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Questions tagged [smoothing]

Smoothing methods in data analysis, like splines or kernel smoothers, also regression smoothers like lowess.

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1answer
22 views

Random effect in GAM - what are the smooth functions used?

In the GAM package in R created by Simon Wood there is a selection of the smooth function basis. I sort of understand the options such as bs='tp', bs='cr', etc. But bs='re' seems odd... that does ...
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0answers
13 views

What do the outputs of the function dlmSmooth from R's dlm package mean?

In the vignette for the R package dlm: link on page 12, the author runs the function dlmSmooth to smooth the data and the function returns an object which is ...
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0answers
29 views

Fixed-Delay Kalman smoother with/without augmented measurements

There are several algorithms regarding fixed-lag Kalman smoothing. In most cases, an augmented state vector is defined in which the elements are the current and delays of the original state vector. ...
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0answers
13 views

Smoothing of experimental data for PCA

I am applying PCA to a set of spectrophotometric measurements with the aim of differentiating two groups of substances. The many small wiggles on the right-hand side of the curves (the region above ~...
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1answer
27 views

Is a Kalman filter ever the optimal way to estimate a dynamic value given a full history of measurements?

I'm trying to get some intuition for Kalman filtering, and I conceived this toy example: Say that I have a sensor that tracks a moving 1-dimensional target. Say that the measurements from the sensor ...
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4answers
108 views

Measure of smoothness

I have an image that has artefacts which I am using a specific process to remove. I want to show that the new image is improved by that process. To compare the two images I am using data from a ...
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3answers
157 views

How to fit a robust step function to a time series?

I have a somewhat noisy time series that hovers around different levels. For example, the following data: I have the solid line data available, and I would like to obtain an estimate for the dashed ...
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0answers
18 views

Is there a filter function that performs similar to moving average but does not loose data?

I need to smooth a time series using a low pass filter. A simple moving average is working fine for me, however, using a moving average causes an inevitable loss of data a the beginning of the ...
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0answers
23 views

Expected Value of Cross Validation approximates Predictive Square Error

In the context of Smoothing Splines, Im trying to show that the expected value of the cross-validation can approximate the predictive square error. More specifically, I want to show that $$E[(y_i - \...
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0answers
44 views

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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1answer
45 views

How to smooth a curve by learning location and shape of 4 Gaussian kernels?

I have a data set of 365 daily curves $f_i(t)$ and want to smooth them by positioning four radial basis functions $r_i(t)$. Each daily curve should then be approximated by $$ \hat{f}_i(t) = w_{i1} ...
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0answers
43 views

How to prove that katz backing off smoothing technique is a valid probability distribution?

How to prove that katz backing off smoothing technique is a valid probability distribution? Take the example of bigram. You can go through this link for katz back off model https://en.wikipedia.org/...
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0answers
23 views

Multivariate smoothing of sensor data?

I have a sensor signal with strong, but not excessive, correlations with other sensors. I can fit a multiple linear regression or a random forest to the sensor signal, showing some predictive power ...
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0answers
20 views

RKHS norm and Fourier transform link

In the notes here, it is stated that norms of some reproducing kernel Hilbert spaces can be written in terms of Fourier transforms, and this is often used to argue that a higher RKHS norm implies a ...
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1answer
22 views

Partial spline. Reference

I have a well done and perfectly working protocol to smooth my experimental data. I do the following: I have a variable of size 1000. Iteratively I choose random 100 points and spline them using the ...
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1answer
57 views

What does “middle of the data” really mean?

I have some noisy time series data for different climate variables and I want to know overall if they are increasing or decreasing with time. From this Water Resources Statistics Guide, the LOWESS ...
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0answers
45 views

difference between LOESS and Spline

I wonder what is the difference between LOESS and Spline. I heard that LOESS is similar with Moving Average. So, is Spline(s) from mgcv::gam is also similar with moving average ? if not, please ...
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1answer
40 views

Show that solution to cubic smoothing spline reduces to regular least squares minimization as $\lambda$ approaches infinity

I am asked to show that the solution to a smoothing splines problem of the form $$ \text{PRSS}(f,\lambda) = \sum_{i=1}^N\left[y_i-f(x_i)\right]^2 + \lambda \int f''(t)^2 dt, $$ with $$ f(x) = \sum_{j=...
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0answers
57 views

$\chi^2$-test for smoothing splines: degrees of freedom

Suppose we are given raw data, e.g. raw mortality rates $\widetilde{q_x}$, which are graduated by a smoothing (cubic natural) spline $S$. That is, we obtain smoothed rates by setting $q_x:=S(x).$ Let ...
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1answer
51 views

Generalized additive models - formula for basis functions

I'm trying to understand the basics of GAMs. Wood's book "Generalized Additive Models: an introduction with R" introduces GAMs via a cubic spline basis $b_j(x)$ (see p. 122), where $b_1(x)=1, b_2(x)=...
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0answers
21 views

How can I do monotonic smoothing in R? [duplicate]

I would like to create a monotonic smoothing function to transform vectors such that I reduce their spikes (i.e., noise) while maintaining their trend. The problem I am encountering with standard ...
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0answers
61 views

Generalized Additive Models: How to fit models with the LMS method by Cole?

I have some problems to understand the univariate generalized models that were proposed by Cole (1992). My question is how the fitting procedure works described in the appendix. More specific, how to ...
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1answer
131 views

Functional Data Analysis and Splines Regression

I'm new to Functional Data Analysis so my question will'be very simple for an expert in this topic. Operatively, when I fit a model like this, in R : model<- lm( y ~ bs( x, df = k, degree = l ) )...
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1answer
83 views

Improve estimate of zeros and how to deal with concurvity in GAM NB

I'm trying to model the number of deaths for each age, for 4 regions, across 5 years. I fitted several models, but end up with the following model: ...
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0answers
32 views

Back-Off with Good-Turing Discounting

How do I handle backing off when using Good-Turing discounting? Let's say I want to score $p(A|B)$. If $A$ has been seen with the word $B$ as its history, I can compute the probability as $p(A|B) = \...
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1answer
279 views

Exponential Smoothing & Seasonality

I am relatively new to stats and forecasting and was hoping to tap the wisdom of this community in regards to a question. One of my colleagues insists that exponential smoothing presumes seasonality. ...
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0answers
50 views

mgcv optimization: why optimize **log** smooth parameter?

In mgcv package, when optimizing smoothing parameters (in the outer iteration), one take the partial derivative of objective function with respect to $\rho = log(\lambda)$. My question is why not take ...
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0answers
25 views

Can you fit a non-parametric regression such that the first derivative will be equal to zero for some specific points?

Given two variables x and y, where y=f(x)+error, is it possible to estimate a non-parametric regression of y on x taking into account the fact that we know that f(x) should take maximum or minimum for ...
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0answers
9 views

accuracy conditional on feature values

I have a binary classification model and I would like to estimate the accuracy as a function of another variable. To be clearer, I can compute the usual accuracy on the testset: $$ acc = \frac{\...
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0answers
38 views

Significance testing of correlated time series

I have two time series (~1200 data points each) and I want to understand how the relationship between them has evolved over time. I took the correlation (Pearson's) over a rolling window and see a ...
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1answer
310 views

Find good smoothing spline factor

I'd like an automatic way to find the "best" smoothing factor s for a spline fit to a given set of data points. Here's a sample visualization of some data and the ...
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1answer
51 views

Exchangeability and data smoothing

If a non-i.i.d sequence of a continuous random variable that is exchangeable, is smoothed by taking rolling average, is the resulting sequence exchangeable? My intuition suggests that it is not. I'll ...
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0answers
80 views

Estimating signal-to-noise ratio after smoothing data and covariance estimates

I have data for two variables, let's say $A$ and $B$, which have distributions that are roughly centered at 0. I am computing a value, let's say $C = \sqrt{A^2 + B^2}$, from this data. I also have a ...
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1answer
130 views

Positive smoothing with the fda-package (Functional data analysis)

In the book Functional Data Analysis with R (Ramsay&Silverman) there is described the possibility to do the "positive smoothing" if it’s needed instead of the "normal smoothing". In the books ...
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26 views

Detect/correct 2d slices outliers from 3d volume

I have to segment 3D volumes (240x240x155 pixels). I am doing this by segmenting 155 2D slices of 240x240 pixels with a CNN. At the end, I reconstruct my 3D volume by simply concatenating the ...
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1answer
46 views

STL function in R: is it possible to make it backward looking?

I would like to smooth modeling input time series data using the stl function in R. Based on my understanding, the function performs a local regression using data ...
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0answers
27 views

How to smooth the decision boundary of a model predicting frost suitability?

I am predicting the suitability of frost formation in a geographical region. Since frost formation is a phenomenon heavily dependent of weather conditions, the model works with the "dew point" (dew) ...
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0answers
45 views

transformation for binary and categorical independent variables

Friends, I have a large dataset in which only Y and one of the independent variables are continuous. there are 12 binary independent variables and 2 other categorical independent variables (each with ...
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1answer
236 views

Probabilistic interpretation of Thin Plate Smoothing Splines

TLDR: Do thin plate regression splines have a probabilistic/Bayesian interpretation? Given input-output pairs $(x_i,y_i)$, $i=1,...,n$; I want to estimate a function $f(\cdot)$ as follows \begin{...
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1answer
197 views

GAM : smoothing splines

Here are 2 questions. Your expertise on those issues would be highly appreciated. In the GAM approach, it makes sense to start with a highly flexible approach and then apply penalties to achieve the ...
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0answers
17 views

Gamma variable in HoltWinters and ETS

R has two different methods for Holt Winters. HoltWinters and ETS. Both return a model with a variable gamma (which is the smoothing parameter for the seasonal component). I wanted to check if they ...
3
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1answer
172 views

Invertibility in Reinsch form Derivation (Smoothing Splines)

In Element of Statistical Learning II, in the context of smoothong splines, $\pmb{S_{\lambda}}$ is defined as $$ \pmb{S_{\lambda}} = \pmb{N}(\pmb{N}^T\pmb{N} + \lambda \pmb{\Omega_N})^{-1}\pmb{N}^T $$ ...
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2answers
92 views

some examples of filtering the noise out of a data set

I have a data set which measures 60 data points in a second (60Hz). Clearly, I do not really need all 60 points in one second since this only generates some noise. ABOUT MY DATA: So my data sets ...
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1answer
137 views

GAM automatic smoothness selection [closed]

I'm trying to use gam in MGCV to automatically select my level of smoothness using cross validation or some equivalent method for my generalized additive model. reading around online it looks like ...
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0answers
36 views

What is the density of $X$ under fixed design?

We observe an i.i.d. sample $(X_1, Y_1), \ldots (X_n, Y_n).$ Let $m(x) = E(Y|X=x),$ $\sigma^2(x) = \operatorname{Var}(Y|X=x)$ and let $f(\cdot)$ be the density of $X.$ Under some regularity ...
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1answer
531 views

Difference between smoothed and moving average?

What's the difference between smoothed and moving average? Are they the same?
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0answers
44 views

Rate Smoothing in Spatial Data -

I asked this question on Reddit a few days ago but didn't get any responses. Working on some spatial epidemiology research a while back, that has resurfaced, I was told that I should smooth the ...
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0answers
28 views

Can I define unique basis functions in GAMs?

I am modelling a process evolving over time using GAMs. There is some theory that says that the time evolution should look like a particular function, so I would like to smooth the time component ...
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1answer
123 views

Denoising technique for signal with beforehand known shape (linear and exponential)

I have a noisy signal which is linear and then exponential. I know the type (Gaussian additive noise) and degree (0.01) of noise. Part of the challenge is determining when the signal changed from ...
3
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1answer
447 views

Moving Average for Unevenly Sampled Time Series

If a time series is unevenly spaced, the simple moving average is not the best option to smooth it out (the window will be larger or narrower depending on the time distance of the events within that ...