Search 250 million research papers

Missing data

John L.P. Thompson, Gilberto Levy · Amyotrophic Lateral Sclerosis and Other Motor Neuron Disorders · 2004

Cited by 7

Missing Data.

Michael De Santi (13730023), Syed Imran Ali (5692955), Jean-François Fesselet (760995) et al. · Figshare · 2025

<div> Ensuring that sufficient free residual chlorine (FRC) persist in drinking water throughout the post-distribution period (collection, transport, and household storage) is critical to keeping drinking water safe in emergencies. Probabilistic models like artificial neural network (ANN) ensemble forecasting systems (EFS) have the potential to reproduce the high variability in post-distribution chlorine decay to generate risk-based chlorination guidance, but training with symmetrical error cost functions like mean squared error leads to poor probabilistic performance. This research proposes multi-objective (MO) training to improve the probabilistic performance of ANN-EFS forecasts of post-distribution FRC. Four MO optimizers were tested with combinations of seven objective functions and evaluated using water quality datasets from five emergency settings. MO training substantially improved probabilistic performance over conventional symmetrical error training. The solution that provided the most consistent improvement used preference-based optimization via backpropagation with the following objectives: similarity of mean, variance, and skew, correlation, recall, and precision. This approach achieved high performance at all sites and outperformed all baseline comparisons. These improved models will help humanitarian responders set informed chlorination targets that ensure water remains safe up to the point-of-consumption. This research highlights the importance of tailoring training approaches in ANN drinking water applications and hydroinformatics more broadly. </div>

Cited by 0Open access

Missing Data

Alexander Rothman, Traci Mann, Mary Panos · OSF Preprints (OSF Preprints) · 2017

Cited by 0

Missing Data

Molenberghs, Geert, Lesaffre, Emmanuel · Lirias (KU Leuven) · 2013

edition: published

Cited by 0

Missing Data

Alexander Rothman, Traci Mann, Mary Panos · OSF Preprints (OSF Preprints) · 2017

Cited by 0

Missing Data

Terence Yee, Brendan Lam · OSF Preprints (OSF Preprints) · 2019

Those who have not completed the PHQ-15 or filled out the demographics section will be excluded from analysis. Moreover, those who have not completed the Vancouver Index of Acculturation (VIA) or CES-D will also be excluded from analysis.

Cited by 0

Missing Data

Mary Panos, Alexander Rothman, Traci Mann · OSF Preprints (OSF Preprints) · 2017

Cited by 0

Missing data

Jan Toman, Michal Vinkler · OSF Preprints (OSF Preprints)

We will use data from all subjects who will provide the information about their COVID-19 infection or health staus and complete the wildlife-, animal-, plant-, infectious disease-, autoimmune- or vaccine-related part of the questionnaire, regardless of whether they completed the whole electronic questionnaire. When the subject checks at least one option per multiple-choice question, the rest will be expected not to be checked on purpose.

Cited by 0

Missing Data

Julian J. Faraway · 2025

Cited by 0

Missing data

Stephanie A. Zimmer, Rebecca J. Powell, Isabella C. Velásquez · 2024

Cited by 0

Missing Data

David J. Weiss · 2005

Abstract Every now and then the best laid plans of rats and sophomores go awry, and a carefully designed study ends up with missing scores. An animal may die, or a human may not show up, and the researcher must deal with a reduced set of data. In terms of the statistical analysis, this reduction can be either that the various cells in the design no longer have the planned equal numbers (the unequal n case) or that one or more cells in the design have no scores at all (the missing cells case). Also, we shall consider the rare situation in which inequality of cell size is intentionally achieved. The situation we examine first is one in which each cell has some scores in it, but the number of scores differs from cell to cell. While such unequal n is of little consequence for one-way designs, special consideration is required for factorial designs. The researcher should not blindly submit the data to a computer program, even one that claims to cater to the situation.

Cited by 0

Missing Data

Julian J. Faraway · 2021

Cited by 0

Missing data

Anindya Hapsari · Open Science Framework · 2024

No response

Cited by 0Open access

Missing Data

Springer texts in statistics · 2006

Cited by 0

Missing Data

Nicholas T. Longford · 2007

Cited by 6

Missing Data

Roderick J. A. Little, Nathaniel Schenker · 1995

Cited by 158

Missing Data

JonDavid Sparks · The American Statistician · 2008

Cited by 0

Missing Data

Kit Eason · Apress eBooks · 2021

This is a chapter about nothing! Specifically, it’s about how we handle the absence of data in our programs. It’s a more important topic than you might think at first: bugs caused by incorrect handling of missing data, typically manifested as “null reference errors,” are distressingly common in Object-Oriented programs. And this still happens, despite code to avoid such errors forming a significant proportion of the line count of many C# code bases.

Cited by 0

Missing Data

Wiley series in probability and statistics · 2006

Cited by 0

Missing Data

Kousick Biswas · 2017

Cited by 0