Finite Population Sampling without Replacement

Personal note of finite population sampling

First moment

Population: $ \Omega = \{ x_1, \dots, x_N \} $
Collection of $n$-samples: $\mathcal{S} = \{ s \in \Omega^n \mid \forall i,j \in s, i \ne j \} $
Collection of $n$-samples containing $x$: $ \mathcal{S}_x = \{ s \in \mathcal{S} \mid x \in s \} $
Observe that $ |\mathcal{S}_x| = \binom{N-1}{n-1} $.
Let population mean be zero. $\mu = 0$, i.e. $ \sum_{i = 1}^N x_i = 0 $
Fix an order for $\mathcal{S}$: $ \mathcal{S} = \{ s_1, s_2, \dots, s_{|\mathcal{S}|} \} $.
$j$-th $n$-sample mean $ m_j = \frac1n \sum_{x \in \mathcal{S}_j} x $
Remark: I don’t use $ \sum s_j $ as in $ \cup \mathcal{T} $ in topology to avoid misreading the $n$-sample $ s_j $ as an element.
mean of $n$-sample mean $ m = \frac{1}{|\mathcal{S}|} \sum_{s_j \in \mathcal{S}} m_j $

[Read More]

What are Dataframes?

Understand dataframes from a non-example

Motivation

The books that I read in the past didn’t explain what a dataframe meant.

Definition

Dataframe
A table of data in which the values of each observed variable is contained in the same column.

Counterexample

I’ve difficulty in reading long lines of text like the above definition, so let’s illustrate this definition with a counterexample.

We have carried out repeated experiments with four types of things and obtaine some data. (Say, poured some liquid into an empty cup and take the temperature.)

[Read More]

2018-10-04 Seminar Notes

I jotted down only a few keywords that might be reusable. I didn’t understand any of the talks.

Functional Data Analysis

  • Goal: predict equipment temperature
  • Tools: Fourier coefficients (trigo ones), followed by discretisation, min-error estimation, cross-validation 10-folds, $R^2$ adjusted ?, MAE, MSPE
  • Comparison with non-functional data

Tolérancement

  • Thème : Traiter les incertitudes sur les dimensions des pièces de l’avion
  • Objectif :
    • établir une modélisation mathématiques
    • construire un virtual twin de l’avion
  • Outils :
    • Modèle de variabilité
    • Modèle d’assemblage $\text{airbus}: Y = \sum_{i = 1}^n a_iX_i?$
    • Notion de risque … calculs des coefficients de convolution

SVM

  • Multiclass vs structual, hidden Markov model
  • Plan for this year:
    • apply structual SVM for real SVM
    • apply structual SVM for deep neural network

Auxiliary information

  • auxiliary function given in one partition
  • auxiliary function given in mutiple partitions
  • bootstrap
  • law of iterated logarithms
  • Kullback–Leibler distance
  • convergence: Donsker class, var, covar
  • ranking ration method: convergence to Gaussian process, entropy conditions, Telegrandś inequality
    • weak convergence: KMT, Berthet-Maison
    • strong convergence: ?
      • consequences: Berry-Essen bound, bias & variance estimation of ranking ration method

Euler scheme SDE

I could only write “Toeplitz tape operator”.

[Read More]