Can we apply non-linear smoothing to a linear looking like data ?

Then, for any value of x, you obtain a value of y. This is a valid way to remove discontinuity or to make a linear correlation stronger. In summary, applying non-linear smoothing to almost linear data can help remove discontinuity and strengthen linear correlation. However, it is important to determine the appropriate coefficients for the smoothing process.
  • #1
paawansharmas
19
0
My doubt is that whether we can apply non-linear smoothing to a almost linear data ( without one or 2 discontinuity)

I have attached the pic in which the red data is the smoothed one. Blue is the original one.
I multiplied each point with an increasing like 1, 1.1, 1.2, 1.3, 1.4...so on

My question is : is this valid way to remove discontinuity or to make a linear correlation stronger ??

thanks


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  • #2
It really sounds like a question in the science of statistics.

For some series it'll make the linear correlation stronger, for some weaker. It probably depends on the effect you want, or the features you want to find in your data.

Statistics can also help you choose the correct coefficients for whatever kind of dampening.

So I advise you to ask this again in (or move the thread to) the statistics forum.
 
  • #3
Thanks Amir.
But i do not know how to move it to other forum.
and if I write same thread in another forum , I will get warnings for multiple posting.

please help.

Thanks
Paawan
 
  • #4
paawansharmas said:
My doubt is that whether we can apply non-linear smoothing to a almost linear data ( without one or 2 discontinuity)

To move a thread, you can use the "report" feature (even though the directions for report sound like it is only to be used to report naughty things) and "report" that you would like your thread moved.

You have not defined a mathematical problem. It isn't clear what you are trying to accomplish.

To take a silly point of view, you can do anything you want with the data. You could erase each value and write in a different number that pleases you! However, your question suggests you think only certain procedures will accomplish your goal. But what is the goal?
 
  • #5
The answer to your question is 'yes'. Your curve looks like having a quadratic component. Take your model as Y= a+ bx + cx^2. Minimize sum[(y-Y)^2] with respect to a,b,c, where y= observed values.
 

1. Can we apply non-linear smoothing to a linear looking like data?

Yes, it is possible to apply non-linear smoothing techniques to data that appears linear. Non-linear smoothing methods such as polynomial regression or moving average can be used to capture non-linear trends in linear-looking data.

2. What is the purpose of non-linear smoothing?

The purpose of non-linear smoothing is to eliminate noise and reveal underlying patterns or trends in data. It can also help to improve the accuracy of predictions or estimates made on the data.

3. How is non-linear smoothing different from linear smoothing?

Linear smoothing methods, such as simple moving average, assume that the data follows a linear trend. Non-linear smoothing methods, on the other hand, allow for more flexible patterns and can capture non-linear trends in the data.

4. What are the limitations of using non-linear smoothing on linear looking data?

Non-linear smoothing methods may not always be effective in capturing complex non-linear patterns in the data. Additionally, if the data is truly linear, using non-linear smoothing techniques may not provide any significant improvement in accuracy.

5. Can non-linear smoothing be applied to all types of data?

Non-linear smoothing can be applied to a wide range of data, including time series data, cross-sectional data, and panel data. However, the effectiveness of the method may vary depending on the data and the underlying patterns present.

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