New Test Statistics To Assess The Goodness-Of-Fit Of Logistic Regression Models
The binary (or binomial) logistic regression model (LRM) is one of the generalised linear models (GLMs). It is used when the dependent variable is dichotomous and the independent variables are of any type. LRM are popular in many applications and in different disciplines including biomedical and...
Saved in:
| Main Author: | |
|---|---|
| Format: | Thesis |
| Language: | English |
| Published: |
2013
|
| Subjects: | |
| Online Access: | http://eprints.usm.my/43278/1/Jassim%20Nassir%20Hussain24.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| id |
my-usm-ep.43278 |
|---|---|
| record_format |
uketd_dc |
| spelling |
my-usm-ep.432782019-04-12T05:26:17Z New Test Statistics To Assess The Goodness-Of-Fit Of Logistic Regression Models 2013-04 Hussain, Jassim Nassir QA1 Mathematics (General) The binary (or binomial) logistic regression model (LRM) is one of the generalised linear models (GLMs). It is used when the dependent variable is dichotomous and the independent variables are of any type. LRM are popular in many applications and in different disciplines including biomedical and social sciences. Assessing the goodness-of-fit (GOF) is considered to be the important step after fitting the model to show the adequacy of the LRM in fitting the observations. The GOF test is defined as an evaluation of how well the estimated outcomes agree with the observed data. Two techniques may be used to construct the GOF test statistics of chi-square type. The first technique is based on ungrouped observations. This technique is not preferred in the LRM for many reasons including that the obtained distribution and 2013-04 Thesis http://eprints.usm.my/43278/ http://eprints.usm.my/43278/1/Jassim%20Nassir%20Hussain24.pdf application/pdf en public phd doctoral Universiti Sains Malaysia Pusat Pengajian Sains Matematik |
| institution |
Universiti Sains Malaysia |
| collection |
USM Institutional Repository |
| language |
English |
| topic |
QA1 Mathematics (General) |
| spellingShingle |
QA1 Mathematics (General) Hussain, Jassim Nassir New Test Statistics To Assess The Goodness-Of-Fit Of Logistic Regression Models |
| description |
The binary (or binomial) logistic regression model (LRM) is one of the
generalised linear models (GLMs). It is used when the dependent variable is
dichotomous and the independent variables are of any type. LRM are popular in
many applications and in different disciplines including biomedical and social
sciences. Assessing the goodness-of-fit (GOF) is considered to be the important step
after fitting the model to show the adequacy of the LRM in fitting the observations.
The GOF test is defined as an evaluation of how well the estimated outcomes agree
with the observed data. Two techniques may be used to construct the GOF test
statistics of chi-square type. The first technique is based on ungrouped observations.
This technique is not preferred in the LRM for many reasons including that the
obtained distribution and |
| format |
Thesis |
| qualification_name |
Doctor of Philosophy (PhD.) |
| qualification_level |
Doctorate |
| author |
Hussain, Jassim Nassir |
| author_facet |
Hussain, Jassim Nassir |
| author_sort |
Hussain, Jassim Nassir |
| title |
New Test Statistics To Assess The
Goodness-Of-Fit Of
Logistic Regression Models |
| title_short |
New Test Statistics To Assess The
Goodness-Of-Fit Of
Logistic Regression Models |
| title_full |
New Test Statistics To Assess The
Goodness-Of-Fit Of
Logistic Regression Models |
| title_fullStr |
New Test Statistics To Assess The
Goodness-Of-Fit Of
Logistic Regression Models |
| title_full_unstemmed |
New Test Statistics To Assess The
Goodness-Of-Fit Of
Logistic Regression Models |
| title_sort |
new test statistics to assess the
goodness-of-fit of
logistic regression models |
| granting_institution |
Universiti Sains Malaysia |
| granting_department |
Pusat Pengajian Sains Matematik |
| publishDate |
2013 |
| url |
http://eprints.usm.my/43278/1/Jassim%20Nassir%20Hussain24.pdf |
| _version_ |
1747821192701542400 |