Bias Estimation Method Research Articles

Data Analyzing by Attention to Weighted Multicollinearity in Logistic Regression Applicable in Industrial Data

The Middle Easts largest industrial complex produces ˚�at steel sheets with specific properties such as low thickness, high strength and suitable formabil ity in order to reduce the vehicle weight and fuel consumption and prevention of environmental pollution. The aim of this study is to investigate the effect of some important explanatory variables on suitable formability of manufacturing steel sheets accor ding to primary data set. Existence or lack of existence of crack on steel sheet is considered as a binary response variable. It is determined by bending test with the angle of zero degree. Existence of multicollinearity between mentioned explanatory variables has an effect on the probability of crack existence. Because of special condition of the response variable, which is binary, the suitable regression is logistic, and correction techniques based on least squares do not work. Developments in weighted multicollinearity diagnostics are used to assess maximum likelihood logistic regression parameter estimates. Then principal component, a biased estimation method, is used in a way that it has additional scaling parameter which can

순차적 정보융합을 이용한 비동기 다중 레이더 환경에서의 바이어스 추정기법

This paper presents a sensor bias estimation method with serial fusion for asynchronous multisensory systems. Serial fusion processes the sensor measurements in a first-come-first-serve basis and it plays an essential role in asynchronous fusion in practice. The proposed algorithm generates the bias measurements using fusion estimates and sensor measurements for bias estimation, and compensates the sensor biases in fusion tracks. A simulation study indicates that the proposed algorithm has the superior performance in bias estimation and accurate tracking.

Complementary Drivers of New Product Development Performance: Cross‐Functional Coordination, Information System Capability, and Intelligence Quality

Coordination efforts that access and align relevant cross‐functional expertise are regarded as an essential element of innovation success. In recent years, these efforts have been further augmented through complementary investments in information systems, which provide the technological platforms for information sharing and coordination across functional and organizational boundaries. Somewhat overlooked has been the critical mediating role of the intelligence gained through these efforts and capabilities. This study draws on the theory of complementarity to elaborate on the nature of this mediating concept. Theoretical predictions of the model are tested using instrument variable regression analysis of data collected from a sample of publicly traded US manufacturing firms. The findings suggest that the effects of both internal and external coordination on market intelligence and supply‐chain intelligence are moderated by the firm's information system capability. The effect of both types of intelligence quality on new product development performance was contingent with the effects being enhanced (attenuated) when the market conditions were dynamic (stable). The results are robust to common‐method bias, endogeneity concerns, and alternative estimation methods.

비동기 다중레이더 환경에서 의사 측정치를 이용한 바이어스 추정기법

In this paper, a sensor bias estimation method with pseudo measurement for asynchronous multisensor systems is proposed. The proposed bias estimation method separates the local filter which estimates the target state with biased measurements into two parts, one is bias part, the other is target state part. By using these two parts, the algorithm generates the pseudo bias measurement for estimating bias, and then eliminates bias of local track through bias compensation. Finally, the proposed algorithm is evaluated by comparing with the existing EXX method.

P1-176 Bias estimation methods for the burden of occupational cancer

IntroductionBias occurs in particular when estimates are based on sparse or inadequate data. We have estimated the burden of cancer in Great Britain attributable to occupation using an attributable fraction...

Accuracy analysis of the GPS instrumental bias estimated from observations in middle and low latitudes

Abstract. With one bias estimation method, the latitude-related error distribution of instrumental biases estimated from the GPS observations in Chinese middle and low latitude region in 2004 is analyzed statistically. It is found that the error of GPS instrumental biases estimated under the assumption of a quiet ionosphere has an increasing tendency with the latitude decreasing. Besides the asymmetrical distribution of the plasmaspheric electron content, the obvious spatial gradient of the ionospheric total electron content (TEC) along the meridional line that related to the Equatorial Ionospheric Anomaly (EIA) is also considered to be responsible for this error increasing. The RMS of satellite instrumental biases estimated from mid-latitude GPS observations in 2004 is around 1 TECU (1 TECU = 1016/m2), and the RMS of the receiver's is around 2 TECU. Nevertheless, the RMS of satellite instrumental biases estimated from GPS observations near the EIA region is around 2 TECU, and the RMS of the receiver's is around 3–4 TECU. The results demonstrate that the accuracy of the instrumental bias estimated using ionospheric condition is related to the receiver's latitude with which ionosphere behaves a little differently. For the study of ionospheric morphology using the TEC derived from GPS data, in particular for the study of the weak ionospheric disturbance during some special geo-related natural hazards, such as the earthquake and severe meteorological disasters, the difference in the TEC accuracy over different latitude regions should be paid much attention.

A Weight-of-Evidence Analysis of the Human Carcinogenicity of Styrene

To quantitatively assess the impact of selection bias caused by nonparticipation in a multinational case–control study of mobile phone use and brain tumor.Non-response questionnaires (NRQ) were completed by a sub-set of nonparticipants. Selection bias factors were calculated based on the prevalence of mobile phone use reported by nonparticipants with NRQ data, and on scenarios of hypothetical exposure prevalence for other nonparticipants.Regular mobile phone use was reported less frequently by controls and cases who completed the NRQ (controls, 56%; cases, 50%) than by those who completed the full interview (controls, 69%; cases, 66%). This relationship was consistent across study centers, sex, and age groups. Lower education and more recent start of mobile phone use were associated with refusal to participate. Bias factors varied between 0.87 and 0.92 in the most plausible scenarios.Refusal to participate in brain tumor case–control studies seems to be related to less prevalent use of mobile phones, and this could result in a downward bias of around 10% in odds ratios for regular mobile phone use. The use of simple selection bias estimation methods in case–control studies can give important insights into the extent of any bias, even when nonparticipant information is incomplete.

Quantifying the Impact of Selection Bias Caused by Nonparticipation in a Case–Control Study of Mobile Phone Use

To quantitatively assess the impact of selection bias caused by nonparticipation in a multinational case-control study of mobile phone use and brain tumor. Non-response questionnaires (NRQ) were completed by a sub-set of nonparticipants. Selection bias factors were calculated based on the prevalence of mobile phone use reported by nonparticipants with NRQ data, and on scenarios of hypothetical exposure prevalence for other nonparticipants. Regular mobile phone use was reported less frequently by controls and cases who completed the NRQ (controls, 56%; cases, 50%) than by those who completed the full interview (controls, 69%; cases, 66%). This relationship was consistent across study centers, sex, and age groups. Lower education and more recent start of mobile phone use were associated with refusal to participate. Bias factors varied between 0.87 and 0.92 in the most plausible scenarios. Refusal to participate in brain tumor case-control studies seems to be related to less prevalent use of mobile phones, and this could result in a downward bias of around 10% in odds ratios for regular mobile phone use. The use of simple selection bias estimation methods in case-control studies can give important insights into the extent of any bias, even when nonparticipant information is incomplete.

Research of Point Flux Estimation Method

In the calculation of point flux by Monte Carlo simulation, there is a special disadvantage in the mostly used method of next event estimation (NEE) for which theoretical variance is infinite. And, this problem has not yet been solved satisfactorily. The purpose of this paper is to provide some new ideas to solve the problem of infinite variance without introducing any bias for the mean. To eliminate the unbounded factors, the relations among the different state variables for two neighboring collisions are analyzed; then, on the basis of the integral expression of the once-more scattered flux contribution to the point detector, by changing the state variables to be sampled, six basic methods are derived - two of them are NEE and collision probability estimation, and four are new methods. Furthermore, based on one of the new methods, by variable substitution, a new method called exponent biased sample estimation (EBS) is obtained that can eliminate the [arrow over]rd - [arrow over]rm-2 singularity factor and has no exponent factor, which exists in other methods. The benchmark results show that EBS is much better than NEE with the variance of one order of magnitude smaller and a figure-of-merit factor of several hundreds higher sometimes, and its calculation efficiency is higher than that of the once more collision flux estimation method. Compared with the direction biased sample estimation method, EBS has no advantage in variance, but the sample procedures are much simpler and use less CPU time.

Bias estimation of Doppler‐radar radial‐wind observations

AbstractDoppler radars provide measurements of the radar radial wind component at high spatial and temporal resolution. The variational data‐assimilation framework enables direct use of these measurements in numerical weather prediction models. Bias estimation of Doppler‐radar radial winds requires special attention because of the azimuthal scanning strategy. Calculation of the bias statistic over all azimuthal directions results in a near‐zero value even in the presence of systematic differences between the measured and modelled wind speed and direction. This paper describes a method for bias estimation of Doppler‐radar radial‐wind observations. Intercomparison between the estimated biases of Doppler‐radar and radiosonde wind observations reveals that the wind speed and direction biases are of comparable magnitude. Copyright © 2007 Royal Meteorological Society

Multisensor target tracking performance with bias compensation

In this paper, multisensor-multitarget tracking performance with bias estimation and compensation is investigated when only moving targets of opportunity are available. First, we discuss the tracking performance improvement with bias estimation and compensation for synchronous biased sensors, and then a novel bias estimation method is proposed for asynchronous sensors with time-varying biases. The performance analysis and simulations show that asynchronous sensors have a slightly degraded performance compared with the "equivalent" synchronous ones. The bias estimates as well as the corresponding Cramer-Rao lower bound (CRLB) on the covariance of the bias estimates, i.e., the quantification of the available information on the sensor biases in any scenario are also given. Tracking performance evaluations with different sources of biases-offset biases, scale biases, and sensor location uncertainties, are also presented and we show that tracking performance is significantly improved with bias estimation and compensation compared with the target tracking using the original biased measurements. The performance is also close to the lower bound obtained in the absence of biases

Seek control and settling control taming actuator resonance of hard disk drives

Novel fast seek and settling controls for hard disk drives (HDDs) have been developed. The controls are suitable for digital control and can be implemented into the servo program of a HDDs microcontroller easily. The controls use a feedback control and a feedforward control that uses feedforward trajectory with little power at actuator resonance frequencies. A finite-impulse-response notch filter is used to design the feedforward trajectories. The seek control uses the pair of feedforward trajectories of position and current. The settling control that connects a long-distance seek control with a following control uses two pairs of feedforward trajectories for initial position and initial velocity and a new bias-estimation method. The authors prove both controls are fast and free from residual vibrations and bias offset.

A method for estimation of bias and variability of continuous gas monitor data: application to carbon monoxide monitor accuracy.

A method is presented for the evaluation of the bias, variability, and accuracy of gas monitors. This method is based on using the parameters for the fitted response curves of the monitors. Thereby, variability between calibrations, between dates within each calibration period, and between different units can be evaluated at several different standard concentrations. By combining variability information with bias information, accuracy can be assessed. An example using carbon monoxide monitor data is provided. Although the most general statistical software required for these tasks is not available on a spreadsheet, when the same number of dates in a calibration period are evaluated for each monitor unit, the calculations can be done on a spreadsheet. An example of such calculations, together with the formulas needed for their implementation, is provided. In addition, the methods can be extended by use of appropriate statistical models and software to evaluate monitor trends within calibration periods, as well as consider the effects of other variables, such as humidity and temperature, on monitor variability and bias.

Unobserved Ability and the Return to Schooling

IN THE EMPIRICAL LITERATURE, it is customary to estimate the return to schooling by ordinary least squares (OLS) or Instrumental Variable (IV) techniques.2 The choice of OLS is justified only if realized schooling and unobserved labor market ability are uncorrelated. If not, OLS estimates suffer the Ability Bias and other estimation methods, such as IV, may be used. The volume of work devoted to the return to schooling is a good indication of the importance of this topic as perceived by economists.3 Frequently, instrumental variables (IV) techniques are applied in a context where the instrument is only weakly correlated with schooling attainments (Staiger and Stock (1997)). As a consequence, the validity of very high returns to schooling, reported in a simple regression framework, should be seriously questioned; see Manski and Pepper (2000).4 When IV techniques are chosen, the log wage regression is usually assumed to be linear in schooling. However, there is no obvious reason to presume that the local returns to schooling are independent of grade level. Indeed, heterogeneity in any component of schooling choices (subjective discount rates, ability or specific taste for schooling) will lead to improper inference if the local returns are erroneously assumed to be constant. As individuals with lower taste for schooling tend to stop school earlier, OLS (or IV) estimates of the return to schooling, which impose equality between local and average returns at all levels of schooling, will be strongly affected by the relative frequencies of individuals with high and low taste for schooling. More precisely, if there are large differences in local returns between various grade levels, the OLS estimate (measuring an average log wage increment per year of schooling) will tend to be biased toward the co-editor and three anonymous referees for comments and suggestions on this version or earlier versions. Belzil thanks the Social Sciences and Humanities Research Council of Canada for generous funding. The usual disclaimer applies. 2 To use the same terminology as in the reduced-form literature, the return (local) to schooling refers to the percentage wage increase per additional year of schooling. In the paper, the terms local and marginal returns may be used interchangeably. The average return refers to the slope of the straight line between the intercept and the expected log wage at a given number of years of schooling. 3 A World Wide Web survey of the most recent literature indicates that, since 1970, more than 200 published articles or working papers (set in a reduced-form) have been devoted to the estimation of the return to schooling or surrounding issues. Very often, studies based on IV methods conclude that the returns to schooling can be between 20% and 40% above the OLS estimates. Reported estimates around 15% per year (for the US) are not uncommon although standard errors are typically very high. See Card (1999) for a survey. 4 Manski and Pepper (2000) obtain upper bound estimates of the return to schooling from a sample taken from the NLSY. Their results cast doubts on the high returns reported in the literature.

Radar bias correction based on GPS measurements for ATC applications

A new method for radar measurement bias correction is described. It is based on the use of a mode S datalink and GPS receivers, and may provide accurate bias estimations both for primary and secondary collocated radars. The proposed method distributes effectively the calibration process over the radar network, potentially reducing communication needs and computation complexity of currently used centralised bias estimation methods. The accuracy of the method is analytically predicted, based on measurement error statistics.

Species richness estimation of bird communities: how to control for sampling effort?

Since estimates of total species richness increase with sampling effort, methods to control for this sampling effect need to be tested and used. We present seven non‐parametric and 12 accumulation curve methods that have been used recently in the ecological literature. To test their performance, we used data from bird communities in the Queen Charlotte Islands, Canada. The performance of each method was evaluated by calculating the bias and precision of its estimates against the known total species richness. For our data set, the two Chao estimators were the overall least biased and most precise estimation methods, followed by the two jackknife estimators, thus supporting results of previous studies. Nonparametric estimators tended to perform better than accumulation curve models. Most estimation methods had the problem that they tended to underestimate species richness for early samples, but slightly overestimated it for late samples. We briefly discuss the practical use of these methods which may greatly increase our ability to answer ecological questions and to guide conservation decisions, especially for species‐rich tropical bird communities.

Numerical validation of a modified neutron source multiplication method using a calculated eigenvalue

To overcome the difficulties of the conventional neutron source multiplication method, the authors have developed the Indirect Bias Estimation Method. This method obtains the bias in calculated K eff using the difference between measured and calculated neutron count rates. This bias in calculated K eff is used for an adjustment of the calculated K eff, deriving a truer K eff. Using neutron diffusion calculations, numerical experiments simulating the neutron source multiplication method were performed to validate this method. It could be shown how closely a true K eff can be reproduced using this method. It was found that an accurate estimation of subcriticality requires neutron count rate measurements at more than three locations.

計算値を用いた未臨界度の推定 ＭＣＮＰによる２分割結合炉心実験の解析

Experiments on coupled cores performed at TCA were analysed using continuous energy Monte Carlo calculation code MCNP 4 A. Errors of neutron multiplication factors are evaluated using Indirect Bias Estimation Method proposed by authors. Calculation for simulation of pulsed neutron method was performed for 17×17+5G+17×17 core system and its of exponential experiment method was also performed for 16×9+3G+16×9 and 16×9+5G+16×9 core systems. Errors of neutron multiplication factors are estimated to be -1.5-0.6% evaluated by Indirect Bias Estimation Method. Its errors evaluated current pulsed neutron method and exponential experiment method are estimated to be 7%, but it is below 1% for estimation of subcriticality with the computed values applied Indirect Bias Estimation Method. Feasibility of subcriticality management is higher by application of the method to full scalefuel strage facility.

Accurate Estimation of Subcriticality Using Indirect Bias Estimation Method, (II)

Subcriticalities were estimated by applying the Indirect Bias Estimation Method to subcritical experiments on a light-water moderated/reflected low-enriched UO2 lattice cores. Two measurable values, prompt neutron time-decay constant and spatial-decay constant were calculated using MCNP 4A and JENDL-3.2. With these calculation errors, the biases in calculated reactivity were derived from the Indirect Bias Estimation Method. The differences between the calculated and measured spatial-decay constants were more or less at the same extent of experimental errors. These results show that the accuracy of subcriticality estimation of MCNP 4A and JENDL-3.2 ranges within the uncertainty which can be achieved by the exponential experiment. The differences between calculated and measured prompt neutron decay constants derive significant biases in calculated reactivity. The subcriticalities were estimated by using the effective multiplication factors adjusted based on these biases in calculated reactivity.

Accurate Estimation of Subcriticality Using Indirect Bias Estimation Method, (I)

To estimate subcriticality of a system, a new method, Indirect Bias Estimation Method, is proposed. Instead of comparing a calculated k eff with a “measured” one which cannot be measured directly, a bias in calculated k eff is evaluated by direct comparison between measured and calculated quantities which are measureable in subcritical experiments. With these errors of measurable quantities, accuracy of a subcriticality calculation is indirectly derived. Three estimation methods based on the source multiplication method, the pulse neutron method, and the exponential experiment method are presented. For three experimental methods, neutron count rate distribution, prompt neutron decay constant, and spatial decay constant are compared between calculation and measurement, respectively, deriving biases in calculated k eff's. With biases obtained by this procedures, namely Indirect Bias Estimation Method, subcriticality of a system can be estimated more precisely than by “measured” k eff's.