scholarly journals A fast EM algorithm for fitting joint models of a binary response and multiple longitudinal covariates subject to detection limits

2015 ◽  
Vol 85 ◽  
pp. 37-53 ◽  
Author(s):  
Paul W. Bernhardt ◽  
Daowen Zhang ◽  
Huixia Judy Wang
Keyword(s):  
Em Algorithm ◽  
Detection Limits ◽  
Binary Response ◽  
Joint Models ◽  
Longitudinal Covariates

scholarly journals EM algorithm for longitudinal data with non-ignorable missing values: An application to health data

2016 ◽  
Vol 27 (2) ◽  
pp. 133-142
Author(s):  
Radia Taisir ◽  
M Ataharul Islam
Keyword(s):  
Longitudinal Data ◽  
Em Algorithm ◽  
Expectation Maximization ◽  
Missing Values ◽  
Likelihood Method ◽  
Estimation Methods ◽  
Binary Response ◽  
Complex Method ◽  
Missing Responses ◽  
Regression Parameters

Longitudinal studies involves repeated observations over time on the same experimental units and missingness may occur in non-ignorable fashion. For such longitudinal missing data, a Markov model may be used to model the binary response along with a suitable non-response model for the missing portion of the data. It is of the primary interest to estimate the effects of covariates on the binary response. Similar model for such incomplete longitudinal data exists where estimation of the regression parameters are obtained using likelihood method by summing over all possible values of the missing responses. In this paper, we propose an expectation-maximization (EM) algorithm technique for the estimation of the regression parameters which is computationally simple and produces similar efficient estimates as compared to the existing complex method of estimation. A comparison of the existing and the proposed estimation methods has been made by analyzing the Health and Retirement Survey (HRS) data of United States.Bangladesh J. Sci. Res. 27(2): 133-142, December-2014

scholarly journals Modelling Inverse Gaussian Data with Censored Response Values: EM versus MCMC

2011 ◽  
Vol 2011 ◽  
pp. 1-8 ◽  
Author(s):  
R. S. Sparks ◽  
G. Sutton ◽  
P. Toscas ◽  
J. T. Ormerod
Keyword(s):  
Em Algorithm ◽  
Environmental Variables ◽  
Detection Limits ◽  
Inverse Gaussian ◽  
Building Models ◽  
The Em Algorithm ◽  
High Detection ◽  
Using Data ◽  
Censored Response ◽  
Gaussian Data

Low detection limits are common in measure environmental variables. Building models using data containing low or high detection limits without adjusting for the censoring produces biased models. This paper offers approaches to estimate an inverse Gaussian distribution when some of the data used are censored because of low or high detection limits. Adjustments for the censoring can be made if there is between 2% and 20% censoring using either the EM algorithm or MCMC. This paper compares these approaches.

Quantitative parallel EELS spectrum imaging developed as a new tool in AEM

1992 ◽  
Vol 50 (2) ◽  
pp. 1202-1203
Author(s):  
Gianluigi Botton ◽  
Gilles L'espérance
Keyword(s):  
Statistical Analysis ◽  
Spatial Resolution ◽  
Personal Computer ◽  
Systematic Study ◽  
Present Author ◽  
Chemical Elements ◽  
Detection Limits ◽  
Research Groups ◽  
Quantitative Performance ◽  
Spectrum Imaging

As interest for parallel EELS spectrum imaging grows in laboratories equipped with commercial spectrometers, different approaches were used in recent years by a few research groups in the development of the technique of spectrum imaging as reported in the literature. Either by controlling, with a personal computer both the microsope and the spectrometer or using more powerful workstations interfaced to conventional multichannel analysers with commercially available programs to control the microscope and the spectrometer, spectrum images can now be obtained. Work on the limits of the technique, in terms of the quantitative performance was reported, however, by the present author where a systematic study of artifacts detection limits, statistical errors as a function of desired spatial resolution and range of chemical elements to be studied in a map was carried out The aim of the present paper is to show an application of quantitative parallel EELS spectrum imaging where statistical analysis is performed at each pixel and interpretation is carried out using criteria established from the statistical analysis and variations in composition are analyzed with the help of information retreived from t/γ maps so that artifacts are avoided.

Analysis of completed commercial semiconductors using EPMA

1996 ◽  
Vol 54 ◽  
pp. 502-503
Author(s):  
R. Packwood ◽  
M.W. Phaneuf ◽  
V. Weatherall ◽  
I. Bassignana
Keyword(s):  
Electron Probe Microanalysis ◽  
Electron Probe ◽  
Semiconductor Industry ◽  
Detection Limits ◽  
High Technology ◽  
Depth Resolution ◽  
Analytical Instruments ◽  
Wide Range ◽  
Numerous Element ◽  
Choice Method

The development of specialized analytical instruments such as the SIMS, XPS, ISS etc., all with truly incredible abilities in certain areas, has given rise to the notion that electron probe microanalysis (EPMA) is an old fashioned and rather inadequate technique, and one that is of little or no use in such high technology fields as the semiconductor industry. Whilst it is true that the microprobe does not possess parts-per-billion sensitivity (ppb) or monolayer depth resolution it is also true that many times these extremes of performance are not essential and that a few tens of parts-per-million (ppm) and a few tens of nanometers depth resolution is all that is required. In fact, the microprobe may well be the second choice method for a wide range of analytical problems and even the method of choice for a few.The literature is replete with remarks that suggest the writer is confusing an SEM-EDXS combination with an instrument such as the Cameca SX-50. Even where this confusion does not exist, the literature discusses microprobe detection limits that are seldom stated to be as low as 100 ppm, whereas there are numerous element combinations for which 10-20 ppm is routinely attainable.

In-depth Analysis of the Impurities in GaN

1996 ◽  
Vol 1 ◽  
Author(s):  
A. P. Kovarsky ◽  
V. S. Strykanov
Keyword(s):  
Secondary Ion Mass Spectrometry ◽  
Dynamic Range ◽  
Detection Limits ◽  
Background Level ◽  
Epitaxial Films ◽  
Depth Analysis ◽  
Lower Detection ◽  
Minimum Detection Level ◽  
Ion Yields ◽  
Secondary Ion

GaN epitaxial films were analyzed by Secondary Ion Mass Spectrometry (SIMS). Standard implanted samples were used to determine the appropriate analytical conditions for analysis of impurities. The dose and energy of implantation for selected elements (Mg, Al, Si, Zn, Cd, H, C and O) were chosen so the maximum impurity concentration was not more than 1020 atoms/cm3. The optimum analysis conditions were ascertained from the standards for each element, and the detection limits were deduced from the background levels of the implantation profiles. We demonstate that lower detection limits of 1015 atoms/cm3 with a dynamic range 103 − 105 are possible. Zn and Cd have low ion yields, so the minimum detection level for these elements is the background level of the detector. The detection limits of the other elements are determined by the contamination of an initial GaN matrix.

Joint Signal Detection and Channel Estimation Using Differential Models via EM Algorithm for OFDM Mobile Communications

2011 ◽  
Vol E94-B (2) ◽  
pp. 533-545 ◽  
Author(s):  
Kazushi MURAOKA ◽  
Kazuhiko FUKAWA ◽  
Hiroshi SUZUKI ◽  
Satoshi SUYAMA
Keyword(s):  
Channel Estimation ◽  
Em Algorithm ◽  
Signal Detection ◽  
Mobile Communications
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