Results 221 to 230 of about 549,387 (258)

Subtypes of the missing not at random missing data mechanism.

Psychological Methods, 2021
issing values that are missing not at random (MNAR) can result from a variety of missingness processes. However, two fundamental subtypes of MNAR values can be obtained from the definition of the MNAR mechanism itself. The distinction between them deserves consideration because they have characteristic differences in how they distort relationships in ...
Brenna Gomer, Ke-Hai Yuan
openaire   +2 more sources

The randomized marker method for single-case randomization tests: Handling data missing at random and data missing not at random

Behavior Research Methods, 2022
Single-case experiments are frequently plagued by missing data problems. In a recent study, the randomized marker method was found to be valid and powerful for single-case randomization tests when the missing data were missing completely at random.
Tamal Kumar De, Patrick Onghena
openaire   +2 more sources

Rank‐based inference with responses missing not at random

Canadian Journal of Statistics, 2018
AbstractMissing data have become almost inevitable whenever data are collected. In this paper, interest is given to responses missing not at random in the context of regression modeling. Many of the existing methods for estimating the model parameters lack robustness or efficiency.
Huybrechts F. Bindele, Akim Adekpedjou
openaire   +1 more source

Social Recommendation with Missing Not at Random Data

2018 IEEE International Conference on Data Mining (ICDM), 2018
With the explosive growth of online social networks, many social recommendation methods have been proposed and demonstrated that social information has potential to improve the recommendation performance. However, existing social recommendation methods always assume that the data is missing at random (MAR) but this is rarely the case.
Jiawei Chen, Can Wang, Martin Ester, Qihao Shi, Yan Feng, Chun Chen   +5 more
openaire   +1 more source

Score Test for Missing at Random or Not under Logistic Missingness Models

Biometrics, 2022
Abstract Missing data are frequently encountered in various disciplines and can be divided into three categories: missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR). Valid statistical approaches to missing data depend crucially on correct identification of the underlying missingness mechanism ...
Hairu Wang, Zhiping Lu, Yukun Liu
openaire   +3 more sources

Imputing missing laboratory results may return erroneous values because they are not missing at random

Journal of Clinical Epidemiology, 2023
Regression models incorporating laboratory tests treat unordered tests as missing and are often imputed. Imputation typically assumes that data are "missing at random" (MAR, test's order status is unrelated to its result after accounting for other variables). This study examined the validity of this assumption.We included 14 biochemistry tests.
Carl van Walraven, Christopher McCudden, Peter C. Austin   +2 more
openaire   +2 more sources

Identifying Variables Responsible for Data not Missing at Random

Psychometrika, 2009
When data are not missing at random (NMAR), maximum likelihood (ML) procedure will not generate consistent parameter estimates unless the missing data mechanism is correctly modeled. Understanding NMAR mechanism in a data set would allow one to better use the ML methodology.
openaire   +1 more source

Missing not at random models for masked clinical trials with dropouts

Clinical Trials, 2015
Background Missing data are an unavoidable problem in clinical trials. Most existing missing data approaches assume the missing data are missing at random. However, the missing at random assumption is often questionable when the real causes of missing data are not well known and cannot be tested from observed data. Methods We propose a specific missing
Shan, Kang, Roderick J, Little, Niko, Kaciroti   +2 more
openaire   +2 more sources

Cautious Classification with Data Missing Not at Random Using Generative Random Forests

2021
Missing data present a challenge for most machine learning approaches. When a generative probabilistic model of the data is available, an effective approach is to marginalize missing values out. Probabilistic circuits are expressive generative models that allow for efficient exact inference.
Julissa Villanueva Llerena, Denis Deratani Mauá, Alessandro Antonucci   +2 more
openaire   +1 more source