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Search Publications by: Hariharan K. Iyer (Fed)

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Displaying 26 - 43 of 43

Likelihood Ratio as Weight of Forensic Evidence: A Closer Look

October 12, 2017
Author(s)
Hariharan Iyer, Steven Lund
The forensic science community has increasingly sought quantitative methods for conveying the weight of evidence. Experts from many forensic laboratories summarize their fndings in terms of a likelihood ratio. Several proponents of this approach have

SVClassify: a method to use multiple datasets to classify candidate structural variants into true positives and false positives

January 16, 2016
Author(s)
Justin M. Zook, Hemang M. Parikh, Desu Chen, Hariharan K. Iyer, Marc L. Salit, Wolfgang Losert
The human genome contains variants ranging in size from small single nucleotide polymorphisms (SNPs) to large structural variants (SVs). While high-quality benchmark small variant calls have recently been developed by the Genome in a Bottle Consortium, no

Pivotal Methods in the Propagation of Distributions

April 24, 2012
Author(s)
Chih-Ming Wang, Jan Hannig, Hariharan K. Iyer
We propose a method for assigning a probability distribution to an input quantity. The distribution is used in the Monte Carlo method for uncertainty evaluation. The proposed method provides an alternative to other methods, such as the principle of maximum

Fiducial Prediction Intervals

February 18, 2012
Author(s)
Chih-Ming Wang, Jan Hannig, Hariharan K. Iyer
This paper presents an approach for constructing prediction intervals for any given distribution. The approach is based on the principle of fiducial inference. We use several examples, including the normal, binomial, gamma, and Weibull distributions, to

On Non-Linear Estimation of a Measurand

November 7, 2011
Author(s)
Chih-Ming Wang, Hariharan K. Iyer
We consider an estimation problem described in the Guide to the Expression of Uncertainty in Measurement (GUM). The problem is concerned with estimating a measurand that is a non-linear function of input quantities. The GUM describes two methods for

On Multiple-Method Studies

October 4, 2010
Author(s)
Chih-Ming Wang, Hariharan K. Iyer
In this paper we review statistical models that describe measurements from a multiple-method study such as in the development of a reference material. We also review requirements for the so-called GUM compliance as this appears to be an important criterion

On interchangeability of two laboratories

June 18, 2010
Author(s)
Chih-Ming Wang, Hariharan K. Iyer
This paper proposes a measure for assessing the degree of equivalence between the two laboratories in a key comparison. The measure is called asymmetric degree of interchangeability. It is asymmetric since, based on this measure, a laboratory may be

Fiducial Intervals for the Magnitude of a Complex-Valued Quantity

December 19, 2008
Author(s)
Chih-Ming Wang, Hariharan K. Iyer
This paper discusses a fiducial approach for constructing uncertainty intervals for the distance between k normal means and the origin. When k=2 this distance is equivalent to the magnitude of a complex-valued quantity. Uncertainty intervals for the

Fiducial approach for assessing agreement between two instruments

July 9, 2008
Author(s)
Chih-Ming Wang, Hariharan K. Iyer
This paper presents an approach for making inferences about the intercept and the slope of a linear regression model with both variables subject to measurement errors. The approach is based on the principle of fiducial inference. A procedure is presented

Uncertainty Analysis for Vector Measurands Using Fiducial Inference

January 1, 2006
Author(s)
Chih-Ming Wang, Hariharan K. Iyer
This paper presents a method for constructing uncertainty regions for a vector measurand in the presence of both type-A and type-B errors. The method is based on the principle of fiducial inference and generally requires a Monte Carlo approach for

Propagation of Uncertainties in Measurements Using Structural Inference

March 21, 2005
Author(s)
Hariharan K. Iyer, Chih-Ming Wang
The ISO Guide to the Expression of Uncertainty in Measurement (GUM) recommends the use of a first-order Taylor series expansion for propagating errors and uncertainties. The GUM also permits the use of other analytical or numerical methods when the

On Higher Order Corrections for Propagating Uncertainties

January 1, 2005
Author(s)
Chih-Ming Wang, Hariharan K. Iyer
The ISO Guide to the Expression of Uncertainty in Measurement (GUM) recommends the use of a first-order Taylor series expansion for propagating errors and uncertainties. The GUM also suggests the use of a second-order Taylor series approximation for

Propagation of uncertainties in measurements using generalized influence

January 1, 2005
Author(s)
Hariharan K. Iyer, Chih-Ming Wang
The ISO Guide to the Expression of Uncertainty in Measurement (GUM) recommends the use of a first- order Taylor series expansion for propagating errors and uncertainties. The GUM also permits the use of "other analytical or numerical methods" when the

Models and Confidence Intervals for True Values in Interlaboratory Trials

December 6, 2004
Author(s)
Hariharan K. Iyer, Chih-Ming Wang, T Mathew
We consider the one-way random effects model with unequal sample sizes and heterogeneous variances. Using the method of generalized confidence intervals, we develop a new confidence interval procedure for the mean. Additionally, we investigate two
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