This is possibly a duplicate of this question of mine, however, here I ask for clarification regarding an estimation that is done when calculating the expectation of the kernel density estimator (KDE) using little-o. The conditions on the KDE stated here are inspired by an exercise in an undergraduate textbook.
Background
Suppose $x_1, ..., x_n$ are independent and identically distributed observations of a random variable $X$ with unknown distribution function $F$ and probability density function $f\in C^m$, for some $m>1$ fixed. Let $k\in C^{m+1}$ be a given fixed function such that \begin{align} k&\geq 0, \\ \mathrm{supp} (k)&=[-1,1], \\ \int_{\mathbb{R}} k(u)\mathrm{d}u&=1, \\ \int_{\mathbb{R}} k(u)u^l\mathrm{d}u&=0 \ \text{for all} \ 1\leq l<m \ \text{and}\\ \int_{\mathbb{R}} k(u)u^m\mathrm{d}u&<\infty . \end{align} Define the KDE $f_n$ of $f$ by $$f_n(t)=\frac{1}{n}\sum_{i=1}^n \frac{1}{h}k\left(\frac{t-x_i}{h}\right),$$ where $h=h(n)$ is the bandwidth. What is the expectation of $f_n$, i.e. $\mathbb{E}[f_n(t)]$?.
By linearity of the expectation, identical distribution of $x_1,...,x_n$, the law of the unconscious statistician and the change of variables $u=(t-x)/h$, \begin{align} \mathbb{E}[f_n(t)]&=\frac{1}{n}\sum_{i=1}^n \mathbb{E}\left[\frac{1}{h}k\left(\frac{t-x_i}{h}\right)\right]\\ &=\mathbb{E}\left[\frac{1}{h}k\left(\frac{t-x}{h}\right)\right]\\ &=\int_{\mathbb{R}}\frac{1}{h}k\left(\frac{t-x}{h}\right)f(x)\mathrm{d}x\\ &=\int_{\mathbb{R}}\frac{1}{h}k(u)f(t-hu)h\mathrm{d}u\\ &=\int_{\mathbb{R}}k(u)f(t-hu)\mathrm{d}u. \tag{1} \end{align} From $f\in C^m$, it follows that $$f(t-hu)=\sum_{l=0}^m \frac{f^{(l)}(t)}{l!} (-hu)^l+o((hu)^m). \tag{2}$$ $o(g(y))$ is a set (or a function) such that $f(y)\in o(g(y))$ (or $f(y)=o(g(y))$) satisfies $\lim_{y\to y_0} f(y)/g(y)=0$ for $y_0$ denoting a real number, a complex number or $\pm \infty$. In $(2)$, $y_0=0$. From $(1)$, $(2)$ and linearity of integration, \begin{align} \mathbb{E}[f_n(t)]&=\int_{\mathbb{R}}k(u)\left(\sum_{l=0}^m \frac{f^{(l)}(t)}{l!} (-hu)^l+o((hu)^m)\right)\mathrm{d}u \\ &=\sum_{l=0}^m\int_{\mathbb{R}}k(u)\frac{f^{(l)}(t)(-hu)^l}{l!}\mathrm{d}u+\int_{\mathbb{R}}k(u)o((hu)^m)\mathrm{d}u. \tag{3} \end{align} From the given conditions on $k$, the $l=0$ term reads $$\int_{\mathbb{R}} k(u)f(t)\mathrm{d}u=f(t)\int_{\mathbb{R}} k(u) \mathrm{d}u=f(t).$$ The $1\leq l<m$ terms are $$\int_{\mathbb{R}} k(u)\frac{f^{(l)}(t)}{l!} (-hu)^l\mathrm{d}u=\frac{f^{(l)}(t)(-h)^l}{l!}\int_{\mathbb{R}} k(u)u^l\mathrm{d}u=0.$$ Finally, the $l=m$ term is $$ \frac{f^{(m)}(t)(-h)^m}{m!}\int_{\mathbb{R}} k(u)u^m\mathrm{d}u<\infty.$$ From the definition of $o(g(y))$ given above, $o((hu)^m)$ denotes a function (the remainder) of $h$ and $u$ that for small $hu$, i.e. $hu\to 0$, approaches $0$ faster than $(hu)^m$. The remainder appears under the integral sign of an improper integral, which is the limit of a definite integral. For finite $u$, $hu\to 0$ means $h\to 0$. Thus the remainder is not only in $o((hu)^m)$ but also non-uniformly in $o(h^m)$, that is, for the remainder it holds that $o((hu)^m)=u^mo(h^m)=o(h^m)$.
Question
In the answer to the above linked question it is claimed, with slight modification, that \begin{equation} \int_\mathbb{R} k(u) o((hu)^m)\mathrm{d}u = \int_\mathbb{R} k(u) o(h^m)\mathrm{d}u =o(h^m)\int_\mathbb{R} k(u) u^m\mathrm{d}u =o(h^m) \tag{4}, \end{equation} but if the remainder is non-uniformly in $o(h^m)$, then the last two equalities in $(4)$ may not hold. The following example shows how a similar reasoning may fail.
For each positive $a$ and $x$ near $0$, \begin{equation} g(x,a)=\frac{x^2}{a^2+x^2}\in o\!\left(x^{3/2}\right). %\ \text{for} \ x \ \text{near} \ 0. \end{equation} Define \begin{equation} f(x)=\int_0^1g(x,a)\,\mathrm{d}a. \end{equation} Is $f(x)\in o\!\left(x^{3/2}\right)$? It is tempting to reason as in $(4)$; \begin{equation} \int_0^1g(x,a)\,\mathrm{d}a=\int_0^1o\!\left(x^{3/2}\right)\mathrm{d}a=o\!\left(x^{3/2}\right). \end{equation} However, $\lim_{x\to0}f(x)/x=\pi/2$, which means that $f(x)\not\in o\!\left(x\right)\supseteq o\!\left(x^{3/2}\right)$.
So, for some $g(h,u)\in o(1)$1, $$\int_\mathbb{R} k(u) o((hu)^m)\mathrm{d}u= h^m \int_\mathbb{R} k(u) u^m g(h,u)\mathrm{d}u,$$ but without knowing how $g(h,u)$ behaves away from zero, it seems like no further estimates can be done. Is it possible to calculate the expectation of the KDE using little-o?
Footnotes:
- The notation $g(h,u)$ implies the notation $g(hu)$. Unlike $g(hu)$, $g(h,u)$ includes those functions in $o(1)$ where $h$ and $u$ not only appear as $hu$.
