on dengue fever from Thailand indicate a subtle interplay between stochasticity and deterministic skeleton. The deterministic system on its own already displays complex dynamics up to deterministic chaos and coexistence of multiple attractors.
A simulation of water pollution model parameter estimation
Kibler, J. F.
1976-01-01
A parameter estimation procedure for a water pollution transport model is elaborated. A two-dimensional instantaneous-release shear-diffusion model serves as representative of a simple transport process. Pollution concentration levels are arrived at via modeling of a remote-sensing system. The remote-sensed data are simulated by adding Gaussian noise to the concentration level values generated via the transport model. Model parameters are estimated from the simulated data using a least-squares batch processor. Resolution, sensor array size, and number and location of sensor readings can be found from the accuracies of the parameter estimates.
Pauwels, V. R. N.; DeLannoy, G. J. M.; Hendricks Franssen, H.-J.; Vereecken, H.
2013-01-01
In this paper, we present a two-stage hybrid Kalman filter to estimate both observation and forecast bias in hydrologic models, in addition to state variables. The biases are estimated using the discrete Kalman filter, and the state variables using the ensemble Kalman filter. A key issue in this multi-component assimilation scheme is the exact partitioning of the difference between observation and forecasts into state, forecast bias and observation bias updates. Here, the error covariances of the forecast bias and the unbiased states are calculated as constant fractions of the biased state error covariance, and the observation bias error covariance is a function of the observation prediction error covariance. In a series of synthetic experiments, focusing on the assimilation of discharge into a rainfall-runoff model, it is shown that both static and dynamic observation and forecast biases can be successfully estimated. The results indicate a strong improvement in the estimation of the state variables and resulting discharge as opposed to the use of a bias-unaware ensemble Kalman filter. Furthermore, minimal code modification in existing data assimilation software is needed to implement the method. The results suggest that a better performance of data assimilation methods should be possible if both forecast and observation biases are taken into account.
V. R. N. Pauwels
2013-04-01
Full Text Available In this paper, we present a two-stage hybrid Kalman filter to estimate both observation and forecast bias in hydrologic models, in addition to state variables. The biases are estimated using the Discrete Kalman Filter, and the state variables using the Ensemble Kalman Filter. A key issue in this multi-component assimilation scheme is the exact partitioning of the difference between observation and forecasts into state, forecast bias and observation bias updates. Here, the error covariances of the forecast bias and the unbiased states are calculated as constant fractions of the biased state error covariance, and the observation bias error covariance is a function of the observation prediction error covariance. In a series of synthetic experiments, focusing on the assimilation of discharge into a rainfall-runoff model, it is shown that both static and dynamic observation and forecast biases can be successfully estimated. The results indicate a strong improvement in the estimation of the state variables and resulting discharge as opposed to the use of a bias-unaware Ensemble Kalman Filter. The results suggest that a better performance of data assimilation methods should be possible if both forecast and observation biases are taken into account.
V. R. N. Pauwels
2013-09-01
Full Text Available In this paper, we present a two-stage hybrid Kalman filter to estimate both observation and forecast bias in hydrologic models, in addition to state variables. The biases are estimated using the discrete Kalman filter, and the state variables using the ensemble Kalman filter. A key issue in this multi-component assimilation scheme is the exact partitioning of the difference between observation and forecasts into state, forecast bias and observation bias updates. Here, the error covariances of the forecast bias and the unbiased states are calculated as constant fractions of the biased state error covariance, and the observation bias error covariance is a function of the observation prediction error covariance. In a series of synthetic experiments, focusing on the assimilation of discharge into a rainfall-runoff model, it is shown that both static and dynamic observation and forecast biases can be successfully estimated. The results indicate a strong improvement in the estimation of the state variables and resulting discharge as opposed to the use of a bias-unaware ensemble Kalman filter. Furthermore, minimal code modification in existing data assimilation software is needed to implement the method. The results suggest that a better performance of data assimilation methods should be possible if both forecast and observation biases are taken into account.
Evaluation of biases for inserted reactivity estimation of JCO criticality accident
Yamamoto, Toshihiro; Nakamura, Takemi; Miyoshi, Yoshinori [Japan Atomic Energy Research Inst., Tokai, Ibaraki (Japan). Tokai Research Establishment
2001-02-01
Biases in criticality calculation methods used in JCO criticality accident analyses were estimated to make accurate predictions of an inserted reactivity in the accident. MCNP 4B and pointwise cross section libraries based on JENDL-3.1, JENDL-3.2 and ENDF/B-VI were used for the criticality calculations. With these calculation methods, neutron effective multiplication factors were obtained for STACY critical experiments, which used 10 wt.% enriched aqueous uranium solutions, and for critical experiments performed at the Rocky Flats Plant, which used 93.2 wt.% enriched aqueous uranium solutions. As a result, biases in keff's for 18.8 wt.% enriched uranium solution of the JCO accident were estimated to be 0.0%, +1.2%, and 0.1% when using JENDL-3.1, JENDL-3.2 and ENDF/B-VI, respectively. (author)
Approaches to radar reflectivity bias correction to improve rainfall estimation in Korea
You, Cheol-Hwan; Kang, Mi-Young; Lee, Dong-In; Lee, Jung-Tae
2016-05-01
Three methods for determining the reflectivity bias of single polarization radar using dual polarization radar reflectivity and disdrometer data (i.e., the equidistance line, overlapping area, and disdrometer methods) are proposed and evaluated for two low-pressure rainfall events that occurred over the Korean Peninsula on 25 August 2014 and 8 September 2012. Single polarization radar reflectivity was underestimated by more than 12 and 7 dB in the two rain events, respectively. All methods improved the accuracy of rainfall estimation, except for one case where drop size distributions were not observed, as the precipitation system did not pass through the disdrometer location. The use of these bias correction methods reduced the RMSE by as much as 50 %. Overall, the most accurate rainfall estimates were obtained using the overlapping area method to correct radar reflectivity.
Response-Based Estimation of Sea State Parameters
Nielsen, Ulrik Dam
2007-01-01
Reliable estimation of the on-site sea state parameters is essential to decision support systems for safe navigation of ships. The sea state parameters can be estimated by Bayesian Modelling which uses complex-valued frequency response functions (FRF) to estimate the wave spectrum on the basis of...... measured ship responses. It is therefore interesting to investigate how the filtering aspect, introduced by FRF, affects the final outcome of the estimation procedures. The paper contains a study based on numerical generated time series, and the study shows that filtering has an influence on the...
Parameter estimation during a transient - application to BWR stability
Tambouratzis, T. [Institute of Nuclear Technology - Radiation Protection, NCSR ' Demokritos' , Aghia Paraskevi, Athens 153 10 (Greece)]. E-mail: tatiana@ipta.demokritos.gr; Antonopoulos-Domis, M. [Institute of Nuclear Technology - Radiation Protection, NCSR ' Demokritos' , Aghia Paraskevi, Athens 153 10 (Greece)
2004-12-01
The estimation of system parameters is of obvious practical interest. During transient operation, these parameters are expected to change, whereby the system is rendered time-varying and classical signal processing techniques are not applicable. A novel methodology is proposed here, which combines wavelet multi-resolution analysis and selective wavelet coefficient removal with classical signal processing techniques in order to provide short-term estimates of the system parameters of interest. The use of highly overlapping time-windows further monitors the gradual changes in system parameter values. The potential of the proposed methodology is demonstrated with numerical experiments for the problem of stability evaluation of boiling water reactors during a transient.
Parameter estimation during a transient - application to BWR stability
The estimation of system parameters is of obvious practical interest. During transient operation, these parameters are expected to change, whereby the system is rendered time-varying and classical signal processing techniques are not applicable. A novel methodology is proposed here, which combines wavelet multi-resolution analysis and selective wavelet coefficient removal with classical signal processing techniques in order to provide short-term estimates of the system parameters of interest. The use of highly overlapping time-windows further monitors the gradual changes in system parameter values. The potential of the proposed methodology is demonstrated with numerical experiments for the problem of stability evaluation of boiling water reactors during a transient
Meuwissen Theo HE; Ødegård Jørgen; Lillehammer Marie
2009-01-01
Abstract The combination of a sire model and a random regression term describing genotype by environment interactions may lead to biased estimates of genetic variance components because of heterogeneous residual variance. In order to test different models, simulated data with genotype by environment interactions, and dairy cattle data assumed to contain such interactions, were analyzed. Two animal models were compared to four sire models. Models differed in their ability to handle heterogeneo...
Estimating the 3D Pore Size Distribution of Biopolymer Networks from Directionally Biased Data
Lang, Nadine R.; Münster, Stefan; Metzner, Claus; Krauss, Patrick; Schürmann, Sebastian; Lange, Janina; Aifantis, Katerina E.; Friedrich, Oliver; Fabry, Ben
2013-01-01
The pore size of biopolymer networks governs their mechanical properties and strongly impacts the behavior of embedded cells. Confocal reflection microscopy and second harmonic generation microscopy are widely used to image biopolymer networks; however, both techniques fail to resolve vertically oriented fibers. Here, we describe how such directionally biased data can be used to estimate the network pore size. We first determine the distribution of distances from random points in the fluid ph...
How cognitive biases can distort environmental statistics: introducing the rough estimation task.
Wilcockson, Thomas D W; Pothos, Emmanuel M
2016-04-01
The purpose of this study was to develop a novel behavioural method to explore cognitive biases. The task, called the Rough Estimation Task, simply involves presenting participants with a list of words that can be in one of three categories: appetitive words (e.g. alcohol, food, etc.), neutral related words (e.g. musical instruments) and neutral unrelated words. Participants read the words and are then asked to state estimates for the percentage of words in each category. Individual differences in the propensity to overestimate the proportion of appetitive stimuli (alcohol-related or food-related words) in a word list were associated with behavioural measures (i.e. alcohol consumption, hazardous drinking, BMI, external eating and restrained eating, respectively), thereby providing evidence for the validity of the task. The task was also found to be associated with an eye-tracking attentional bias measure. The Rough Estimation Task is motivated in relation to intuitions with regard to both the behaviour of interest and the theory of cognitive biases in substance use. PMID:26866972
Huang, Chencheng; Zeng, Li
2015-01-01
Intensity inhomogeneity causes many difficulties in image segmentation and the understanding of magnetic resonance (MR) images. Bias correction is an important method for addressing the intensity inhomogeneity of MR images before quantitative analysis. In this paper, a modified model is developed for segmenting images with intensity inhomogeneity and estimating the bias field simultaneously. In the modified model, a clustering criterion energy function is defined by considering the difference between the measured image and estimated image in local region. By using this difference in local region, the modified method can obtain accurate segmentation results and an accurate estimation of the bias field. The energy function is incorporated into a level set formulation with a level set regularization term, and the energy minimization is conducted by a level set evolution process. The proposed model first appeared as a two-phase model and then extended to a multi-phase one. The experimental results demonstrate the advantages of our model in terms of accuracy and insensitivity to the location of the initial contours. In particular, our method has been applied to various synthetic and real images with desirable results. PMID:25837416
Federico Scarpa
2015-01-01
Full Text Available The identification of thermophysical properties of materials in dynamic experiments can be conveniently performed by the inverse solution of the associated heat conduction problem (IHCP. The inverse technique demands the knowledge of the initial temperature distribution within the material. As only a limited number of temperature sensors (or no sensor at all are arranged inside the test specimen, the knowledge of the initial temperature distribution is affected by some uncertainty. This uncertainty, together with other possible sources of bias in the experimental procedure, will propagate in the estimation process and the accuracy of the reconstructed thermophysical property values could deteriorate. In this work the effect on the estimated thermophysical properties due to errors in the initial temperature distribution is investigated along with a practical method to quantify this effect. Furthermore, a technique for compensating this kind of bias is proposed. The method consists in including the initial temperature distribution among the unknown functions to be estimated. In this way the effect of the initial bias is removed and the accuracy of the identified thermophysical property values is highly improved.
Bias Estimations for Ill-posed Problem of Celestial Positioning Using the Sun and Precision Analysis
ZHAN Yinhu
2016-08-01
Full Text Available Lunar/Mars rovers own sun sensors for navigation, however, long-time tracking for the sun impacts on the real-time activity of navigation. Absolute positioning method by observing the sun with a super short tracking period such as 1 or 2 minutes is researched in this paper. Linear least squares model of altitude positioning method is deduced, and the ill-posed problem of celestial positioning using the sun is brought out for the first time. Singular value decomposition method is used to diagnose the ill-posed problem, and different bias estimations are employed and compared by simulative calculations. Results of the calculations indicate the superiority of bias estimations which can effectively improve initial values. However, bias estimations are greatly impacted by initial values, because the initial values converge at a line which passes by the real value and is vertical relative to the orientation of the sun. The research of this paper is of some value to application.
Bias and robustness of uncertainty components estimates in transient climate projections
Hingray, Benoit; Blanchet, Juliette; Jean-Philippe, Vidal
2016-04-01
A critical issue in climate change studies is the estimation of uncertainties in projections along with the contribution of the different uncertainty sources, including scenario uncertainty, the different components of model uncertainty and internal variability. Quantifying the different uncertainty sources faces actually different problems. For instance and for the sake of simplicity, an estimate of model uncertainty is classically obtained from the empirical variance of the climate responses obtained for the different modeling chains. These estimates are however biased. Another difficulty arises from the limited number of members that are classically available for most modeling chains. In this case, the climate response of one given chain and the effect of its internal variability may be actually difficult if not impossible to separate. The estimate of scenario uncertainty, model uncertainty and internal variability components are thus likely to be not really robust. We explore the importance of the bias and the robustness of the estimates for two classical Analysis of Variance (ANOVA) approaches: a Single Time approach (STANOVA), based on the only data available for the considered projection lead time and a time series based approach (QEANOVA), which assumes quasi-ergodicity of climate outputs over the whole available climate simulation period (Hingray and Saïd, 2014). We explore both issues for a simple but classical configuration where uncertainties in projections are composed of two single sources: model uncertainty and internal climate variability. The bias in model uncertainty estimates is explored from theoretical expressions of unbiased estimators developed for both ANOVA approaches. The robustness of uncertainty estimates is explored for multiple synthetic ensembles of time series projections generated with MonteCarlo simulations. For both ANOVA approaches, when the empirical variance of climate responses is used to estimate model uncertainty, the bias
Another Look at the EWMA Control Chart with Estimated Parameters
N.A. Saleh; M.A. Mahmoud; L.A. Jones-Farmer; I. Zwetsloot; W.H. Woodall
2015-01-01
The authors assess the in-control performance of the exponentially weighted moving average (EWMA) control chart in terms of the SDARL and percentiles of the ARL distribution when the process parameters are estimated.