Smooth quantile normalization

CQN (Conditional Quantile Normalization)

When to use Quantile Normalization?

Normalization and preprocessing are essential steps for the analysis of high-throughput data including next-generation sequencing and microarrays. Multi-sample global normalization methods, such as quantile normalization, have been successfully used to remove technical variation from noisy data. These methods rely on the assumption that observed global changes across samples are due to unwanted...

Subset Quantile Normalization Using Negative Control Features

Normalization has been recognized as a necessary preprocessing step in a variety of high-throughput biotechnologies. A number of normalization methods have been developed specifically for microarrays, some general and others tailored for certain experimental designs. All methods rely on assumptions about data characteristics that are expected to stay constant across samples, although some make ...

Feature Normalization Using Smooth

We propose a method for estimating the parameters of SPLICElike transformations from individual utterances so that this type of transformation can be used to normalize acoustic feature vectors for speech recognition on an utterance-by-utterance basis in a similar manner to cepstral mean normalization. We report results on an in-house French language multi-speaker database collected while deploy...

A Smooth Nonparametric Conditional Quantile Frontier Estimator

Traditional estimators for nonparametric frontier models (DEA, FDH) are very sensitive to extreme values/outliers. Recently, Aragon, Daouia, and Thomas-Agnan (2005) proposed a nonparametric α-frontier model and estimator based on a suitably defined conditional quantile which is more robust to extreme values/outliers. Their estimator is based on a nonsmooth empirical conditional distribution. In...