TY - JOUR
T1 - Designing experiments for estimating an appropriate outlet size for a silo type problem
AU - MAY, CATERINA
AU - LOPEZ-FIDALGO, JESUS
AU - MOLER, JOSE ANTONIO
N1 - Publisher Copyright:
© Institute of Mathematical Statistics, 2023.
PY - 2023
Y1 - 2023
N2 - Jam formation is a problem that may occur when granular material is discharged by gravity from a silo. The estimation of the minimum outlet size which guarantees that the time to the next jamming event is long enough can be crucial in the industry. The time is modeled by an exponential distribution with two unknown parameters, and this goal translates to precise estimation of a non-linear transformation of the parameters. We obtain $c$-optimum experimental designs with that purpose, applying the graphic Elfving method. Because the optimal experimental designs depend on the nominal
values of the parameters, we conduct a sensitivity analysis on
our dataset.
Finally, a simulation study checks the performance of the approximations, first with the Fisher Information matrix, then with the linearization of the function to be estimated. The results are useful for experimenting in a laboratory and translating then the results to a real scenario. From the application, we develop
a general methodology for estimating a one-dimensional
transformation of the parameters of a nonlinear model.
AB - Jam formation is a problem that may occur when granular material is discharged by gravity from a silo. The estimation of the minimum outlet size which guarantees that the time to the next jamming event is long enough can be crucial in the industry. The time is modeled by an exponential distribution with two unknown parameters, and this goal translates to precise estimation of a non-linear transformation of the parameters. We obtain $c$-optimum experimental designs with that purpose, applying the graphic Elfving method. Because the optimal experimental designs depend on the nominal
values of the parameters, we conduct a sensitivity analysis on
our dataset.
Finally, a simulation study checks the performance of the approximations, first with the Fisher Information matrix, then with the linearization of the function to be estimated. The results are useful for experimenting in a laboratory and translating then the results to a real scenario. From the application, we develop
a general methodology for estimating a one-dimensional
transformation of the parameters of a nonlinear model.
KW - {bulk solid storage}
{jam formation}
{non-linear heteroscedastic model}
{optimal design of experiments}
KW - {bulk solid storage}
{jam formation}
{non-linear heteroscedastic model}
{optimal design of experiments}
UR - https://iris.uniupo.it/handle/11579/139955
U2 - 10.1214/22-AOAS1644
DO - 10.1214/22-AOAS1644
M3 - Article
SN - 1932-6157
VL - 17
SP - 606
EP - 620
JO - THE ANNALS OF APPLIED STATISTICS
JF - THE ANNALS OF APPLIED STATISTICS
IS - 1
ER -