Effects Of Incomplete Data Research Articles

Bivariate analysis for the improvement of genetic evaluations with incomplete records in Charolais cattle

Objective: Estimate (co)variance components and genetic parameters of live weight traits and examine the effect of selection culling when using bivariate analysis in registered Charolais beef cattle. Materials and methods: The effect of incomplete data over accuracies was compared, expected progeny differences (EPD) and standard errors of prediction (SEP) were obtained and evaluated by comparing univariate and bivariate models for birth (BW), weaning (WW) and yearling (YW) weights. Results: Bivariate models for WW and YW, improved accuracies of EPDs and reduced the SEPs. Joint analysis for BW and WW increased in a 38% the accuracies and reduced SEP estimators for YW (p<0.001). Accuracies of EPD for BW obtained from univariate models were improved when BW was included in bivariate models. Conclusions: The results support the use of bivariate genetic analysis in limited or incomplete live weight indicators databases that were registered after birth, such as weaning and yearling weight.

The experience of point-of-care testing for influenza in Scotland in 2017/18 and 2018/19 - no gain without pain.

BackgroundDuring the 2017/18 and 2018/19 influenza seasons, molecular amplification-based point-of-care tests (mPOCT) were introduced in Scotland to aid triaging respiratory patients for hospital admission, yet communication of results to national surveillance was unaccounted for.AimThis retrospective study aims to describe steps taken to capture mPOCT data and assess impact on influenza surveillance.MethodsQuestionnaires determined mPOCT usage in 2017/18 and 2018/19. Searches of the Electronic Communication of Surveillance in Scotland (ECOSS) database were performed and compared with information stored in laboratory information management systems. Effect of incomplete data on surveillance was determined by comparing routine against enhanced data and assessing changes in influenza activity levels determined by the moving epidemic method.ResultsThe number of areas employing mPOCT increased over the two seasons (6/14 in 2017/18 and 8/14 in 2018/19). Analysis of a small number of areas (n = 3) showed capture of positive mPOCT results in ECOSS improved between seasons and remained high (> 94%). However, capture of negative results was incomplete. Despite small discrepancies in weekly activity assessments, routine data were able to identify trend, start, peak and end of both influenza seasons.ConclusionThis study has shown an improvement in capture of data from influenza mPOCT and has highlighted issues that need to be addressed for results to be accurately captured in national surveillance. With the clear benefit to patient management we suggest careful consideration should be given to the connectivity aspects of the technology in order to ensure minimal impact on national surveillance.

Buys-Ballot Estimates of Quadratic Trend Component and Seasonal Indices and Effect of Incomplete Data in Time Series

This study examines the Buys-Ballot estimates of quadratic trend component and seasonal indices and effect of incomplete data in descriptive time series analysis. This paper is to provide estimates of trend parameters and seasonal indices using Buys-Ballot table with incomplete observations. The methods adopted in this study are 1) Mean imputations 2) Regression imputation 3) Buys-Ballot table for time series decomposition. The methods are based on the row, column and overall means of time series data arranged in Buys-Ballot table with m rows and s columns. The model structure is additive.

Issues in Evaluating Model FitWith Missing Data

Effects of incomplete data on fit indexes remain relatively unexplored. We evaluate a wide set of fit indexes (χ2, root mean squared error of appproximation, Normed Fit Index [NFI], Tucker-Lewis Index, comparative fit index, gamma-hat, and McDonald's Centrality Index) varying conditions of sample size (100-1,000 in increments of 50), factor loadings (.4 or. 8), factor covariances (.4 or. 8), type of missing data (missing completely at random or missing at random), and extent of missing data (0-95% on 3 of 9 indicators in increments of 5%) for correct and 2 misspecified (measurement or structural) models. Incremental and absolute fit indexes indicate better fit with higher proportions of missing data. Effects of missing data on the NFI were more varied, indicating poorer model fit as missing data increased for the correct model, and indicating better or poorer fit as an interaction of all the other factors for misspecified models. Recommendations are made for researchers and software developers.

Virial Mass Determinations of Bound and Unstable Groups of Galaxies.

view Abstract Citations (70) References (27) Co-Reads Similar Papers Volume Content Graphics Metrics Export Citation NASA/ADS Virial Mass Determinations of Bound and Unstable Groups of Galaxies. Aarseth, Sverre J. ; Saslaw, William C. Abstract In the first part, we investigate uncertainties inherent in the observational determinations of virial masses for groups of galaxies. This is done by computing virial theorem masses of small N-body configurations for three different ensembles representing groups of 8, 16, or 32 galaxies. Dynamical evolution, projection effects, and the effects of incomplete data are used to simulate actual observations. Instantaneous departures from equilibrium and projection effects introduce an uncertainty of about 50 percent in the computed masses. Selection of heavy (and presumably more luminous) members usually produces a significant underestimate of both the actual and projected virial masses of bound clusters. The validity of theoretical assumptions is tested against dynamically consistent configurations of one N-body system with 500 particles. The virial mass derived from cylindrical samples of centrally condensed models also underestimates the actual mass, even though the selected velocities exceed the rms value. The average net result of these effects is to increase the discrepancy between virial and luminosity masses for groups and clusters. In the second part, we examine the observational implications of the hypothesis that groups of galaxies are unbound and expanding. Detailed models of initial instability or significant mass loss are considered. Groups with positive energy expand rapidly, and the apparent mass discrepancy based on the equilibrium assumption is magnified by the linear growth factor as well as the initial excess. Evolution tracks of virial masses have also been computed for several types of mass-loss mechanisms and different rates. A range of mass discrepancies consistent with observations can easily be reproduced from relatively moderate assumptions. Finally, we discuss several methods for distinguishing between the hidden-mass hypothesis and the expansion hypothesis. Publication: The Astrophysical Journal Pub Date: February 1972 DOI: 10.1086/151324 Bibcode: 1972ApJ...172...17A full text sources ADS |