Statistical Inference for a Class of Startup Demonstration Tests
No Thumbnail Available
Date
2019
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Taylor & Francis inc
Open Access Color
Green Open Access
No
OpenAIRE Downloads
OpenAIRE Views
Publicly Funded
No
Abstract
In this article, we develop a general statistical inference procedure for the probability of successful startup p in the case of startup demonstration tests when only the number of trials until termination of the experiment are observed. In particular, we define a class of startup demonstration tests and present expectation-maximization (EM) algorithm to get the maximum likelihood estimate of p for this class. Most of well-known startup testing procedures are involved in this class. Extension of the results to Markovian startups is also presented.
Description
Eryilmaz, Serkan/0000-0002-2108-1781
ORCID
Keywords
EM algorithm, Markov chain, maximum likelihood estimation, phase-type distributions, reliability
Turkish CoHE Thesis Center URL
Fields of Science
0211 other engineering and technologies, 02 engineering and technology, 0101 mathematics, 01 natural sciences
Citation
WoS Q
Q1
Scopus Q

OpenCitations Citation Count
4
Source
Journal of Quality Technology
Volume
51
Issue
3
Start Page
314
End Page
324
PlumX Metrics
Citations
Scopus : 6
Captures
Mendeley Readers : 6
Google Scholar™

OpenAlex FWCI
0.80175
Sustainable Development Goals
1
NO POVERTY

3
GOOD HEALTH AND WELL-BEING

4
QUALITY EDUCATION

5
GENDER EQUALITY

7
AFFORDABLE AND CLEAN ENERGY

8
DECENT WORK AND ECONOMIC GROWTH

9
INDUSTRY, INNOVATION AND INFRASTRUCTURE

10
REDUCED INEQUALITIES

12
RESPONSIBLE CONSUMPTION AND PRODUCTION

16
PEACE, JUSTICE AND STRONG INSTITUTIONS

17
PARTNERSHIPS FOR THE GOALS


