Section 2.1.0 - Introductory Writeups
Section 2.1.0.1 - Basic Writeups
Section 2.1.0.1.1 - Part 1 - Katariya - Statistics Introduction
Section 2.1.0.1.2 - Part 2 - Wikipedia - Statistics
Section 2.1.0.1.3 - Part 3 - Wikibooks - Statistics
Section 2.1.0.1.4 - Part 4 - Wikipedia - Glossary of Probability and Statistics
Section 2.1.0.2 - Detailed Writeups
Section 2.1.0.2.1 - Part 1 - MIT - Statistics Cheatsheet
Section 2.1.0.2.2 - Part 2 - Keone Hon - Introduction to Statistics
Section 2.1.0.2.3 - Part 3 - David M. Lane - Introduction to Statistics
Section 2.1.0.2.4 - Part 4 - Joseph C. Watkins - An Introduction to the Science of Statistics
Section 2.1.0.2.5 - Part 5 - Prasanna Sahoo - Probability and Mathematical Statistics
Section 2.1.0.2.6 - Part 6 - Louisville - Probability and Mathematical Statistics
Section 2.1.0.2.7 - Part 7 - Louisville - Probability and Mathematical Statistics
Section 2.1.0.2.8 - Part 8 - Allen B. Downey - Think Stats I
Section 2.1.0.2.9 - Part 9 - Allen B. Downey - Think Stats 2nd Edition
Section 2.1.0.3 - Code
Section 2.1.0.3.1 - Part 1 - Rouse Guy - Intro2Stats
Section 2.1.0.3.2 - Part 2 - Sujit Pal - Think Stats Solutions(Check Allen Downey's book above)
Section 2.1.0.3.3 - Part 3 - Allen Downey - Think Stats 2 Code
Section 2.1.0.4 - Videos
Section 2.1.0.4.1 - Part 1 - MIT - Courseware on Statistics
Section 2.1.0.4.2 - Part 2 - Khan Academy - Statistics
Section 2.1.0.4.3 - Part 3 - Dr. Nic's - Series on Statistics
Section 2.1.1 - Inferential Statistics in Machine Learning
Section 2.1.1.0 - Basic Writeup - Katariya - Inferential Statistics in Machine Learning
Section 2.1.1.1 - Statistical Classification
Section 2.1.1.2 - Regression Analysis
Section 2.1.1.3 - Bayesian Statistics
Section 2.1.2 - Some Statistical Fundamentals
Section 2.1.2.0 - Basic Writeup - Katariya - Statistical Fundamentals
Section 2.1.2.1 - Mean
Section 2.1.2.2 - Median
Section 2.1.2.3 - Mode
Section 2.1.2.4 - Random(Stochastic) Variable
Section 2.1.2.5 - Univariate Random Variable
Section 2.1.2.6 - Multivariate Random Variable or Random Vector
Section 2.1.2.7 - Probability Mass Function - pmf- (Probability for Discrete Random)
Section 2.1.2.8 - Probablity Density Function - pdf- (Limit Integrated for Continuous Random)
Section 2.1.2.9 - Cummulative Distribution Function (CDF)
Section 2.1.2.10 - Skewness
Section 2.1.2.11 - Kutosis
Section 2.1.2.12 - Entropy
Section 2.1.2.13 - Moment Generating Function - MGF
Section 2.1.2.14 - Characteristic Function
Section 2.1.2.15 - Probability Generating Function - PGF
Section 2.1.2.16 - Fisher Information
Section 2.1.2.17 - Expected Value
Section 2.1.2.18 - Deviation
Section 2.1.2.19 - Variance
Section 2.1.2.20 - Covariance
Section 2.1.2.21 - Standard Deviation
Section 2.1.2.22 - Average absolute deviation
Section 2.1.2.23 - Bootstrapping
Section 2.1.2.23.0 - Basic Writeup - Wikipedia - Bootstrapping (Statistics)
Section 2.1.2.23.1 - Case Resampling
Section 2.1.2.23.2 - Bayesian bootstrap
Section 2.1.2.23.3 - Smooth bootstrap
Section 2.1.2.23.4 - Parametric bootstrap
Section 2.1.2.23.5 - Resampling residuals
Section 2.1.2.23.6 - Gaussian process regression bootstrap
Section 2.1.2.23.7 - Wild bootstrap
Section 2.1.2.23.8 - Block bootstrap
Section 2.1.2.24 - Statistical Sampling
Section 2.1.2.24.0 - Basic Writeup - Wikipedia - Sampling (Statistics)
Section 2.1.2.24.1 - Sampling Frame
Section 2.1.2.24.1.0 - Basic Writeup - Wikipedia - Sampling Frame
Section 2.1.2.24.1.1 - Probability Sampling
Section 2.1.2.24.1.2 - Non-Probability Sampling
Section 2.1.2.24.2 - Sampling Methods
Section 2.1.2.24.2.0 - Basic Writeup - Wikipedia - Sampling Methods
Section 2.1.2.24.2.1 - Simple Random Sampling
Section 2.1.2.24.2.2 - Systematic Sampling
Section 2.1.2.24.2.3 - Stratified Sampling
Section 2.1.2.24.2.4 - Probability-proportional-to-size sampling
Section 2.1.2.24.2.5 - Cluster Sampling
Section 2.1.2.24.2.6 - Quota Sampling
Section 2.1.2.24.2.7 - Minimax Sampling
Section 2.1.2.24.2.8 - Accidental Sampling
Section 2.1.2.24.2.9 - Voluntary Sampling
Section 2.1.2.24.2.10 - Line-intercept Sampling
Section 2.1.2.24.2.11 - Panel Sampling
Section 2.1.2.24.2.12 - Snowball Sampling
Section 2.1.2.24.2.13 - Theoretical Sampling
Section 2.1.3 - Types of Statistical Models
Section 2.1.3.0 - Basic Writeup - Katariya - Types of Statistical Models
Section 2.1.3.1 - Discriminative Statistical Modeling
Section 2.1.3.2 - Generative Statistical Modeling
Section 2.1.3.3 - Probabalistic Graphical Statistical Modeling
Section 2.1.4 - Estimators and Estimation Theory
Section 2.1.4.0 - Basic Writeup - Katariya - Estimators and Estimation Theory
Section 2.1.4.1 - Bounding Variance or Divergence in Estimation
Section 2.1.4.1.0 - Basic Writeup - Paniniski - Estimation Theory
Section 2.1.4.1.1 - Cramér–Rao bound
Section 2.1.4.1.2 - Chapman–Robbins bound
Section 2.1.4.1.3 - Kullback's inequality
Section 2.1.4.1.4 - Brascamp–Lieb inequality
Section 2.1.4.2 - Regression Analysis Techniques Based Estimation
Section 2.1.4.2.0 - Basic Writeup - Katariya - Regression Analysis Techniques
Section 2.1.4.2.1 - Least Squares
Section 2.1.4.2.1.0 - Basic Writeup - Wikipedia - Least Squares
Section 2.1.4.2.1.1 - Linear Least Squares Estimators
Section 2.1.4.2.1.1.0 - Basic Writeup - Wikipedia - Linear Least Squares
Section 2.1.4.2.1.1.1 - Ordinary Least Squares Estimator
Section 2.1.4.2.1.1.2 - Weighted Least Squares Estimator
Section 2.1.4.2.1.1.3 - Generalized Least Squares Estimator
Section 2.1.4.2.1.1.4 - Iteratively Weighted Least Squares Estimator
Section 2.1.4.2.1.1.5 - Instrumental Variables Regression
Section 2.1.4.2.1.1.6 - Total Least Squares
Section 2.1.4.2.1.2 - Non-Linear Least Squares Estimators
Section 2.1.4.2.1.3 - Partial Least Squares Estimators
Section 2.1.4.2.1.4 - Non-Negative Least Squares Estimators
Section 2.1.4.2.1.5 - Regularized Least Squares Estimators
Section 2.1.4.2.1.6 - Polynomial Least Squares Estimators
Section 2.1.4.2.2 - Least Absolute Deviation (LAD) Regression
Section 2.1.4.2.3 - Lasso (least absolute shrinkage and selection operator) Regression
Section 2.1.4.2.4 - Bayesian Linear Regression
Section 2.1.4.2.5 - Bayesian Multivariate Linear Regression
Section 2.1.4.2.6 - Quantile Regression
Section 2.1.4.3 - Bayesian Statistical Techniques Based Estimation
Section 2.1.4.3.1 - Bayesian Linear Regression
Section 2.1.4.3.2 - Bayesian Multivariate Linear Regression
Section 2.1.4.3.3 - Bayes Estimators
Section 2.1.4.3.4 - Maximum a posteriori (MAP)
Section 2.1.4.3.5 - Markov Chain Monte Carlo (MCMC)
Section 2.1.4.3.5.0 - Introductory Writeups
Section 2.1.4.3.5.0.1 - Basic Writeup - Katariya - Markov Chain Monte Carlo
Section 2.1.4.3.5.0.2 - Detailed Writeup - Wikipedia - Markov Chain Monte Carlo
Section 2.1.4.3.5.1 - Markov Chain Monte Carlo with Examples
Section 2.1.4.4 - Minimum Mean Square Error Techniques Based Estimation
Section 2.1.4.4.0 - Basic Writeup - Wikipedia - Minimum Mean Square Error
Section 2.1.4.4.1 - Linear MMSE
Section 2.1.4.4.2 - Sequential Linear MMSE
Section 2.1.4.4.3 - Kalman Filter
Section 2.1.4.4.4 - Weiner Filter
Section 2.1.4.5 - Maximum Likelihood Estimators
Section 2.1.4.5.0 - Introductory Writeups
Section 2.1.4.5.0.1 - Basic Writeup - Katariya - Maximun Likelihood Estimators
Section 2.1.4.5.0.2 - Detailed Writeup - Wikipdeia - Maximum Likelihood Estimation
Section 2.1.4.6 - Adaptive Estimation
Section 2.1.4.7 - Method of Moments Estimators
Section 2.1.4.8 - The Theil-Sen Estimator
Section 2.1.4.9 - Prediction Error Method
Section 2.1.4.10 - Minimum Variance Unbiased Estimator (MVUE)
Section 2.1.4.11 - Best Linear Unbiased Estimator (BLLUE) - Gauss Markov Theorem
Section 2.1.4.12 - Estimator Bias
Section 2.1.4.12.0 - Estimator Bias
Section 2.1.4.12.1 - Median-unbiased Estimators
Section 2.1.4.12.2 - Bayesian View
Section 2.1.4.13 - Spectral Density Estimation
Section 2.1.4.13.0 - Introductory Writeups
Section 2.1.4.13.0.1 - Basic Writeup - Katariya - Spectral Density Estimations
Section 2.1.4.13.0.2 - Detailed Writeup - Wikipedia - Spectral Density Estimation
Section 2.1.4.13.1 - Spectral Density Estimation Techniques
Section 2.1.4.13.1.0 - Basic Writeup - Wikipedia - Spectal Density Estimation Techniques
Section 2.1.4.13.1.1 - Non-Parametric Spectral Density Estimation Techinques
Section 2.1.4.13.1.1.0 - Basic Writeup - TAMU - Nonparametric Spectal Density Estimation
Section 2.1.4.13.1.1.1 - Periodogram
Section 2.1.4.13.1.1.2 - Bartlett's method
Section 2.1.4.13.1.1.3 - Welch's method
Section 2.1.4.13.1.1.4 - Multitaper
Section 2.1.4.13.1.1.5 - Least Squares Spectral Analysis
Section 2.1.4.13.1.1.6 - Non-uniform discrete Fourier transform
Section 2.1.4.13.1.1.7 - Singular Spectrum Analysis
Section 2.1.4.13.1.1.8 - Short-time Fourier transform
Section 2.1.4.13.1.1.9 - Critical filter
Section 2.1.4.13.1.2 - Parametric Spectral Density Estimation Techniques
Section 2.1.4.13.1.2.0 - Basic Writeup - Wikipedia - Parametric Spectral Density Estimation Techniques
Section 2.1.4.13.1.2.1 - Autoregressive model (AR) estimation
Section 2.1.4.13.1.2.2 - Moving-average model (MA) estimation
Section 2.1.4.13.1.2.3 - Autoregressive moving average (ARMA) estimation
Section 2.1.4.13.1.2.4 - MUltiple SIgnal Classification (MUSIC)
Section 2.1.4.13.1.2.5 - Maximum entropy spectral estimation
Section 2.1.5 - Statistical Hypothesis Testing
Section 2.1.5.0 - Basic Writeup - Wikipedia - Statistical Hypothesis Testing
Section 2.1.5.1 - Z-Test (Normal)
Section 2.1.5.2 - Students t-test
Section 2.1.5.3 - F-Test
Section 2.1.5.4 - Goodness of Fit
Section 2.1.5.4.0 - Basic Writeup - Wikipedia - Goodness Of Fit
Section 2.1.5.4.1 - Chi-squared
Section 2.1.5.4.2 - G-Test
Section 2.1.5.4.3 - Kolmogorov–Smirnov
Section 2.1.5.4.4 - Anderson–Darling
Section 2.1.5.4.5 - Lilliefors
Section 2.1.5.4.6 - Jarque–Bera
Section 2.1.5.4.7 - Normality (Shapiro–Wilk)
Section 2.1.5.4.8 - Likelihood Ratio Test
Section 2.1.5.4.9 - Modal Selection
Section 2.1.5.4.10 - Cross Validation
Section 2.1.5.4.11 - Cross Validation
Section 2.1.5.4.12 - Model Selection Criteria
Section 2.1.5.4.12.0 Basic Writeup - Wikipedia - Model Selection
Section 2.1.5.4.12.1 Akaike information criterion
Section 2.1.5.4.12.2 Bayesian information criterion
Section 2.1.5.5 - Rank Statistics
Section 2.1.5.5.0 - Basic Writeup - Wikipedia - Rank Statistics
Section 2.1.5.5.1 - Sign Test
Section 2.1.5.5.2 - Signed Rank (Wilcox)
Section 2.1.5.5.3 - Rank Sum (Mann-Whitney)
Section 2.1.5.5.4 - Non-Parametric ANOVA (1-way Kruskal-Wallis)
Section 2.1.5.5.5 - 2-way (Friedman)
Section 2.1.5.5.6 - Ordered alternative (Jonckheere–Terpstra)
Section 2.1.6 - Statistical Modeling
Section 2.1.6.0 - Basic Writeup - Wikipedia - Statistical Modeling
Section 2.1.6.1 - Regression Analysis and Modeling
Section 2.1.6.1.0 - Basic Writeup - Wikipedia - Regression Analysis
Section 2.1.6.1.1 - Linear Regression
Section 2.1.6.1.2 - Simple and Multiple Linear Regression
Section 2.1.6.1.3 - Polynomial Regression
Section 2.1.6.1.4 - General linear or Multivariate model
Section 2.1.6.1.5 - Generalized Linear Model
Section 2.1.6.1.6 - Discrete choice
Section 2.1.6.1.7 - Logistic regression
Section 2.1.6.1.8 - Multinomial logistic regression
Section 2.1.6.1.9 - Mixed logit
Section 2.1.6.1.10 - Probit Model
Section 2.1.6.1.11 - Multinomial Probit Model
Section 2.1.6.1.12 - Ordered Logit
Section 2.1.6.1.13 - Ordered Probit
Section 2.1.6.1.14 - Poisson Regression
Section 2.1.6.1.15 - Ordinal Regression
Section 2.1.6.1.16 Multilevel model
Section 2.1.6.1.17 - Fixed Effects Model
Section 2.1.6.1.18 - Random Effects Model
Section 2.1.6.1.19 - Mixed Model
Section 2.1.6.1.20 - Nonlinear Regression
Section 2.1.6.1.21 - Nonparametric Regression
Section 2.1.6.1.22 - Semi parametric Regression
Section 2.1.6.1.23 - Robust Regression
Section 2.1.6.1.24 - Quantile Regression
Section 2.1.6.1.25 - Isotonic Regression
Section 2.1.6.1.26 - Principal Components Regression
Section 2.1.6.1.27 - Least Angle Regression
Section 2.1.6.1.28 - Local Regression
Section 2.1.6.1.29 - Segmented Regression
Section 2.1.6.1.30 - Errors In Variables Model
Section 2.1.6.2 - System Indentification
Section 2.1.6.2.0 - Basic Writeup - Wikipedia - System Identification
Section 2.1.6.2.1 - Nonlinear System Identification
Section 2.1.6.2.1.0 - Basic Writeup - Wikipedia - NonLinear System Identification
Section 2.1.6.2.1.1 - Volterra Series Models
Section 2.1.6.2.1.2 - Block Structured Systems
Section 2.1.6.2.1.3 - Neural Network Systems
Section 2.1.6.2.1.4 - NARMAX (Non-Linear Autoregressive Moving Average with Exogenous Inputs) Models
Section 2.1.6.2.1.5 - State-space models (Stochastic Non Linear Models)
Section 2.1.6.3 - Filtering Problem(Stochastic Processes)
Section 2.1.6.3.0 - Basic Writeup - Wikipedia - Filtering Problem (Stochastic Processes)
Section 2.1.6.3.1 - Linear Filters
Section 2.1.6.3.1.0 - Basic Writeup - Wikipedia - Linear Filter
Section 2.1.6.3.1.1 - Kalman Filter
Section 2.1.6.3.1.2 - Weiner Filter
Section 2.6.3.2 - Non-Linear Filters
Section 2.1.6.3.2.0 - Basic Writeup - Wikipedia - Nonlinear Filter
Section 2.1.6.3.2.1 - Extended Kalman Filter
Section 2.1.6.3.3 - Particle Filter
Section 2.1.7 - Statistical Algorithms
Section 2.1.7.0 - Basic Writeup - Wikipedia - Iterative Method
Section 2.1.7.1 - Expectation Maximization Algorithm
Section 2.1.7.1.0 - Basic Writeup - Wikipedia - Expectation Maximimization Algorithm
Section 2.1.7.1.1 - α-EM algorithm
Section 2.1.7.1.2 - Parameter-expanded expectation maximization (PX-EM)
Section 2.1.7.1.3 - Expectation conditional maximization (ECM)
Section 2.1.7.1.4 - Generalized expectation maximization (GEM)
Section 2.1.7.2 - Minorize-Maximization (MM)
