Logic behind definition of Reparametrization

In summary, the definition of reparametrization requires a bijective map and the inverse map to be smooth. This means that (t^3,t^6) may describe the same curve as (t,t^2), but it is not considered a reparametrization according to this definition.
  • #1
Raman Choudhary
21
0
What is the intuitive logic behind setting up the definition of reparametrization as being a bijective map and all that(the inverse map being smooth) and not alone that the reparametrisation must give us the same image curve.e.g if we see (t,t^2) as being describing the same curve as (t^3,t^6) but bijective map definition restricts(t^3,t^6) as being the reparametrisation even though physically it(t^3,t^6) describes that same curve??
 
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  • #2
Just to clarify your question, are you saying that (t3,t6) is not a reparametrization of (t,t2)? It seems like it is.
 
  • #3
Yeah that's the thing it is not a reparametrisation of (t,t^2)...this is why i am asking ??
 

Related to Logic behind definition of Reparametrization

1. What is the purpose of reparametrization in scientific research?

Reparametrization is used to simplify and clarify complex mathematical models, making them easier to analyze and interpret. It also allows for more efficient computations and helps to eliminate redundancies in data.

2. How does reparametrization affect statistical inference?

Reparametrization can have a significant impact on statistical inference, as it can change the interpretation of model parameters and their associated statistical properties. It can also help to improve the efficiency and accuracy of statistical estimators.

3. What are the different types of reparametrization methods?

There are several types of reparametrization methods, including linear reparametrization, logarithmic reparametrization, and power reparametrization. Each method has its own advantages and is used in different scenarios.

4. Can reparametrization be used in any type of data analysis?

Yes, reparametrization can be used in various types of data analysis, including regression analysis, multivariate analysis, and time series analysis. It is a versatile tool that can be applied to many different types of models and data sets.

5. Are there any limitations or drawbacks to reparametrization?

While reparametrization can be a powerful tool in scientific research, it is not always appropriate or necessary. In some cases, it may not be possible to simplify a model through reparametrization, and it can also lead to overfitting or bias in certain situations. It is important to carefully consider the implications and potential limitations of reparametrization before using it in data analysis.

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