Questions tagged [smoothing]
Smoothing methods in data analysis, like splines or kernel smoothers, also regression smoothers like lowess.
291
questions
3
votes
2answers
61 views
Forcing smoothness of regression coefficients
I'm building regression models on spectral datasets: the predictors are the intensites of signal at the different frequencies. In this case the intensities at close frequency values are highly ...
0
votes
1answer
32 views
Smoothing time series with non-constant variance
I have a discrete time series $x(t)$ $ t = \{0,\Delta t,2\Delta t\dots\}$ in which every point comes with a confidence value $c(t)$ arising from the measurements. You may think of is as the variance ...
1
vote
2answers
68 views
What are the exact factors used in smooth.spline by R?
What exactly is optimized mathematically when I use:
smooth.spline(x, y, lambda)
in terms of the integrated second derivative?
Is it
$$\min_{f\in C^2} \sum_{i=1}...
0
votes
0answers
37 views
Demmler-Reinsch basis for smoothing splines
I have seen some papers about using the so-called Demmler-Reinsch basis for smoothing spline because it is a basis for natural spline space and also Sobolev space. For example, these papers:
A ...
0
votes
0answers
79 views
Smoothing in a monotonically increasing manner
I have a number of curves that contain numbers from between 0 and 1. The curves should be monotonically increasing, but due to random noise, there may be some times where it is decreasing.
Is there ...
0
votes
0answers
28 views
predicting X values from smooth.spline
I have an existing smooth.spline object, and I wish to estimate X values for a set of new Y values. I see that ...
0
votes
1answer
37 views
Questions re. fitting a polynomial: Runge's phenomenon solutions
I have data on hospital treatment times. I would like to fit a polynomial to the data using least-squares.
In a previous question raised before I have already been advised against this, but ...
2
votes
2answers
80 views
Questions re. fitting a polynomial: smoothing, cross-validation, etc
I have data on hospital treatment times. I would like to fit a polynomial to the data. My data comes in 5 minute increments and it is very noisy. It looks like this:
I can aggregate to a higher ...
2
votes
1answer
31 views
How can I smooth data in 2D coordinates that has time-dependent error?
I have collected some GPS data from running over and around a hill many, many times.
The hill itself is about 9-10 meters high compared to the ground around it, although when I collected data, my ...
1
vote
2answers
105 views
What is the difference between smoothing and decomposition in time series?
i am bit new to time series modelling, currently i am trying to understand some basics. What is the difference between smoothing and decomposition in time series . I have gone through many materials , ...
0
votes
0answers
23 views
Matrix inversion in the smoothing splines [duplicate]
The question is about the matrix inversion in the smoothing splines.
Given observations (y1, x1), ..., (yn, xn) and a choice of $\lambda \ge 0$, the smoothing spline estimator, $\hat{f}_{\lambda}$ is ...
0
votes
0answers
32 views
R: how to get effective degrees of freedom?
I'm building a sparse additive model and using the Generalized Cross Validation score for a linear smoother $\hat f(x) = L(x) \underline Y$
$$GCV(\lambda) = \frac 1n \sum_{i=1}^n \left( \frac{Y_i - \...
0
votes
1answer
86 views
Should I do detrending or smoothing first?
Does it matter which one I perform first? If yes, why?
Might be a simple question, yet I could not find an answer anywhere else.
3
votes
1answer
63 views
How does the penalized form of RSS (residual sum of squares) work?
In another word, how to reverse engineering the equation (5.9) by explain all the assumption and reasoning after the plus sign of (5.9) in Elements of Statistical Learning.
Note: I had used the ...
0
votes
0answers
18 views
how to parameter the gamess function?
i want to run gampackage to calculate threshold and semi parametric model, especialy partialy linear model like :
Y = V1 + V2 + V3 + f(V4)
where:
Y is factor and dichotomic varible;
V1 is continuous ...
0
votes
0answers
21 views
What does alpha in smoothing stand for
I want to apply Gaussian smoothing to a dataset and came across the smth.gaussian function in R. That besides the numerical input data requires two parameters:
...
1
vote
1answer
35 views
How to generate mean curve of non-function?
I am currently working on curves generated in tensile tests of polymer specimens. Here, I try to generate a mean curve of five data sets generated at the same composition of the samples. Unfortunately,...
5
votes
2answers
324 views
Is there a name for a moving average when it is done not across time but some other variable?
The moving average is defined as
A method of smoothing a time series to reduce the effects of random variation and reveal any underlying trend or seasonality.
(Oxford Dictionary of Statistics, ed. ...
asked May 2 at 17:09
0
votes
0answers
17 views
Smoothing detected vehicle positions from camera given my own vehicle's location, velocity and acceleration
I have a dashcam which detects the position and estimated depth of other vehicles on the road relative to my own. Given my own vehicle's global co-ordinates, I can convert the detected vehicle's ...
0
votes
0answers
89 views
Detecting trend in panel data, smoothing techniques and outlier detection
I'm conducting an analysis on a Landsat scene to detect trends for change detection phenomena (forest disturbances) over a time series of 20 years. I identified on the image the pixels that are ...
1
vote
1answer
37 views
Why 2nd derivative is “squared” to represent wigglyness in GAM?
In David Miller's presentation (here, slide 21), he drew 1st derivative and 2nd derivative of a function. Then he said (slide 22) that grey part can is :
$ \int (\frac{\partial ^{2}f(x)}{\partial x^{...
0
votes
0answers
26 views
How we handle unknown bigrams in bigram probability model with Good-Turing discounting?
Assume Good-Turing discounting.
Assume number of unknown words is equal to the number of known words in our event space.
Let $s$ be a sentece such that $s=w_1,w_2,\ldots,w_n$.
We know the probability ...
1
vote
0answers
32 views
Smoothing with non-regular observations
If we have data that changes continuously in time, and we sample this data at regular intervals - i.e. we get samples $x_0$, $x_1$, $\dots$, where the time $\Delta t$ between taking samples $x_i$ and $...
0
votes
1answer
92 views
Retrieving time series from a smoothed periodogram
If I were to smooth a periodogram and then filter out low level frequencies, how can I derive the filtered time series? For example, in the case of a non-smoothed periodogram: https://folk.uib.no/...
0
votes
1answer
224 views
Natural spline term in GAM
Is it advisable to use natural regression spline basis? I learned that in R the supported smoothers in gam are the lo, ...
0
votes
0answers
18 views
Time series regression with temporal smoothing of regressors
I'm trying to forecast a discrete time series $Y_t$ with many observations of a very noisy covariate $X_t$. I could simply use a large number of lags:
$$ \mathrm{E}[Y_t] = \beta_0 + \sum_{i=1}^k \...
0
votes
0answers
28 views
Wavelet smoothing or regression with scattered data
Suppose we have a data set $\{x_i, y_i\}_i$ where $x_i$ is a multi-dimensional tuple and scattered (not on a equally spaced regular lattice). How does one regress or smooth such a scattered data set ...
0
votes
0answers
28 views
Reproducing Holt-Winters analysis in the Cowpertwait-Metcalfe book
I am having trouble reproducing some output for some R code in the time series book by Cowpertwait and Metcalfe. There are quite a few typos in their code throughout the book, but in other cases I ...
0
votes
0answers
27 views
Good turing smoothing for unigram LM
I was wondering if it is at all possible to use good turing smoothing for unigram language model?
I know that this smoothing technique helps distribute the weights from most occurring words to less ...
1
vote
0answers
24 views
Transforming a categorical distribution up side down
I have the following categorical distribution:
$$
original = (2, 7, 3, 5, 0, 1, 4, 6, 3, 8, 8)\\
\sum original = 47
$$
I want to transform it to its upside down distribution step by step, while ...
1
vote
0answers
17 views
Computing a moving average when data points arrive one at a time
I am sure there's a cool python/numpy/pandas way to do this.
I am receiving one data point at a time. I would like to compute something like a moving average over the last n observations, even better ...
1
vote
1answer
185 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 ...
1
vote
0answers
48 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 ...
0
votes
0answers
41 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.
...
1
vote
0answers
32 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 ~...
0
votes
1answer
56 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 ...
3
votes
4answers
770 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 ...
7
votes
3answers
462 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 ...
1
vote
0answers
83 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 ...
1
vote
0answers
33 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 - \...
1
vote
0answers
73 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^{-...
3
votes
1answer
64 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
$$ \hat{f}_i(t) = w_{i1} ...
1
vote
0answers
116 views
RKHS norm and Fourier transform link
In the notes here, it is stated that norms of some reproducing kernel Hilbert spaces can be written in terms of Fourier transforms, and this is often used to argue that a higher RKHS norm implies a ...
1
vote
1answer
32 views
Partial spline. Reference
I have a well done and perfectly working protocol to smooth my experimental data. I do the following:
I have a variable of size 1000. Iteratively I choose random 100 points and spline them using the ...
3
votes
1answer
64 views
What does “middle of the data” really mean?
I have some noisy time series data for different climate variables and I want to know overall if they are increasing or decreasing with time. From this Water Resources Statistics Guide, the LOWESS ...
2
votes
1answer
118 views
Show that solution to cubic smoothing spline reduces to regular least squares minimization as $\lambda$ approaches infinity
I am asked to show that the solution to a smoothing splines problem of the form
$$
\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=...
1
vote
1answer
133 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)=...
6
votes
1answer
397 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 ) )...
0
votes
1answer
145 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:
...
1
vote
1answer
475 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. ...
