Results 271 to 280 of about 16,362,739 (323)
Some of the next articles are maybe not open access.
Visualization and Imputation of Missing Values
Statistics and computing, 2023M. Templ
semanticscholar +1 more source
Dealing with missing values in proteomics data
Proteomics, 2022Weijia Kong, H. Hui, Hui Peng, W. Goh
semanticscholar +1 more source
2019
Preprint submitted to Decision Support Systems ...
Mart��nez-Plumed, Fernando +3 more
openaire +1 more source
Preprint submitted to Decision Support Systems ...
Mart��nez-Plumed, Fernando +3 more
openaire +1 more source
Incomplete Databases: Missing Records and Missing Values
2012Data completeness is an essential aspect of data quality as in many scenarios it is crucial to guarantee the completeness of query answers. Data might be incomplete in two ways: records may be missing as a whole, or attribute values of a record may be absent, indicated by a null.
Werner Nutt +2 more
openaire +1 more source
This chapter applies our concept of total value to a discussion of the Australian TV crime drama series, Miss Fisher’s Murder Mysteries (ABC 2012-2015) one of Australia’s most successful TV exports. Miss Fisher has been sold to an unprecedented 247 territories in 179 countries and is available on a range of different networks and platforms thus ...
Sue Turnbull, Marion McCutcheon
openaire +1 more source
Sue Turnbull, Marion McCutcheon
openaire +1 more source
A Test of Missing Completely at Random for Multivariate Data with Missing Values
, 1988R. Little
semanticscholar +1 more source
2014
In this chapter the reader is introduced to the approaches used in the literature to tackle the presence of Missing Values (MVs). In real-life data, information is frequently lost in data mining, caused by the presence of missing values in attributes. Several schemes have been studied to overcome the drawbacks produced by missing values in data mining ...
Salvador García +2 more
openaire +1 more source
In this chapter the reader is introduced to the approaches used in the literature to tackle the presence of Missing Values (MVs). In real-life data, information is frequently lost in data mining, caused by the presence of missing values in attributes. Several schemes have been studied to overcome the drawbacks produced by missing values in data mining ...
Salvador García +2 more
openaire +1 more source
1994
We now assume that we have missing values in the second covariate. The observability of X2 is indicated by a random variable $$O_{2}:\begin{cases} & \text{1 if}\;\;X_{2} \;\;\textup obervable \\ & \text{0 if}\;\;X_{2} \;\;\textup unobservable \end{cases}\;\;\;\;2\dagger)$$ and instead of X2 we observe the random variable $$Z_{2}:=\begin{
openaire +1 more source
We now assume that we have missing values in the second covariate. The observability of X2 is indicated by a random variable $$O_{2}:\begin{cases} & \text{1 if}\;\;X_{2} \;\;\textup obervable \\ & \text{0 if}\;\;X_{2} \;\;\textup unobservable \end{cases}\;\;\;\;2\dagger)$$ and instead of X2 we observe the random variable $$Z_{2}:=\begin{
openaire +1 more source
Tensor Completion for Estimating Missing Values in Visual Data
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009Ji Liu +3 more
semanticscholar +1 more source