Financial professional with expertise in financial modeling, risk management, and related quantitative methods including Monte Carlo simulation, linear and logit regression, and statistical hypotheses testing. For past few years have been engaged in developing econometrics models related to Stress testing for a large financial institution.

Jan
20
comment When to create a control group with paired T test
It is not the same thing. The paired t test tells you how patients fared after taking the drug. The unpaired one tells you if the drug is more effective than a placebo. The unpaired t test is statistically and clinically a more rigorous test. The paired t test result could simply be due to the passage of time (with time patients just got better). The unpaired t test, as structured, controls for that with a Control Group.
Jan
20
answered When to create a control group with paired T test
Jan
15
comment How to detect noisy entries in the data set
Well, that's what Anony-Mousse suggests. I am not familiar with that technique. The first question I would raise is will it work with your logistic regression framework?
Jan
13
comment How to detect noisy entries in the data set
Without knowing how you want to use the data, you can't answer this question. If we knew that you wanted to develop a model to estimate X1 by using X2-X7 as independent variables; once you had developed such a model we could look for outliers among the residuals using various metrics (D's Cook, leverage-Halt, studentized residuals). And, using such metrics we could identify datapoints with too much influence on the coefficients. And, rerun the model without such outliers. But, I don't know what you want to achieve with this data. You can't identify the noise before specifying the model.
Jan
12
awarded Nice Question
Jan
9
comment How do I read this linear model output from R?
Your design structure seems completely different from your model one. The design appears to cater to a paired hypothesis testing framework (difference between same group before and after treatment). Appropriate tests would be paired student t test or its nonparametric equivalents. In this framework, you would not even need a Control group (and I don't know why you need one). But, your R codes clearly suggests this is a multiple regression models with the variables as specified. It seems you should respecify your statistical model completely differently, as suggested.
Jan
9
answered How do I read this linear model output from R?
Dec
14
accepted What are the three forms of the Park test for heteroskedasticity?
Dec
13
answered Why is homogeneity of variance so important?
Dec
12
revised How to use the Glejser test?
added 301 characters in body
Dec
12
comment What are the three forms of the Park test for heteroskedasticity?
gung, I am aware that the Park test has some problems. However, those may not necessarily have anything to do with the independent variables being correlated or even more so... multi collinear. Thanks much for digging out who the professor is who mentioned the three forms of the Park test. I will contact him.
Dec
11
comment What are the three forms of the Park test for heteroskedasticity?
gung, your concept of correlation may be related to thresholds. If two independent variables are so correlated that they are multicollinear (VIF = 10 corresponds to correlation = 0.95). In such case, it may have implications regarding Park. But,if variables are just correlated within an acceptable range, it may have no implications regarding Park. Let's face it, all variables are correlated. The question is are they correlated too much (multicollinear threshold. VIF = 1) or not?
Dec
11
comment What are the three forms of the Park test for heteroskedasticity?
gung, I may let you argue your point regarding the invalidity of the Park test with Park himself. Hopefully, he is still alive. You could have the same debate regarding the Glejser test with Glejser himself constrained by the same condition (hopefully he is still alive). The URL below is where I did find the mentioning of three different forms of the Park tests. But, the Word document described only two of them. As you can tell this statement is not well sourced or referenced. go.owu.edu/~rjgitter/Heteroscedasticity%20Testing%20Homework.doc
Dec
11
asked How to use the Glejser test?
Dec
11
comment What are the three forms of the Park test for heteroskedasticity?
gung, as mentioned I have seen 3 forms mentioned on the Internet. But, statement was not well sourced. I was hoping someone would know readily of those 3 forms and how well established they are. Thanks to you, we are comfortable with the linear form of Park test (similar to Breusch-Pagan). In Wikipedia, they mention you can use Park on one or more Xs. But, those Xs are tested separately unlike in the Breusch-Pagan test. Park test is designed to test a single variable just like Glejser test. Those are valid as structured even if the Xs are correlated to a certain degree.
Dec
11
comment What are the three forms of the Park test for heteroskedasticity?
gung, the source I found mentioning the three forms of the test was not "well sourced." That's actually part of my question. Can one disclose what the 3d form is? And, based on good reference.
Dec
11
comment What are the three forms of the Park test for heteroskedasticity?
This is a very erudite paragraph. But, it does not answer the question. Also, as a clarification the Breusch-Pagan test does test for heteroskedasticity on the whole model (Residuals vs estimates). The Park test instead is customized to test for heteroskedasticity at the independent variable level (Residuals vs X1, or X2, etc...). Your comment brings out an interesting point. And, that is that the linear form of the Park test is identical in structure to the Breusch-Pagan test. Given that, I think this gives much legitimacy to Park's test linear form.
Dec
10
asked What are the three forms of the Park test for heteroskedasticity?
Nov
4
comment How to test heteroskedasticity at the independent variable level?
The Levene's test is appropriate for hypothesis testing to test the difference between two groups Averages. I don't think it is the most appropriate test for testing heteroskedasticity of residuals for the overall regression or relative to specific independent variables. The latter is really my question.
Nov
1
comment How to test heteroskedasticity at the independent variable level?
Breush-Pagan and/or the White Test. Their respective structure, I think, are not workable for testing the independent variables vs testing the overall model. I uncovered that the Park test is appropriate to test independent variable on this count. But, I think there is some reservations regarding this test. There may be some reservations in testing heteroskedasticity at the variable level, I don't know. Any insight on the issue is welcome.
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