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Generally speaking, with the way people use the term nonparametric to decribe a model, if you had more data points, there would be more parameters, generally without an upper limit. It doesn't mean "has no parameters" nor is it required to be infinite-parametric with a finite sample size.

To take a different example, consider say spline models. Even though with some given sample you could give a list of parameters and their estimates (that is, after all, how you calculate the smooth fit), it is still nonparametric in this sense of the word.

Similarly nonparametric distributional models for continuous data can be based on things like the ecdf itself or on a histogram or on a KDE. In particular note that with the KDE, even though for a given kernel family (say Gaussian), the kernel itself is "parametric" in the sense that it has a single parameter, the density estimate itself is not fixed-parametric.

In each such case, the number of parameters is not fixed and will generally grow with sample size (not always linearly).

Generally speaking, with the way people use the term nonparametric to decribe a model, if you had more data points, there would be more parameters, generally without an upper limit. It doesn't mean "has no parameters" nor is it required to be infinite-parametric with a finite sample size.

To take a different example, consider say spline models. Even though with some given sample you could give a list of parameters and their estimates (that is, after all, how you calculate the smooth fit), it is still nonparametric in this sense of the word.

Similarly nonparametric distributional models for continuous data can be based on things like the ecdf itself or on a histogram or on a KDE. In particular note that with the KDE, even though for a given kernel family (say Gaussian), the kernel itself is "parametric" in the sense that it has a single parameter, the density estimate itself is not fixed-parametric.

In each such case, the number of parameters that describes the function is not fixed and will generally grow with sample size (not always linearly).

Generally speaking, with the way people use the term nonparametric to decribe a model, if you had more data points, there would be more parameters, generally without an upper limit. It doesn't mean "has no parameters" nor is it required to be infinite-parametric with a finite sample size.

To take a different example, consider say spline models. Even though with some given sample you could give a list of parameters and their estimates (that is, after all, how you calculate the smooth fit), it is still nonparametric in this sense of the word.

Similarly nonparametric distributional models for continuous data can be based on things like the ecdf itself or on a histogram or on a KDE.

In each case, the number of parameters is not fixed and will generally grow with sample size (not always linearly).

Generally speaking, with the way people use the term nonparametric to decribe a model, if you had more data points, there would be more parameters, generally without an upper limit. It doesn't mean "has no parameters" nor is it required to be infinite-parametric with a finite sample size.

To take a different example, consider say spline models. Even though with some given sample you could give a list of parameters and their estimates (that is, after all, how you calculate the smooth fit), it is still nonparametric in this sense of the word.

Similarly nonparametric distributional models for continuous data can be based on things like the ecdf itself or on a histogram or on a KDE. In particular note that with the KDE, even though for a given kernel family (say Gaussian), the kernel itself is "parametric" in the sense that it has a single parameter, the density estimate itself is not fixed-parametric.

In each such case, the number of parameters is not fixed and will generally grow with sample size (not always linearly).

Often with a nonparametric model, if you had more data points, there would be more parameters, without an upper limit. It doesn't mean "has no parameters" nor is it required to be infinite-parametric with a finite sample size.

To take a different example, consider say spline regression models for example. Even though with some given sample you could give a list of parameters and their estimates (that is, after all, how you calculate the smooth fit), it is still nonparametric in this sense of the word.

Similarly nonparametric distributional models for continuous data can be based on things like the ecdf itself or on a histogram or on a KDE.

In each case, the number of parameters is not fixed and will generally grow with sample size (not always linearly).

Generally speaking, with the way people use the term nonparametric to decribe a model, if you had more data points, there would be more parameters, generally without an upper limit. It doesn't mean "has no parameters" nor is it required to be infinite-parametric with a finite sample size.

To take a different example, consider say spline models. Even though with some given sample you could give a list of parameters and their estimates (that is, after all, how you calculate the smooth fit), it is still nonparametric in this sense of the word.

Similarly nonparametric distributional models for continuous data can be based on things like the ecdf itself or on a histogram or on a KDE.

In each case, the number of parameters is not fixed and will generally grow with sample size (not always linearly).