precipitation

The dataset contains daily accumulated precipitation data (in mm) from Abisko
Scientific Research Station in northern Sweden for 100 years, from 1st January
1913 to 31st December 2012.

datasets

Implements the EM algorithm with one-step Gradient Descent method to estimate
the parameters of the Block-Basu bivariate Pareto distribution with location
and scale. We also found parametric bootstrap and asymptotic confidence
intervals based on the observed Fisher information of scale and shape parameters,
and exact confidence intervals for location parameters. Details are in
Biplab Paul and Arabin Kumar Dey (2023) <doi:10.48550/arXiv.1608.02199>
"An EM algorithm for absolutely continuous Marshall-Olkin bivariate Pareto
distribution with location and scale";
E L Lehmann and George Casella (1998) <doi:10.1007/b98854> "Theory of Point Estimation";
Bradley Efron and R J Tibshirani (1994) <doi:10.1201/9780429246593>
"An Introduction to the Bootstrap";
A P Dempster, N M Laird and D B Rubin
(1977) <www.jstor.org/stable/2984875> "Maximum Likelihood from Incomplete
Data via the EM Algorithm".

Biplab Paul

bvpa

Bivariate Pareto Distribution

Arabin Kumar Dey

precipitation function

A data frame with 36524 rows and 2 columns and the following variables:<dl>
 <dt>Time</dt>
<dd>1st column represents Day.</dd> <dt>Precipitation..mm.</dt>
<dd>2nd column represents daily accumulated precipitation (in mm)
 of the day.</dd>
</dl>

Format

Precipitation data — precipitation

A data frame with 36524 rows and 2 columns and the following variables:<dl>
 <dt>Time</dt>
<dd>1st column represents Day.</dd>

 <dt>Precipitation..mm.</dt>
<dd>2nd column represents daily accumulated precipitation (in mm)
 of the day.</dd>


</dl>

precipitation: Precipitation data

Description

Usage

Arguments

Format

Examples