thomvolker

mice.impute.pmm() can give counterintuitive results when using type 1 matching, see for example the following scenario.

The following code is provided by Stef van Buuren, inspired by Templ 2023, Visualization and Imputation of Missing Values.

library(mice)
#> 
#> Attaching package: 'mice'
#> The following object is masked from 'package:stats':
#> 
#>     filter

thomvolker

Finding ellipsoidal probabilities under the multivariate normal model

Thom Benjamin Volker

Introduction

Many statistical models impose multivariate normality, for example, for example on the regression parameters in a linear regression model. A question that may arise is how to find the probability that a random

thomvolker

Density ratio estimation through Bregman divergence optimization

Thom Benjamin Volker

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The density ratio estimation problem is to estimate the ratio of two probability density functions $p(x)$ and $q(x)$ from samples $\{nu_i\}_{i=1}^n$ and $\{de_i\}_{i=1}^m$ drawn from $p_{nu}(x)$ and $p_{de}(x)$, respectively. The density ratio estimation problem is important in many machine learning applications, such as domain adaptation, covariate shift,

thomvolker

An iterative simple linear regression procedure

Regression is known to estimate regression coefficients conditional on the other coefficients in the model. One way to show this is using the following script, which estimates coefficients in an iterative manner. Specifically, we perform regression on the residuals that are based on all other variables that are estimated previously. To do this, we initialize a response vector $r_\text{old}$ that equals the observed outcome variable, and we initialize a vector of regression coefficients $b^{(0)}$ to a zero-vector of length $P$. Then, starting with the first variable $X_1$, we update the regression coefficient by regressing $r_\text{old}$ on $X_1$, and defining the regression coefficient as $b_1^{(t)} = b_1^{(t-1)} + \sigma_{X_1, r_\text{old}}$ and we update the residual $r_\text{new}$ as $r\text{old} - X_1 * b_1^{(t)}$. For the next variable, we apply the same

thomvolker

Spectral density ratio estimation

Izbicki, Lee & Schafer (2014) show that the density ratio can be computed through a eigen decomposition of the kernel gram matrix. However, for a data set of size $n$, the kernel Gram matrix takes on dimensions $n \times n$ and an eigen decomposition has complexity $\mathcal{O}(n^3)$. Fortunately, it is possible to approximate the solution with a subset of the kernel Gram matrix. That is, we can perform an eigen decomposition of a subset of $k \geq n$ rows and columns of the kernel Gram matrix to approximate the original solution.

library(ggplot2)

thomvolker

Where should the Gaussian centers come from in density ratio estimation?

Sugiyama, Suzuki & Kanamori (2012) advise to use (a subset from) the numerator samples in density ratio estimation. However, this advise might very much depend on the use-case. Namely, the density ratio can be estimated accurately in some region only if there are centers located in that region. In the figures below, we have two datasets sampled from two functions with different support. If the centers are chosen from the population with smallest support, the regularization seems to have a stronger effect.

thomvolker

What is the expected confidence interval overlap under a correct synthesis model?

ci_overlap <- function(obs_l, obs_u, syn_l, syn_u) {
  obs_ol <- (min(obs_u, syn_u) - max(obs_l, syn_l)) / (obs_u - obs_l)
  syn_ol <- (min(obs_u, syn_u) - max(obs_l, syn_l)) / (syn_u - syn_l)
  (obs_ol + syn_ol) / 2
}

set.seed(123)

thomvolker

Density ratio estimation with and without intercept

When performing density ratio estimation, the regularization parameter $\lambda$ causes the estimated kernel weights to be shrunken towards zero. However, a ratio is least complex when it is one, rather than zero. Hence, the regularization yields a bias that implies more mass in the denominator samples (compared) to the numerator samples. By adding an intercept, we can mitigate the bias, but not completely remove it. This is the case because the intercept is also regularized, and hence shrunken towards zero.

thomvolker

# binomial differential privacy

obs <- rbinom(100, 1, 0.5)
mean(obs)
#> [1] 0.51

rlaplace <- function(n, sensitivity, epsilon) {
  u <- runif(n, -0.5, 0.5)
  b <- sensitivity / epsilon

thomvolker

Singular value decomposition

Thom Benjamin Volker

The singular value decomposition of a $n \times p$ matrix $\boldsymbol{X}$ is a factorization of the form $$\boldsymbol{X} = \boldsymbol{U} \boldsymbol{\Sigma} \boldsymbol{V^\top},$$ where $\boldsymbol{U}$ is a $n \times p$ semi-orthogonal matrix with the left singular vectors, $\boldsymbol{\Sigma}$ is a $p \times p$ diagonal matrix with non-negative real numbers on the diagonal such that $\sigma_{1,1} \geq \sigma_{2,2} \geq \dots \geq \sigma_{p,p} \geq 0$