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We are conducting a factor analysis in R, beginning by estimating mixed correlation matrices using the psych package. When doing so, we get the following warning: "Matrix was not positive ...
I have the following problem:
I have a time series with counted data. I now want to smooth it using a Gaussian low-pass filter.
Is there a method to determine the sigma value? The window should have a ...
I have these values (negative and positive) and I want to determine the nonlinear relationship between variable and predictor using generalized additive models (GAMs).
...
I am wondering why you would want to use a by variable smooth s(time, by= x)? (time is non-linear hence why I am using GAMs)
I am using GAMs to explore if my ...
We need to show that a smoothing spline of $y_i$ to $x_i$ retains the local regression part of the fit.
For linear regression, this problem seems trivial because it is relatively easy to move from $...
I'm trying to use Statsmodels' simple exponential smoothing for time series analysis.
There are various methods available for initializing the recursions (estimated, heuristic, known).
Can someone ...
I'm using what I have been referring to as 'kernel density estimation' to estimate the rates of a series of variables a, b, c from noisy observations distributed in ...
I am a part-time M.Sc candidate that has been advised to apply a GAM to my temperature reconstructions instead of a LOESS smoother. I have read a lot of papers on GAM, have watched some webinars on it ...
Let's say we have a sequential decision making problem. At each step, we need to make a decision, and the decision made in this step will determine the possible actions for the next step.
Now I have a ...
This is the predictions of a binary classification model. The model is doing predicitons continuously, and these values are the sum of positive labels during a 10 hours period. As you can see, some of ...
A land surveyor made a topographic map of a property in the hills by measuring the elevation at different points. The x-y coordinates are more or less "random", meaning no regular grid:
...
I am trying to estimate the trend of a time series. I have used a non-robust loess smoothing with a window size of 30 points. Looking at the raw data, it is clear that there is no increase at the end ...
In a tutorial I came across this:
"Recall that the forecast value is: $\hat{y}_{t+1} = \frac{y_t + y_{t-1} + ... + y_{t-m+1}}{m}$
It's worth pondering that formula for a minute. While easy to ...
Cross validation allows to automatically select a reasonable smoothing parameter for a given data set, avoiding underfitting and overfitting. Cross validation requires independent data, an assumption ...
Eric Topol posted the below on Twitter claiming that Europe is "turning COVID around" based on the trend in the past <7 days. However, while the case trend hitherto appears smooth, I was ...
When I read The Elements of statistical learning, in the section of "5.5 Automatic Selection of the Smoothing Parameters", equation (5.26) gives a LOOCV formula for corresbonding $\lambda$
...
In ordinary least squares regression, for outcome vector $y$ and design matrix $X$ (full rank), the estimated coefficient values are $\hat{\beta} = (X^TX)^{-1}X^TY$. Given a new set of covariates $X_{...
I'm currently working with a daily (business days) time series which has a monthly seasonality and an overall positive trend over the last two years. I want to estimate the error component of the time ...
I have just recently started using GAMLSS models (after being pointed in that direction in this question), and I'm wondering wether it's 'legit' to use smoothing (i.e. cubic splines in my case) to ...
I have a simple task of fitting a smooth line with monthly points across six years. The line doesn't need to go through any specific point, but the yearly totals need to be specific values.
Is there a ...
I'm trying to calculate the bias of the estimator $p(h)=n^{-1}\displaystyle\sum_{i=1}^{n}(Y_{j}-\hat{m}_{h}(X_{j})^{2}w(X_{j})$ of the averaged squared error. The result I find in the literature is ...
What's the best way to find outliers in the time-series like the one attached?
Just using the knowledge that it is a measurement of a continuous physical process. The data is quite short (200-500 ...
We have $N$ samples of the unknown function $f(x)$ on the finite interval $[a, b]$. The samples are subject to white noise of known variance. We want to approximate the function $f(x)$ by a polynomial ...
I have different vectors field and I want to characterize their "smoothness" or tendency to align, or like their heterogeneity. Here are two examples, a relatively smooth vector field on the ...
I'm digging into smoothing splines and finding good resources, but no one talks about how to actually calculate the penalty $\int \hat{f}^{"}(x)^2 dx$ in the standard smoothing spline loss:
Since <...
What's the best way to find outliers in the time-series, encountering continuity?
I attached two time-series that I'm interested to filter. One is less noisy, and one is a bit noisier. I'm mostly ...
To construct a plot, I'm looking for an algorithm which can handle inf and (very) negative values.
If I have infinity values everything is a line but not infinities.
Example
...
I am using Penalized spline to smooth noisy data. Those splines are non parametric regression models which only rely on a smoothing parameter $\lambda \geq 0$ (which has to be chosen).
I would like to ...
I would like to know which strategies are used in practice to choose the correct smoothing function for each features in the GAMs, both for classification and regression tasks. I thought of plotting ...
I'm playing around with the built-in matlab movmean function which, using default options, creates the simple and centered moving average of given data (I'm pretty ...
I uses supsmu to fit a smooth curve to a scatter plot of data .
I am quite new to curve fitting, but my graph looks somewhat sinusoidal. What would you recommend in terms of next steps to determine a ...
I've generated a PDF of binned data using the python package binsmooth. The PDF is plotted in the following image:
I am trying to smooth the PDF so as to provide a more intuitive interpretation of ...
I have categorical, time-series data distributed in space. It is very noisy, but over the whole series there are big shifts in distribution - my goal is to see how these shifts progressed in space. So,...
How create multidimensional smoothing spline for regression in R? Is there a function to implement thin-plate spline or tensor product?
I see the function ssanova() based on tensor product but I don't ...
I read a paper on thin plate splines that stated several advantages. Thin plate splines:
Do not rely on ’knots’ as many other spline methods do
Are of isotropic character (i.e. unaffected by the ...
I am a bit unsure how the newdata= argument is set in the prediction function. Here I have found the optimal degree of freedom to be 4 via 10-fold CV. I have fitted ...
So instead of using the (LOOCV) cv argument in smooth.spline(), I would like to use a block of 10-fold CV code to find the optimal degree of freedom.
I find that the smooth.spline model is somewhat ...
I am creating a calibration curve to asses the fit of a logistic regression.
Does it make more sense to use the local or global optimum smoothing parameter for the LOESS line?
The orange line uses ...
While using the Holt-Winters model for seasonality, I am unable to choose a better fit between additive and multiplicative models. I used to look at RMSE value and choose the one with the lower RMSE. ...
When using the density function in R, it includes smooth transitions down to 0 at both ends of the data. Is there a way to prevent this? As a trivial example, ...
I am trying to smooth some binned data. I have a discrete variable X which might best be thought of as time and a continuous variable Y. I want to know the average Y value for each value of X and this ...
I know similar questions have been asked for time series data. But my question is a little bit different.
Consider that we have input dataset $X \in R^{N \times M}$, where $M$ is the dimension of ...
I'm working on an application that tracks an object's position in 3D space through time. The hardware generates (x,y,z,t) data points, with a non-uniform sampling ...
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