Description Usage Arguments Details Value References Examples

Generates maximum entropy bootstrap replicates for dependent data. (See details.)

1 2 3 4 |

`x` |
vector of data, |

`reps` |
number of replicates to generate. |

`trim` |
a list object containing the elements: |

`reachbnd` |
logical. If |

`expand.sd` |
logical. If |

`force.clt` |
logical. If |

`scl.adjustment` |
logical. If |

`sym` |
logical. If |

`elaps` |
logical. If |

`colsubj` |
the column in |

`coldata` |
the column in |

`coltimes` |
an optional argument indicating the column that contains the times at which the observations for each individual are observed. It is ignored if the input data |

`...` |
possible argument |

Seven-steps algorithm:

Sort the original data in increasing order and store the ordering index vector.

Compute intermediate points on the sorted series.

Compute lower limit for left tail (

`xmin`

) and upper limit for right tail (`xmax`

). This is done by computing the`trim`

(e.g. 10Compute the mean of the maximum entropy density within each interval in such a way that the

*mean preserving constraint*is satisfied. (Denoted as*m_t*in the reference paper.) The first and last interval means have distinct formulas. See Theil and Laitinen (1980) for details.Generate random numbers from the [0,1] uniform interval and compute sample quantiles at those points.

Apply to the sample quantiles the correct order to keep the dependence relationships of the observed data.

Repeat the previous steps several times (e.g. 999).

The scale and symmetry adjustments are described in Vinod (2013) referenced below.

In some applications, the ensembles must be ensured to be non-negative.
Setting `trim$xmin = 0`

ensures positive values of the ensembles. It also
requires `force.clt = FALSE`

and `expand.sd = FALSE`

. These arguments are
set to `FALSE`

if `trim$xmin = 0`

is defined and a warning is returned
to inform that the value of those arguments were overwritten.
Note: The choice of `xmin`

and `xmax`

cannot be arbitrary and should be
cognizant of `range(x)`

in data. Otherwise, if there are observations outside those
bounds, the limits set by these arguments may not be met.
If the user is concerned only with the trimming proportion, then it can be passed as argument
simply `trim = 0.1`

and the default values for `xmin`

and `xmax`

will be used.

`x` |
original data provided as input. |

`ensemble` |
maximum entropy bootstrap replicates. |

`xx` |
sorted order stats (xx[1] is minimum value). |

`z` |
class intervals limits. |

`dv` |
deviations of consecutive data values. |

`dvtrim` |
trimmed mean of dv. |

`xmin` |
data minimum for ensemble=xx[1]-dvtrim. |

`xmax` |
data x maximum for ensemble=xx[n]+dvtrim. |

`desintxb` |
desired interval means. |

`ordxx` |
ordered x values. |

`kappa` |
scale adjustment to the variance of ME density. |

`elaps` |
elapsed time. |

Vinod, H.D. (2013), Maximum Entropy Bootstrap Algorithm Enhancements. http://ssrn.com/abstract=2285041.

Vinod, H.D. (2006), Maximum Entropy Ensembles for Time Series Inference in Economics,
*Journal of Asian Economics*, **17**(6), pp. 955-978

Vinod, H.D. (2004), Ranking mutual funds using unconventional utility theory and stochastic dominance, *Journal of Empirical Finance*, **11**(3), pp. 353-377.

1 2 3 4 5 6 7 8 9 | ```
## Ensemble for the AirPassenger time series data
set.seed(345)
out <- meboot(x=AirPassengers, reps=100, trim=0.10, elaps=TRUE)
## Ensemble for T=5 toy time series used in Vinod (2004)
set.seed(345)
out <- meboot(x=c(4, 12, 36, 20, 8), reps=999, trim=0.25, elaps=TRUE)
mean(out$ens) # ensemble mean should be close to sample mean 16
``` |

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