All Questions
7
questions
1
vote
1
answer
86
views
Does the P value need to be "back- transformed" after logging one variable in Regression analyse?
I have a data series, sediment concentration in water Vs Time. The data is not normal but I want to use regression and so have logged the sediment concentration (log10(x)). The residual and fit to ...
- 11
2
votes
1
answer
737
views
Is Kernel-Regression parametric or non-parametric?
As the title says, is kernel regression a parametric or non-parametric method, and how can this be motivated/explained?
- 35
1
vote
1
answer
986
views
Estimating conditional probability with many samples
I am confused about the estimation of conditional probabilities. Suppose I want to predict a binary outcome variable $Y = 0,1$ given $n$ categorical features $X = (X_1, \ldots, X_n)$, i.e. to ...
- 63
1
vote
0
answers
109
views
Bunching Estimator with Heterogeneous Elasticities - Saez (2010)
The bunching estimator is an estimator developed by Saez (2010) for kinks and Kleven and Waseem (2013) for notches in order to estimate the elasticity of taxable income.
I understand the estimator ...
- 121
77
votes
15
answers
12k
views
Why would parametric statistics ever be preferred over nonparametric?
Can someone explain to me why would anyone choose a parametric over a nonparametric statistical method for hypothesis testing or regression analysis?
In my mind, it's like going for rafting and ...
- 947
5
votes
3
answers
339
views
Regression methods
What is the fundamental difference between:
Linear regression
Non linear regression
Parametric regression, and
Non-parametric regression?
When should we use each type? How do we know what ...
- 641
2
votes
1
answer
115
views
Common parameters for conditional likelihood
I am trying to understand the concept of conditional likelihood in the context of logistic regression.
One paper I am reading defines $L(\theta; y|x) = f(y|x; \theta)$, then goes on to point out ...
- 121