meboot.part

Maximum entropy density based dependent data bootstrap.
An algorithm is provided to create a population of time series (ensemble)
without assuming stationarity. The reference paper (Vinod, H.D., 2004 <DOI: 10.1016/j.jempfin.2003.06.002>) explains
how the algorithm satisfies the ergodic theorem and the central limit theorem.

Fred Viole

meboot

Maximum Entropy Bootstrap for Time Series

meboot.part function

<dl> <dt>x</dt>
<dd>vector of data, <code>ts</code> object or <code>pdata.frame</code> object.</dd> <dt>n</dt>
<dd>length of <code>x</code>.</dd> <dt>z</dt>
<dd>class intervals limits.</dd> <dt>xmin</dt>
<dd>lower limit in the left tail.</dd> <dt>xmax</dt>
<dd>upper limit in the left tail</dd> <dt>desintxb</dt>
<dd>desired inteval means.</dd> <dt>reachbnd</dt>
<dd>logical. If TRUE potentially reached bounds (xmin = smallest value - trimmed mean and xmax=largest value + trimmed mean) are given when the random draw happens to be equal to 0 and 1, respectively.</dd></dl>

Arguments

meboot Internal Function — meboot.part

<dl>

 <dt>x</dt>
<dd>vector of data, <code>ts</code> object or <code>pdata.frame</code> object.</dd>

 <dt>n</dt>
<dd>length of <code>x</code>.</dd>

 <dt>z</dt>
<dd>class intervals limits.</dd>

 <dt>xmin</dt>
<dd>lower limit in the left tail.</dd>

 <dt>xmax</dt>
<dd>upper limit in the left tail</dd>

 <dt>desintxb</dt>
<dd>desired inteval means.</dd>

 <dt>reachbnd</dt>
<dd>logical. If TRUE potentially reached bounds (xmin = smallest value - trimmed mean and xmax=largest value + trimmed mean) are given when the random draw happens to be equal to 0 and 1, respectively.</dd>

</dl>

meboot.part: meboot Internal Function

Description

Usage

Value

Arguments

See Also