example91

A dataset containing the data set used in Example 9.1 on page 369 of Qiu (2014).

datasets

The methods discussed in this package are new non-parametric methods
based on sequential normal scores 'SNS' (Conover et al (2017)
<doi:10.1080/07474946.2017.1360091>), designed for sequences of observations,
usually time series data, which may occur singly or in batches,
and may be univariate or multivariate. These methods are designed
to detect changes in the process, which may occur as changes in location
(mean or median), changes in scale (standard deviation, or variance), or
other changes of interest in the distribution of the observations,
over the time observed. They usually apply to large data sets,
so computations need to be simple enough to be done in a reasonable
time on a computer, and easily updated as each new observation
(or batch of observations) becomes available. Some examples and more detail
in 'SNS' is presented in the work by Conover et al (2019) <arXiv:1901.04443>.

Luis Benavides

SNSchart

Sequential Normal Scores in Statistical Process Management

Victor Tercero

Jorge Merlo

example91 function

The data (X,Y) consist of 20 batches with 50 observations in each batch.<dl class="dl-horizontal">
 <dt>V1</dt><dd>1st batch.</dd>
 <dt>V2</dt><dd>2nd batch.</dd>
</dl>

Format

Data from Example 9.1 on page 369 Qiu (2014). — example91

The data (X,Y) consist of 20 batches with 50 observations in each batch.<dl class='dl-horizontal'>
 <dt>V1</dt><dd>1st batch.</dd>
 <dt>V2</dt><dd>2nd batch.</dd>
</dl>

example91: Data from Example 9.1 on page 369 Qiu (2014).

Description

Usage

Arguments

Format