Confidence intervals for point-of-stabilization of content uniformity

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3A10448839" target="_blank" >RIV/00216208:11320/22:10448839 - isvavai.cz</a>

Výsledek na webu

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=ZR~6vsgBd7" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=ZR~6vsgBd7</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1002/pst.2207" target="_blank" >10.1002/pst.2207</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Confidence intervals for point-of-stabilization of content uniformity

Popis výsledku v původním jazyce

Within the framework of continuous pharmaceutical manufacturing, we are interested in statistical modeling of the initial behavior of the production line. Assuming a gradually changing sequence of a suitable product quality characteristic (e.g., the content uniformity), we estimate the so-called point-of-stabilization (PoSt) and construct corresponding confidence regions based on appropriate asymptotic distributions and bootstrap. We investigate linear, quadratic, and nonlinear gradual change models both in homoscedastic and heteroscedastic setup. We propose a new nonlinear E-max gradual change model and show that it is applicable even if the true model is linear. Asymptotic distribution of the PoSt estimator is known only in a homoscedastic linear and quadratic model and, therefore, bootstrap approximations are used to construct one-sided PoSt confidence intervals.

Název v anglickém jazyce

Confidence intervals for point-of-stabilization of content uniformity

Popis výsledku anglicky

Within the framework of continuous pharmaceutical manufacturing, we are interested in statistical modeling of the initial behavior of the production line. Assuming a gradually changing sequence of a suitable product quality characteristic (e.g., the content uniformity), we estimate the so-called point-of-stabilization (PoSt) and construct corresponding confidence regions based on appropriate asymptotic distributions and bootstrap. We investigate linear, quadratic, and nonlinear gradual change models both in homoscedastic and heteroscedastic setup. We propose a new nonlinear E-max gradual change model and show that it is applicable even if the true model is linear. Asymptotic distribution of the PoSt estimator is known only in a homoscedastic linear and quadratic model and, therefore, bootstrap approximations are used to construct one-sided PoSt confidence intervals.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

<a href="/cs/project/GF22-01639K" target="_blank" >GF22-01639K: Postupné funkcionální změny - GraFuCha</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Pharmaceutical Statistics

ISSN

1539-1604

e-ISSN

1539-1612

Svazek periodika

21

Číslo periodika v rámci svazku

5

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

16

Strana od-do

944-959

Kód UT WoS článku

000773669400001

EID výsledku v databázi Scopus

2-s2.0-85127341594