Questions tagged [transform]

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20 views

Transforming Logistic Regression Model

I have a Logistic Regression Model in R ...
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25 views

Copula between a distribution and it's univariate transformation

been reading for a while, first time questioner. I'm trying the compute the copula (or joint distribution) between x and a univariate transformation, like say sin(x). That is compute $C_{XY}$ (or $F_{...
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1answer
44 views

Inverse Gaussian chi square connection

The inverse Gaussian distribution $IG(\mu,\lambda)$ is associated with the density $$f(x;\mu,\lambda) = \sqrt{\frac{\lambda}{2\pi x^3}}\,\exp\left\{-\frac{\lambda(x-\mu)^2}{2\mu^2x}\right\}\qquad \...
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1answer
49 views

PMF of $aX_1 + bX_2$ (Bernoulli)

Let $Y_1 = aX_1 \sim \text{Bernoulli}(p)$ and $Y_2 = bX_2 \sim \text{Bernoulli}(p)$, what is the PMF of $Z = Y_1 + Y_2$ for $a > 0$, $b > 0$ and $a \neq b$? Can somebody check my result? $$p_{...
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1answer
22 views

A Normal distribution variable in the power of N

If $X$ is normal distributed random variable, what is the distribution of $|X|^n$? I am struggling to understand the distribution. Any guidance would be appreciated.
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32 views

Why can I interpret a log transformed dependent variable in terms of percent change in linear regression?

Looking at resources such as this one and this one, you see claims like "Exponentiate the coefficient, subtract one from this number, and multiply by 100. This gives the percent increase (or ...
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2answers
77 views

How do you get the double sum or integral from $E(X+Y)$ (expected value)?

I was given a proof for $E(X+Y)$ = $E(X)+E(Y)$ for cases where both variables are either discrete or continuous: Discrete: $$ \begin{align*} E(X+Y) &=\sum_{x\in\mathcal X}\sum_{y\in\mathcal Y}(x+y)...
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Laplace-Stieltjes Transforms and distribution

I was going through a paper, I came across below relation, \begin{equation} T=\begin{cases} C, & \text{with probability $P(H<C)$}\\ 0, & \text{with probability $P(H>C)$} \...
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Is the copula function invariant only under deterministic monotonic transformation?

I read about the following theorem (see Proposition 3 in the picture below) on the invariance of copula under monotonic transformation, my questions is: 1. Are the $T_i$ mentioned in the following ...
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How can I obtain a full-conditional distribution resulting from a transformation that has a dimension reduction?

Problem statement Suppose we have a function $h(\mathbf{s})=1-[\exp(e^{\beta_0+\beta_1(||\mathbf{s}-\mathbf{x}||)^2})]^{-1}=y$, where both s and x are $1\times2$ vectors, $y$ is a scalar, $\mathbf{s}=(...
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1answer
95 views

Change of variables in pdf

I have the joint pdf$$f(x_1,x_2)=x_1e^{-x_1(1+x_2)}I_{(0,\infty)}(x_1)I_{(0,\infty)}(x_2)$$and have to derive the joint pdf of $$Y_1=e^{-X_1}\qquad\text{ and }\quad Y_2=e^{-X_1X_2}$$ I set $x_1=-\ln(...
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1answer
53 views

How can I shift the average probability keeping constraint (0.0:1.0)?

I have a large datasets of values that range from 0 to n. I am interpreting the values as probabilities for a later pseudo-random selection process. To make the values serve as probabilities, I ...
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5 views

Knowledge Distillation - Comparing different methods

I recently got into this field and I am a little confused. For example, in this paper by Hinton and this paper, how exactly are we supposed to interpret the results? I mean sure, this kind of training ...
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1answer
115 views

How to interpret a specific data transformation?

I came across this specific data transformation in the context of a physics application, which by itself is rather complex and hence out of the scope of this question. However since this ...
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1answer
33 views

How do you work with a function of a uniform distribution? [closed]

I am struggling with parts b and c. How do you solve them? Could you please give the solution?
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39 views

how do you transform/standardise a function to always give values between y1 and y2?

Having lost some of my math skills, I am having problems with something that I think should be fairly easy but is eluding me: I have a plateau shaped function that I would like to standardise such ...
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Can I use Linear Regression or do I need Nonlinear Regression

I am trying to fit these two equations to data in R via regression. First Equation: $$y(x) = a + \frac{b}{c + x^m}.$$ This equation is constant plus reciprocal function, resulting in a hyperbolic ...
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1answer
51 views

Normalize target value for linear regression

I'm building a regression model to predict sensor value over time. Bellow is a figure of my sensors data over time: Based on this video about transforming nonlinear data with a log function, What ...
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24 views

Squeeze a time series to fit in a range while maintaining shape

I have the following time series with intermediate highs and lows marked by the vertical lines: I want to transform/squeeze the series so that the resulting series would fit in a range, let's say [0,...
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1answer
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What is the general second-order Taylor approximation to $\mathbb{V}(f(X))$?

If $X \sim \text{N}(0, \sigma^2)$ it is well-known that we have the second-order Taylor approximation: $$\mathbb{V}[f(X)] \approx f'(\mu)^2 \cdot \sigma^2 + \frac{f''(\mu)^2}{2} \cdot \sigma^4.$$ ...
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Probability of a random variable being positive

Suppose $x \sim p(x); x \in R $ is a random variable. Here I do not assume any family distribution of $x$ like x is Gaussian or exponential distribution. I would like to find a clear form of a ...
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1answer
77 views

How to present Confidence Interval for Log-Transformed Means & Mean Difference?

After trying to read on this topic, I still have some clarifications remaining. Context: Comparing between 2 arms (categorical), measuring microbiological plate-counted bacteria concentration (...
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1answer
397 views

How to reduce kurtosis of data

I'm trying to reduce the kurtosis of my dataset and make it approximately Gaussian, with a common-sense uni-modal shape. The raw data looks like this: I first tried ...
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Data transformation - pointwise or batch?

A data transformer performs a pre-preprocessing step ("transformation") before an estimator can fit or classify the data. The transformation step is a projection (any idempotent map: $T^2 = T$), ...
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What is the laplace transform of the below given PDF?

Really am interesting to know more about statistical properties of the following PDF , of the Random variable $z$: $$F(\sigma,\mu,z)= \frac{(z-\sigma )^2 \exp \left(-\frac{(z-\sigma )^2 \sqrt{\left(...
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How to transform/convert likelihoods to scores?

I have the probability of loan default for a labeled dataset where the distribution of probabilities is heavily skewed. Labels are defined as "good/0" for no default and "bad/1" for defaults. My goal ...
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1answer
68 views

Transformation of Confidence Interval = Confidence Interval of Transformation? [duplicate]

I am wondering about the following situation: I have a confidence interval estimator $\delta(x)=[lb, ub]$, which returns valid a%-confidence intervals for a value $\theta \in \mathbb{R}$ (not ...
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1answer
104 views

Modelling exchange rates: how to log transform percentage changes?

I'm trying to model an exchange rate to test for extreme values. However, I have percentage changes from day to day. Given some changes are negative, I can't take the logarithm. Any idea how I could ...
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1answer
34 views

Is SVM RBF applied to both classes?

Lets say i have following 1D data (position on x), color is target class and I need a classifier which classifies green from red: I decided to use SVM. Data is clearly not linearly separable, so i ...
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2answers
677 views

How can I obtain a Cauchy distribution from two standard normal distributions?

I am interested in Let $X\sim N(0,1), Y \sim N(0,1)$ independently. Show $\frac{X}{X+Y}$ is a Cauchy random variable. My work: $f_{X,Y}(x,y)=\frac{1}{2\pi} e^{\frac{-1}{2}(x^2+y^2)}, -\infty&...
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Why is the mean of the natural log of a uniform distribution (between 0 and 1) different from the natural log of 0.5?

For a uniformly distributed variable between 0 and 1 generated using rand(1,10000) this returns 10,000 random numbers between 0 and 1. If you take the mean, it ...
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Can ADVI (Variational Inference) Induce Weak Multi Modality in a system with Uniform Priors, if a Gaussian Variational Family is Used

Question Set Up If I have a weakly multi modal (see below in the edit) target posterior distribution which I am aiming to approximate using ADVI (Automatic Differentiation Variational Inference) with ...
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1answer
70 views

Transformation of Uniform Distribution to Real Number Line in ADVI

In the Automatic Differentiation Variational Inference (ADVI) paper, the authors claim to solve the VI problem in a transformed parameter space, which is over $\mathbb{R}$, in order to simplify the ...
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1answer
93 views

How can I generate 2 sets of variables from different distributions with a correlation between them in r? [duplicate]

I am working in R and would like to generate 40 numbers from $\mathrm{N}(0,1)$ and another 40 from $\mathrm{Uniform}(0,2)$ with a negative correlation (for example: $r = -0.45$) between them. The ...
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How to interpret hourly rate data given in one miniute intervals

I have time series data on natural gas flow, which is in units of "tonnes/hour". But the data are given in one minute intervals (each row represents a single minute of time duration). Here is a sample:...
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1answer
589 views

What does 'km' transform in cox.zph function mean?

I'm trying to understand how cox.zph function in r programming language works and I find myself not knowing what km transform ...
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463 views

Techniques to apply Discrete Wavelet Transform (DWT) to denoise and predict time series

I just started playing with wavelets and have been using this library (https://github.com/rafat/wavelib) to further my understanding and see if 'denoising' the series at all possible levels is ...
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1answer
753 views

How does the inverse transform method work in discrete r.v.?

In this question How does the inverse transform method work? it's mentioned the general procedure to generate r.v. U <- runif(1e6) X <- qnorm(U) X How ...
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1answer
523 views

CDF Variable Transformation

Let $X$ be uniform on $(-1, 2)$ and let $Y = X^2$. Find the pdf of $Y$. So far I have noted that $F_X(x) = P(X \leq x) = \int_{-1}^x \frac{1}{3} dt = \frac{1}{3}(x+1)$. Then, since $Y=X^2$, $y \in [...
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1answer
152 views

Transforming non-normal to normal distribution and back-transform

I would like to transform non-normal distribution to normal distribution, and back-transform to its original state (or at least close to the original state). From this article, I've read that you can ...
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0answers
56 views

VaR/inverse cdf of transformation of normal variables

I have the following exercise to solve as good preparation for an exam: NOTE: $VaR_p(X)$ = Value at risk = $F^{-1}_X(p)$ Consider the bivariate normal random vector $(X_1, X_2)$. The marginals are ...
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1answer
125 views

In OLS, while using log-log and linear-log transforamtions, is valid to transform some regressors only?

In OLS I was wondering if it is valid to log-transform some regressors only. Specifically, continuous regressors, because it is advised not to transform binary or categorical variables. For instance, ...
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1answer
50 views

How to scale between a equal distribution and an empirical distribution

I am not that good at expressing things mathematically, so I'll start with the practical problem right away: I have a set of four objects: O1, O2, O3, O4. Now I want to assign a variable that scales ...
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1answer
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Interpreting adjusted R-squared of a log transformed regression model

I am running a linear regression model where the dependent variable (Y) is log-transformed. I am struggling on how to interpret the adjusted R-squared of this log-transformed model that is meaningful. ...
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General approaches and techniques for developing good explanatory models for nonlinear data

Various recent efforts of mine on modelling some data through logistic regression have been... not successful. While there is still more data to look at, I've been wanting to explore nonlinear ...
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94 views

A transformation from uniform random variable to Gaussian mixture

I am attempting to describe a prior_transform for a multivariate Gaussian mixture in order to estimate the evidence integral of that prior convolved with another likelihood distribution. This is ...
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1answer
74 views

Likelihood of the product of a normal cdf and pdf

Suppose you had a random sample of r.vs X_i , i= 1....n . What is the likelihood of 2 * pdf(x) * cdf(x) , with pdf and cdf of the standard normal distribution?
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1answer
137 views

Variance after resampling uniformly from a sample from a normal population

I have recently been looking into the Bootstrap, and I was wondering, if I were to have a sample $X=\{x_1,x_2,\dots,x_N\}$ that has $N$ samples, all i.i.d coming from a normal distribution, $N(\mu,\...
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1answer
8k views

Difference between a exponential model and power model

There was given some data, in which I have carry out a linearizing procedure, using either a power model or a exponential model. From my understanding, power models and exponential models are ...
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1answer
64 views

Estimates of some function of a parameter (MCMC)

Let's say I have some MCMC framework whereby I am estimating two parameters $$(\log\alpha,\log\beta)$$ The reason for the $\log$ functions is so that the implementation of the joint prior ...