example87

A dataset containing the data set used in Example 8.7 on page 339 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

example87 function

A data frame with 86 rows (86 batches of size equals to 1)<dl class="dl-horizontal">
 <dt>X</dt><dd>observations of all batches</dd>
 <dt>X.id</dt><dd>id of each observation of the batch</dd>
 <dt>Y</dt><dd>reference sample of size equals to 14</dd>
</dl>

Format

Data from Example 8.7 on page 339 Qiu (2014). — example87

A data frame with 86 rows (86 batches of size equals to 1)<dl class='dl-horizontal'>
 <dt>X</dt><dd>observations of all batches</dd>
 <dt>X.id</dt><dd>id of each observation of the batch</dd>
 <dt>Y</dt><dd>reference sample of size equals to 14</dd>
</dl>

example87: Data from Example 8.7 on page 339 Qiu (2014).

Description

Usage

Arguments

Format