# Tag Info

7

What are the benefits of a unified framework? You are right that we are rapidly going into some much used special cases line logistic regression or Poisson regression, but there is still benefit in having a common framework. Technology transfer from the general linear model (not generalized!), that is with gaussian errors and identity link. A lot of what ...

5

Even if $X_n\to c$ in probability for some real constant $c$, it is not necessary that $P(Y\le X_n)\to P(Y\le c)$ -- you also need to require that $P(Y=c)=0$. More generally, if the limit of $X_n$ is not a constant, then you need to assume the convergence, not just of the distribution of $X_n$, but of the joint distribution of $X_n$ and $Y$. In particular, ...

4

For real $u_m>0$, the probability in question is $$p_m:=P(\|X\|^2\ge u_m|X_1|)=P\Big(\frac{|X_1|}{\|X\|^2/m}\le\frac m{u_m}\Big).$$ Passing to a subsequence, without loss of generality $$\frac{u_m}m\to c\in[0,\infty]$$ (as $m\to\infty$). By the law of large numbers, $\|X\|^2/m\to1$ in probability. So, by Slutsky's theorem , p_m\to p_\infty:=P(|X_1|\le1/... 4 There exists a variety of measures of uniformity of a point set. See, for example, On assessing spatial uniformity of particle distributions... for an overview, and a critical comparison when applied to real-world data. There are two distinct classes of uniformity measures: Quadrat-based measures divide the region into a number of small grids, called ... 3 Judging from your pictures, it should be sufficient to consider the root-mean-square distance \rho of the points \vec{x}_k from their center of mass \vec{\mu}, divided by the radius R of the circle: \begin{align} \vec{\mu} &= \frac{1}{N}\sum_{k=1}^N\vec{x}_k, \\ \rho &= \sqrt{\left(\frac{1}{N}\sum_{k=1}^N\left\lVert\vec{x}_k-\vec{\mu}\right\... 2 First, notice that w.l.o.g. you can assume that the matrix H_n is diagonal (from rotational invariance of the isotropic Gaussian). You thus are interested in \frac{1}{n} \sum_{i=1}^n \lambda_i(H_n) X_i^2. $$So the condition$$\sum_{i=1}^n \lambda_i^2(H_n)/n^2 \to 0$$is the key. Then, one has that$$ Var\left(\frac{1}{n} \sum_{i=1}^n \lambda_i(H_n) X_i^...

1

Using e.g. the Gauss elimination, we can diagonalize the matrix $(v_{kl})$, that is, write $$v_{kl}=\sum_{r=1}^d a_r s_{rk}t_{rl}$$ for some real $a_r,s_{rk},t_{rl}$ and all $k,l$; the computational complexity (CC) of this diagonalization is $O(d^3)$; cf. e.g. this source. Now we can write $$u_{ij}=\sum_r a_r\,Ex_ix_jX_rY_r,$$ where $$X_r:=\sum_k s_{rk}x_k,\... 1 The joint distribution of the eigenvalues \lambda_i, i=1,2,\ldots n of A is known,$$P(\lambda_1,\lambda_2,\ldots\lambda_n)=c_{k,n}\prod_{i<j}|\lambda_i-\lambda_j|\prod_m e^{-\lambda_m/2}\lambda_m^{(k-n-1)/2},$$with c_{k,n} a normalization constant. The desired expectation value is given by$$U_{n,k}=\mathbb E[\det(I+(I+A)^{-1})]=\int_0^\infty d\...

1

$\newcommand\si\sigma\newcommand\Om\Omega$To determine the limit distribution of the process $Z_N$ (and even the distribution of the process $X$), it is not enough to know only $\si^2$; one also has to know the covariances $r_{x,y}:=Cov(X(x),X(y))$ for $x,y$ in $\Om$. Then, using e.g. the joint moment generating function of $(X(x),X(y))$, one can see that ...

Only top voted, non community-wiki answers of a minimum length are eligible