Questions tagged [numerics]
Also known as Numerical Analysis, Numerics aims to provide methods and algorithms for numerical computations.
141
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Establish numerical equivalence of statistical model across software
we are trying to establish numerical equivalence (within reasonable precision) for selected statistical models across programming languages such as SAS, R & Python or even different packages ...
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How can I compute rectangular confidence regions for parameters using R?
Simultaneous confidence regions for multivariate parameters (say, a confidence region for multivariate mean, or for regression parameters) usually find an elliptical region when the parameters' ...
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Can I make Stata run lasso faster?
I am trying to run a lasso in Stata.
I have 1.5 million observations and 1700 variables.
Stata is running too slow.
I am in 36th grid after 4 days. And get slower ever grid.
I am using a 98GB Memory, ...
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More stable reparametrization of a parameter on $(-1,1)$?
Suppose that a distribution contains a parameter $\theta \in (-1,1)$. I want to reparametrize this model in terms of $\beta = h(\theta) \in (-\infty,\infty)$.
I am considering:
$$h(\theta) = \mbox{...
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Is there a closed form approximation for the composition of the Gamma CDF with the inverse Normal CDF?
Given $k$, $\theta$ fixed shape and scale parameters for some Gamma distribution which has a CDF $F$. Let $G^{-1}$ be the inverse CDF of the standard Normal distribution. Consider the composition $H(x)...
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Generalized variance of a multivariate normal without calculating determinants
In order to calculate ‘generalized variance’ of a multivariate normal distribution, it is often recommended (e.g., here: https://online.stat.psu.edu/stat505/lesson/1/1.5) to calculate the determinant ...
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Can I create a numerical variable from the combination of other numerical variables? [duplicate]
I am working with a dataset that contains limnological parameters. This dataset contains turbidity-related measures in different units and consequently different ranges. Is there a way to create a ...
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Is there a way to create a numeric variable from a numeric set of measurements with different ranges?
I am working with a dataset that contains limnological parameters. One of them is turbidity-related measures in different units and consequently different ranges. Is there a way to create a numerical ...
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Are numerical solutions appropriate for inference (eg, estimating variance for confidence intervals)?
For a nicely differentiable objective function, we traditionally always derive the gradients to use for e.g. estimating the variance. (1) Is it common nowadays to use numerical rather than analytical ...
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Estimate MLE of discrete distribution with two parameters in R [closed]
I want to estimate the MLE of a discrete distribution in R using a numeric method.
My data looks like this:
data1<-c(5,2,2,3,0,2,1 2,4,4,1)
If we assume it ...
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Avoid numerical problems with product of probabilities when taking logs or subtracting the maximum is not enough
I have to take draws from the discrete posterior distribution:
$ P(X = x_i |y) \propto P(X = x_i)\prod_{t}^N p(Y_t|X)$
where $P(X = x )$ is the probability mass function of a discrete uniform with ...
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Get accurate eigenvectors, when eigenvalues are minuscule
I have a symmetric matrix A. I'm not able to compute all the eigenvectors accurately, and I believe it is due to the last few eigenvalues for ...
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Numerically PCA implements SVD or SVD implements PCA
How do we numerically implement SVD? I confused the numerically implementations between PCA and SVD (who implements who). Since we know that
PCA can be numerically implemented by ...
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Find the shape parameter for gamma CDF with other parameters known
Is there a reasonably computationally efficient way to find an approximation of the shape parameter (which will always be positive, but not limited to integers) for a gamma CDF (scale = 1) where the ...
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Are there examples where errors in numerical analysis has caused wrong decisions in statistics?
I read from statistics books that numerical analysis is not in important role in statistics. Are there any examples in history where numerical approximation has been caused wrong decisions in ...
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Is the QR Algorithm guaranteed to compute eigenvectors?
I'm writing some C++ matrix library for hobby.
For computing eigenvalues and eigenvectors, I referred the following "Francis double step QR algorithm":
In particular, page 82 of https://...
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First difference in logs transformation produces biased results on back-transformation [duplicate]
I have a strongly trended series where the trend appears to be exponential and I believe the errors tend to be proportional to the current value. In order to convert it to a stationary series for ...
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What is the correct equation for Newton's Method?
Different publications provide different equations for Newton's method or the Newton-Raphson method. In
Giudici, P., Givens, G. H., & Mallick, B. K. (2013). Wiley Series in Computational ...
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I'm so confused about MLE
In maximum likelihood estimation, we maximise the likelihood.
I don't understand how this is possibly: for any reasonable dataset, the likelihood of hitting that EXACT data set is obviously zero! So ...
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Numeric variable with outliers as a categories
I'm working with a dataset that has a few variables that I'm having difficulty trying to preprocess. So one of them is called MENTHLTH where it is a numeric variable.
The point of the variable is to ...
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Seeking algorithms for fast, simple linear regressions
I am working on a project which requires me to work in a proprietary programming language. Unfortunately, this language lacks a matrix algebra library. In this environment, I would like to perform a ...
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Discrete correlation function (sample cross-covariance)
The continuous correlation function for the random variable $A(t)$ at a instant of time $t$ is given by
\begin{equation}
C_{AA}(\tau) =\frac{1}{T} \int_{0}^T d\bar{t} A(\bar{t})A(\bar{t}+\tau)
\end{...
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How to plot $x^{1700}(1-x)^{300}$?
I'm trying to plot a Bernoulli likelihood function on R:
$$x^{1700}(1-x)^{300}$$
But when I try to plot this function on R it looks like this:
I think the maximum should be at 0.85, but it shows me a ...
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Log Sum Exponential Trick On Weibull Mixture [duplicate]
I am trying to evaluate the log-likelihood of a mixture of weibull distributions and am running into problems with the numerical aspect. In short, I have $M$ mixtures and want to evaluate:
$$
\log \...
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log(1 - softmax(X))? [closed]
Let $\vec X$ be a vector. The $\vec V = \mathrm{logsoftmax}(\vec{X})$ function is defined as:
$$v_i = \ln\left(\frac{e^{x_i}}{\sum_i e^{x_i}}\right)$$
This is provided in machine learning numerical ...
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Time series reference - Estimating ARIMA models using the Yule-Walker equations and Durbin-Levinson algorithm
I am doing some research related to time-series analysis, and hence was trying to find some good examples of implementations of the common estimation algorithms. For example, I wanted to fit some ...
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Reference request - time series analysis book with numerical algorithms
I am working on some applications of time series, and I wanted to find a book that has the numerical algorithms or pseudocode for computing things like AR models, and ARIMA models, using nonlinear ...
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Why is my QR decomposition updating code numerically off?
I apologize if this is the wrong place for this question; there are a number of potential points of failure each of which suggest either Math StackExchange or StackOverflow or here, but since the ...
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Wrong coefficients in a polynomial fit
I am trying to fit data to a fourth-degree polynomial. I tried this in multiple programs (R, Origin Pro, SigmaPlot), all of which give me a polynomial of the form
$ 40000 -2000x + 40x^2 -0.3x^3 + 0....
user277312
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Where is the error in my computation of the wrapped normal distribution density?
Let
$\sigma\in(0,1)$
$$\phi(x):=\frac1{\sqrt{2\pi\sigma^2}}e^{-\frac{x^2}{2\sigma^2}}\;\;\;\text{for }x\in\mathbb R$$ and $$\psi(x):=\sum_{k\in\mathbb Z}\phi(x+k)\;\;\;\text{for }x\in\mathbb R$$
$\...
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How should I compute this proposal kernel density?
Let $d\in\mathbb N$ and $$u(x,y):=\beta+(1-\beta)\prod_{i=1}^d\psi(y_i-x_i)\;\;\;\text{for }x,y\in[0,1)^d,$$ where $\beta\in[0,1]$, $$\psi(x):=\sum_{k\in\mathbb Z}\varphi(k+x)\;\;\;\text{for }x\in(-1,...
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Algorithm for simple linear regression that is efficient and numerically stable
I'm developing an application that is fed with continuous data while older data is discarded.
I'm using some algorithms to compute simple linear regression on these data with Perl.
Basically that ...
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Equivalent of log sum exp trick for subtraction [duplicate]
I have two small positive real numbers $u, w$ such that $u > w$. Given $\log(u), \log(w)$ I'd like to find a numerically stable way to calculate $\log(u - w)$.
One possible way of transforming the ...
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Why did the log likelihood decrease with additional parameters?
I'm trying to decide the effect of some factors on the time for an event to happen . Specifically, I am looking at how long it takes for the subject to pass a test (recognize the stimulus) when ...
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Expectation of $\ln(1 + e^x)$, where $x$ is normally distributed
I need to evaluate the following integral:
$$\int_{-\infty}^\infty\mathrm d x \exp\left(-\frac{(x-\mu)^2}{2\nu}\right) \ln(1+e^x)$$
where $\mu$ is a finite real number and $\nu > 0$. This is just ...
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How does the choice of norm affect the condition of a problem?
we know that for a differentiable problem, the absolute condition number is the norm of its jacobian i.e. ||J||. We also know that a well-conditioned problem typically has a small condition number.
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Inverse-normal CDF approximation in Excel, Python or R
I read that the implementations of Inverse-normal cumulative distribution function (CDF) /quantile / ppf in R, Python (scipy) and Excel give similar results. However, I can't find the very formulae ...
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Why does lasso return unstable features when using the same data?
I am using scikit-learn to shrink my data set having around 800 features. It is a very noisy data (market and economic data) To my best knowledge, lasso returns same features for the same data set. ...
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Intuition about a coupon problem were we ask for the distribution of the unique coupons when the number of draws is fixed
Alternative viewpoint of the coupon collectors problem
In the coupon collectors problem we draw from a collection of $n$ coupons, with replacement and ask the question how many draws $K$ it takes to ...
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Why is it much quicker to compute ridge regression than regular linear regression? [duplicate]
By my understanding, for a matrix with n samples and p features:
Ridge regression using cholesky takes O(p^3) time
Ordinary linear regression takes O(p^3) time
Singular value decomposition if u, v ...
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How can we numerically compute the autocorrelation of a sample from a Markov chain generated by the Metropolis-Hastings algorithm?
Let $(X_n)_{n\in\mathbb N_0}$ denote a $\mathbb R^d$-valued Markov chain generated by the Metropolis-Hastings algorithm. Suppose I've run the algorithm on a computer and obtained a sample $x_0,\ldots,...
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Model failed to converge in lme4::glmer() when the a factor is centered or releveled
I'm running a mixed-effects model using glmer() function. The modeling works well with R's default dummy coding. But if I center or relevel a factor of 2 levels, the model failed to converge. I am ...
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chi square distribution as a function of other variables
I'm minimizing $\chi^2$ between some data and a function. The expression $\chi^2 = \sum\frac{1}{\sigma_i^2}(y_i-f(A,B))^2$ happens to be analytically solvable, so I have a value for the minimum $\chi^...
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adding a small constant to the diagonals of a matrix to stabilize
I have a large correlation matrix (110x110) with some small eigenvalues (about 20 < 0.1). It has been suggested that adding a constant (about 0.1) to the diagonals will help to stabilize the matrix....
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downsampling a kde / combining kde and histogram
I'm calculating a KDE of one parameter (y, particle density) in bins of another parameter (x, distance from the origin). At ...
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Model or numerical inaccuracy in R package distr
With the goal to have an "outlier"-aware normal distribution, I build a simple univariate mixing model of normal and uniform distributions:
...
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NUTS Drawing samples from slice sampler; how to keep bounds on log scale?
I'm currently working to adapt the No U-Turn Sampler from this paper for a model I'm working on.
The No-U Turn sampler augments the typical hamiltonian system by incorporating a slice variable $u$ ...
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Natural Splines and Smoother Matrix
In the context of smoothing splines, one can show that the Reinsch form is given by:
$
\hat{y} = N (N^{T}N +\lambda \Omega)^{-1}N^{T} y
= (I+ \lambda K)^{-1}y
$
where (1) $K = (N^{T})^{-1}\Omega N^{-...
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305
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Moment generating function of a Weibull distribution and root finding heavy and light tailed case
I consider the equation $M_x(v)=1+(1+\beta)\mu$ and I need to find the solution $v>0$ such that the equation is fulfilled.
For this example I consider the moment generating function $M_X(v)$ of a ...
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3
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951
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Estimating correlation matrix using numeric likelihood maximization
I'm performing maximum likelihood estimation on jointly distributed data and I'm having some issues estimating the correlation terms. I am using an approach based on the Cholesky decomposition, but I ...
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