125,966 research outputs found
Onelia-G/Heat-map-microfluidic-chambers: Heat-map-microfluidic-chambers
No full textA Matlab code to drawing heat maps of microfluidic culture chambers in horizontal and vertical directions
Telegram from Jim Chambers to Amon G. Carter, Jr.
No full textTelegram from Jim Chambers, Vice President and General Manager of the Dallas Times Herald, to Amon G. Carter, Jr. upon the death of Amon Giles Carter. The telegram expresses condolences about his death.https://mavmatrix.uta.edu/specialcollections_meachamcarterpapers/1514/thumbnail.jp
Interview with William G. Shepherd
No full textClarke A. Chambers interviews William G. Shepard, former vice-president for Academic Administration.Shepherd, William G.; Chambers, Clarke A.. (1984). Interview with William G. Shepherd. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/50616
Small Area Estimation with Skewed Data
No full textIn business surveys, data typically are skewed and the standard approach for small area estimation based on linear mixed models lead to inefficient estimates. In this paper, we discuss small area estimation techniques for skewed data that are linear following a suitable transformation. In this context, implementation of the empirical best linear unbiased prediction (EBLUP) approach under transformation to a linear mixed model is complicated. However, this is not the case with the model-based direct (MBD) approach (Chambers and Chandra, 2006), which is based on weighted linear estimators. We extend the MBD approach to skewed data using sample weights derived via model calibration based on a log transform model with random area effects. Our results show this estimator is both efficient and robust with respect to the distribution of these random effects. An application to real data demonstrates the satisfactory performance of the method
Bias Adjusted Estimation for Small Areas with Outlying Values
No full textSmall area estimation techniques typically rely on regression models that use both covariates and random effects to explain between domain variation. Chambers and Tzavidis (2006) describe a novel approach to small area estimation that is based on modelling quantile-like parameters of the conditional distribution of the target variable given the covariates. This is an outlier robust approach that avoids conventional Gaussian assumptions and the problems associated with specification of random effects, allowing inter-domain differences to be characterized by the variation of area-specific M-quantile coefficients. These authors observed, however, that M-quantile estimates of small area means are biased with the magnitude of the bias being related to the presence of outliers in the data. In this paper we propose a bias adjustment to the M-quantile small area estimator of the mean that is based on representing this estimator as a functional of the small area distribution function. The method is then generalized for estimating other quantiles of the distribution function in a small area. The effect of this bias adjustment on small area estimation with random effects models in the presence of model misspecification is also examined
Chambers, M G, 213453
No full textThis record was harvested from a previous catalogue system and will be withdrawn in 2025. Information in this record may be superseded or incomplete. Visit this record in UMA's new catalogue at: https://archives.library.unimelb.edu.au/nodes/view/376574Surname: CHAMBERS
Given Name(s) or Initials: M G
Military Service Number or Last Known Location: 213453
Missing, Wounded and Prisoner of War Enquiry Card Index Number: SEA-2493189562
Item: [2016.0049.08879] "Chambers, M G, 213453
Tom G. Chambers
No full textPhotograph used for a newspaper owned by the Oklahoma Publishing Company. Caption: "Tom G. Chambers, Attorney-City, 703 Cotton Exch. Bldg.
Myron G. Chambers
No full textPhotograph used for a story in the Daily Oklahoman newspaper. Caption: "Myron G. Chambers, Pres. Knoxville Tenn. News-Sentinel. Deceased: 12-31-60
Imputation vs. Estimation of Finite Population Distributions
No full textEstimates of the distribution of hourly wage rates for employees are an important output for a national statistics agency. However, many employees are not paid by the hour and so their hourly wage rate data are effectively missing in a survey that attempts to collect this information. A standard approach in this situation is to impute these missing values using derived measures of this wage rate based on salary and hours worked data also collected in the survey. This paper contrasts this imputation approach with direct estimation of the wage rate distribution using the derived wage rate variable as an auxiliary. In particular, we focus on data obtained in the 2002 UK New Earnings Survey and use simulation based on actual and derived hourly wage rate data collected in this survey to compare two imputation approaches, one based on substituting the derived wage rate values for the missing actual values, the other using nearest neighbour imputation based on the derived wage rate, with two estimation approaches that use this variable as an auxiliary. The first of these is a semi-parametric extension of the Chambers and Dunstan (1986) estimator of the finite population distribution function, the other is a calibrated spline-based estimator of this function recently suggested by Harms and Duchesne (2004). Our conclusion is that an approach based on the semi-parametric estimator is best for these data. However, confidence interval estimation remains an open problem
Robert Chambers and Charles G. Rogers
No full textRobert Chambers is sitting on a windowsill facing Charles G. Rogers who is standing.Inscriptions on image and/or album page: "Robert Chambers (left)/ Charles G. Rogers (right) #431 '23"Digitized by: MBLWHOI Libraryimage/jpg black and white image reformatted digitalPhotograph
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