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5
votes
2answers
107 views
+50

Identifying the correlation between a slope and a level

Throughout this post, I assume at least second moments exist. Consider a heterogeneous linear treatment effect model of the form: $$Y_i = \alpha_i + \beta_i X_i$$ where $\alpha_i, \beta_i$ are ...
4
votes
2answers
181 views
+100

What core topics would all statisticians be required to know?

I would like to know what topics are considered 'core knowledge' for a statistician. Please keep in mind I know very little about statistics. At my university, I hear statistics students discuss ...
3
votes
0answers
92 views
+50

On random variables made up of independent random digits

Some random variables can be expressed as a binary expansion whose digits are chosen independently at random; this is called a convolution. One example of this kind of random variable is the one for ...
3
votes
2answers
2k views
+50

Testing predictive power of a set of features

This is perhaps a typical setup in Bioinformatics: we need to build a model to predict a dependent biological variable (say $y$), given a large set of (usually genomic) features $X$ (which might not ...
0
votes
1answer
36 views
+50

Converting Categorical Data to Numerical by Sampling

Suppose I had sampled $1000$ individuals from a population in order to learn about two different questions, both of which had categorical, binary answers. (For the sake of this hypothetical, let's say ...
0
votes
0answers
32 views
+50

Identification of correlated errors with multinominal probit

Consider the multinational probit model where we observe $Y_i \in \{1, \dots, K + l\}$ with $$ \begin{align*} Y_i = l \Leftrightarrow Z_l&\geq \max(Z_1,\dots Z_{K +1}\} \qquad l \in \{1, \dots, ...
2
votes
1answer
77 views
+50

R - generate deviate using probability based on known occurrence of event

In R, I want to simulate an event using a probability $p$ based on a known occurrence $k$ of the event. If I have a population of $n = 100$ individuals in a given area, and I know over a period of ...
2
votes
0answers
43 views
+50

Bayesian Regression- Expectation Maximization

In Bayesian regression, we have $y_i=x_i^{T}w+\epsilon_i$ where $w \sim \mathcal{N}(0,\alpha)$ and $\epsilon_i \sim \mathcal{N}(0,\frac{1}{\beta})$. Inference of $\alpha$ and $\beta$ is done by ...
1
vote
2answers
33 views
+50

Is censoring data necessary to calculate the hazard ratio between 2 KM curves?

I would like to know if censoring data is necessary to calculate the hazard ratio between 2 Kaplan Meier (KM) curves. Censoring data is typically represented by small vertical bars atop of the curve ...
0
votes
1answer
58 views
+100

Let $X_t$ be a solution of a SDE. Does the set $\{X_t \in \{p\}\}$ has null measure?

This question was previously posted on https://math.stackexchange.com/questions/3981156/let-x-t-be-a-solution-of-a-sde-does-the-set-x-t-in-p-has-null-meas. I think this question is easy. However, I ...
2
votes
0answers
35 views
+50

Estimating the errors in parameters in the ordinary least square

I am reading the book An Introduction to Error Analysis by John R. Taylor. In Ch8: Least-Squares Fitting, he has derived expressions for parameters $A$ and $B$ in fitting the line $A+Bx$ to the set of ...
0
votes
0answers
35 views
+50

Find optimal training dataset after concept drift

There are many strategies how to detect a concept drift or model drift, like when there was a major change in the underlying process so that the model becomes invalid. It can be an abrupt change or it ...
0
votes
0answers
33 views
+100

How to calculate uncertainty for predictions coming from cascade of models?

I have developed a bunch of models to predict house prices. It is a 3 fold process: I fit a gbm (first_model) and get the first prediction (first_pred), there are some sub-models (simple lineer ...