Latent Variable Model with R: FA, EFA, CFA; SEM

Mô hình biến tiềm ẩn với R: FA, EFA, CFA; SEM

 

  • Alex Beaujean

Factor Analysis using R [EFA+CFA]


Chapter 1: Example and computing

Chapter 2: Factor Analysis

Chapter 3: Multigroup Factor Analysis

Chapter 4: Structural Equation Models


  • Joel Cadwell [bifactor model]

Network Visualization of Key Driver Analysis

Halo Effects and Multicollinearity: Separating the General from the Specific

Structural Equation Modeling: Separating the General from the Specific (Part II)


  • lavaan Tutorial [path analysis, confirmatory factor analysis, structural equation modeling and growth curve models]

http://lavaan.ugent.be


  • psych Tutorial [factor analysis, PCA, cluster analysis and reliability analysis; Item Response Theory; front end for SEM; Graphical displays]

http://personality-project.org/r/psych/

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Machine Learning with R

Phương pháp tốt nhất để học Machine Learning là thiết kế và hoàn thiện những dự án nhỏ

Học máy với R

Thuật toán:

  1. Linear Discriminant Analysis (LDA)
  2. Classification and Regression Trees (CART).
  3. k-Nearest Neighbors (kNN).
  4. Support Vector Machines (SVM) with a linear kernel.
  5. Random Forest (RF)

Thuật toán: Random Forest


Thuật toán:

  1. Logistic Regression
  2. Recursive partitioning for classification (Basic and Bayesian)
  3. Random Forest
  4. Conditional Inference Tree
  5. Bayesian Networks
  6. Unbiased Non-parametric methods- Model Based (Logistic)
  7. Support Vector Machine
  8. Neural Network
  9. Lasso Regression

Mô hình: Artificial Neural Network (ANN): Feed-forward neural network


Mô hình:

  1. Feedforward neural network (Use Case: MNIST Digit Classification)
  2. Deep Autoencoders (Use Case: Anomaly Detection)


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Học thống kê/kinh tế lượng trên Youtube

LEARNING STATISTICS ON YOUTUBE

(Nguồn: http://flavioazevedo.com)

1. LEARNING STATISTICS WITH R

Youtube channel Content Software Online Materials? Remarks
Mike Marin [Intro] Basic Stats in R R Yes, good materials University British Columbia
Michael Butler [Intro] to R and Stats, Modern R Yes Good intro to R + Exercises
EZLearn [Intro] Basic Stats in R R Exercises w/ solutions
Renegade Thinking: Courtney Brown [Intro] Undergraduate Stats R Yes Good Lectures
Barton Poulson [Intro] Classical Stats, Programming & Solved Exercises R, Python, SPSS Yes Gives intro to Python, R, SPSS and launching an OLP
Ed Boone [Intro] Basic R and SAS R & SAS Yes
Lynda.com [Intro] Basics of R and Descriptives R Yes OLP
Bryan Craven [Intro] Basic Stats in R R No
Laura Suttle (Revolution) [Intro] R tour for Beginners R No
Phil Chan [Intro] Classical and Bio-stats R, SPSS, Eviews No
Gordon Anthony Davis [Intro] R Programming Tutorial R No Thorough intro for beginners
David Langer [Intro] Basics of R R No Excellent pedagogical Skills
MrClean1796 [Intro] Math, Physics and Statistics, lecture & R R No
Brian Caffo Advanced & Bio-Stats, Reproducible Research R Yes, CourseraandGitHub Professor of Bio-statistics, Johns Hopkins Univ.
James Scott Advanced Stats R Yes, and GitHub Several Course Materials on GitHub
Derek Kane Machine Learning R Yes Excellent Videos, Fourier Analysis, Time series Forecasting
DataCamp Programming, DataViz, R Markdown [free] R Yes, paid. 9$ for students
Maria Nattestad DataViz R Personal Website Plotting in R for Biologists
Christoph Scherber Mixed, GLM, GLS, Contrasts R Yes
Librarian Womack Time Series, DataViz, BigData R Yes, Course and .R Materials online
Jarad Niemi R Workflow, Bayesian, Statistical Inference R Yes
Justin Esarey Bayesian, Categorical and Longitudinal Data, Machine Learning R Yes, lots and lots Political Scientist
Jeromy Anglim Research Methods R Blog:Psych & Stats,GitHub + Rmeetups and Notes on Gelman, Carlin, Stern, and Rubin
Erin Buchanan Under- & post-graduate Stats, SEM R, G*Power, Excel Yes Excellent pedagogical strategies
Richard McElreath From Basic to Advanced Bayesian Stats R and Stan Yes, lots Book lectures
edureka Data Science R, Hadoop, Python Yes, online learning plattaform R Intro w/ Hadoop [free]
Learn R R programming, stats on webiste R, Python Yes, andOne R Tip A Day On website, lots of starter’s code
Data School Machine Learning, Data Manipulation (dplyr) Python, R Yes, dplyr
Econometrics Academy Statistics (via Econometrics) R, STATA, SPSS Yes OLP, Excellent Materials and Resources
Jalayer Academy Basic Stats + Machine Learning R, Excel No Also Lectures
Michael Levy Authoring from R, Markdown, Shiny R No
Melvin L. Machine Learning, R Programming, PCA, DataViz R, Python, Gephi No Interesting Intro for Spark
OpenIntroOrg Intro to Stats/R plus Inference, Linear Models, Bayesian R Yes,CourseraandOpenIntro Coursera Courses, Resources in SAS

2. LEARNING STATISTICS WITH OTHER SOFTWARE

Youtube channel Content Software Online Materials? Remarks
Jonathan Tuke Basic Stats Matlab No
Saiful Yusoff PLS, Intro to MaxQDA SmartPLS, MaxQDA Yes BYU
James Gaskin SEM, PLS, Cluster SPSS, AMOS, SmartPLS Yes BYU
Quantitative Specialists Basic Stats SPSS No Upbeat videos
RStatsInstitute Basic Stats SPSS No Instructor at Udemy
how2stats Basic Stats, lecture and software demonstrations SPSS Yes Complete Classical Stats
BrunelASK Basic Stats SPSS
The Doctoral Journey Basic Stats SPSS Yes
StatisticsLectures Basic Stats, lecture format SPSS Yes discontinued, but thorough basic stats
Andy Field Classical Stats, lecture and software demonstrations SPSS Yes, registration needed Used heavely in Social Sciences
Quinnipiac University:Biostatistics Classical Stats SPSS No
The RMUoHP Biostatistics Basic and Bio-Stats SPSS, Excel No
PUB708 Team Classical Statistics SPSS, MiniTab No
Professor Ami Gates Classical Stats SPSS, Excel, StatCrunch Yes
H. Michael Crowson Intro and Basic Stats in several Softare SPSS, STATA, AMOS, LISREAL Yes?
Math Guy Zero Classical Stats + SEM SPSS, Excel, PLS No Lots of materials
BayesianNetworks Bayesian Statistics, SEM, Causality BayesianLab Yes
Khan Academy Programming 101 Python Yes
Mike’s SAS Short intro to SAS, SPSS SAS, SPSS No
Christian A. Wandeler Basic Stats PSPP No

3. LECTURES ON STATISTICS

Youtube channel Content Software Online Materials? Remarks
Stomp On Step 1 [Intro] Bio-Stats, Basic Lectures Yes USMLE
Khan Academy [Intro] Basic Stats, lecture format Lectures Yes
Joseph Nystrom [Intro] Basic Stats Lectures Yes Active & unorthodox teaching
Statistics Learning Centre [Intro] Basic Stats Lectures Yes Register to access materials
Brandon Foltz [Intro] Basic Stats Lectures soon Excellent visuals
David Waldo [Intro] Probability Theory Lectures No
Andrew Jahn [Intro] Basic Stats Lectures No FSL, AFNI and SPM [Neuro-immaging]
Professor Leonard [Intro] Stats and Maths Lectures No Excellent pedagogical skills
ProfessorSerna [Intro] Basic Stats Lectures No
Victor Lavrenko Machine Learning, Probabilistic, Cluster, PCA, Mixture Models Lectures Yes, very complete Excellent Content, and lots of it
Jeremy Balka’s Statistics Graduate-level Classical Stats, Lecture Lectures Yes, very thorough Excellent altogether, p-value vid great!
Methods Manchester Uni Discussion on a wide variety of methods, SEM Lectures Yes Methods Fair
Steve Grambow Series on Inference Lectures Yes Great Lectures on Inference [DUKE]
Statistics Corner: Terry Shaneyfelt Statistical Inference Lectures Yes from a clinical perspective
Michel van Biezen Complete Course of Stats Lectures Yes, 1, 2, 3 Thorough and complete, plus Physics and Maths
Oxford Education Bayesian statistics: a comprehensive course Lectures Yes
Nando de Freitas Machine Learning Lectures Yes, alsohere and here
Alex Smola Machine Learning Lectures Yes, slides and code
Abu (Abulhair) Saparov Machine Learning Lectures Yes Taught by Tom Mitchell and Maria-Florina Balcan
Geoff Gordon Machine Learning, Optimization Lectures Yes
MIT OpenCourseWare Probability Theory,Stochastic Processes Lectures Yes, here,and here
Alexander Ihler Machine Learning Lectures Yes, along w/ many others classes
Royal Statistical Society Important Statistical issues Lectures Yes Interesting topics
Ben Lambert Graduate and Advanced Stats Lectures No Asymptotic Behaviour of Estimators, SEM, EFA
DeepLearning TV Machine (and Deep) Learning Lectures No Excellent pedagogical skills
Mathematical Monk Machine Learning, and Probability Theory Lectures No

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Time Series Analysis with R: ARMA, ARIMA, ARFIMA, ARCH, GARCH, VAR Models

Phân tích chuỗi thời gian với R:  Mô hình ARMA, ARIMA, ARFIMA (FARIMA), ARCH, GARCH, VAR

  • OTexts: Forecasting: principles and practice

ARIMA models

Dynamic regression models

Vector autoregressions (VAR)



  • Analytics Vidhya

A Complete Tutorial on Time Series Modeling in R



  • Avril Coghlan: Using R for Time Series Analysis

ARIMA Models



  • Quintuitive

ARMA Models for Trading



  • Milind Paradkar (Quantinsti)

Forecasting Stock Returns using ARIMA model



  • Portfolio Probe

A practical introduction to garch modeling (+EGARCH)



  • I. Ozkan: ARCH-GARCH Example with R

ARCH-GARCH Example with BIST, Oil and TL/USD Series



  • Alexios Ghalanos 

Introduction to the rugarch package (ARFIMAX, GARCH, NGARCH, NAGARCH, IGARCH, EGARCH, GJR-GARCH, TGARCH, AVGARCH, APARCH, fGARCH, …)

The rmgarch models: Background and properties (Multivariate GARCH: DCC-GARCH, GO-GARCH, Copula-GARCH)



  • Bernhard Pfaff

VAR, SVAR and SVEC Models: Implementation Within R Package vars (+Cointegration)



  • QuantStart: Time Series Analysis
  1. Beginner’s Guide to Time Series Analysis
  2. Serial Correlation in Time Series Analysis
  3. White Noise and Random Walks in Time Series Analysis
  4. Autoregressive Moving Average ARMA(p, q) Models for Time Series Analysis – Part 1
  5. Autoregressive Moving Average ARMA(p, q) Models for Time Series Analysis – Part 2
  6. Autoregressive Moving Average ARMA(p, q) Models for Time Series Analysis – Part 3
  7. Autoregressive Integrated Moving Average ARIMA(p, d, q) Models for Time Series Analysis
  8. Generalised Autoregressive Conditional Heteroskedasticity GARCH(p, q) Models for Time Series Analysis
  9. ARIMA+GARCH Trading Strategy on the S&P500 Stock Market Index Using R
  10. State Space Models and the Kalman Filter
  11. Cointegrated Time Series Analysis for Mean Reversion Trading with R (Cointegration)

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(Generalized) Linear Models and Mixed Models with R

Mô hình hồi quy tuyến tính & Mô hình hỗn hợp (tổng quát hóa) với R

This is a decidedly conceptual introduction to the linear model framework and linear mixed effects models in R. It is intended to be very basic.

Tutorial 1

Tutorial 2





Linear Mixed effect Models are becoming a common statistical tool for analyzing data with a multilevel structure. I will start by introducing the concept of multilevel modeling where we will see that such models are a compromise between two extreme: complete pooling and no pooling



Mixed effects logistic regression is used to model binary outcome variables, in which the log odds of the outcomes are modeled as a linear combination of the predictor variables when data are clustered or there are both fixed and random effects.



The purpose of these notes is to provide a quick introduction to R, particularly as a tool for fitting linear and generalized linear models.

1. Introduction

2. Getting Started

3. Reading and Examining Data

4. Linear Models

5. Generalized Linear Models

6. Conclusion



The goal of this course is to give you the skills to do the statistics that are in current
published papers, and make pretty figures to show off your results. While we will go over the mathematical concepts behind the statistics, this is NOT meant to be a classical statistics class. We will focus more on making the connection between the mathematical equation and the R code, and what types of variables fit into each slot of the equation.

Lesson 0 – Introduction and Set-up

Lesson 1 – R Basics

Lesson 2 – Linear Regression

Lesson 3 – Logistic Regression

Lesson 4 – Multiple Regression

Lesson 5 – Analysis of Variance (ANOVA)

Lesson 6, Part 1 – Linear Mixed Effects Models (Generalized linear mixed model)

Lesson 6, Part 2 – Linear Mixed Effects Models (Generalized linear mixed model)

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MIT-Topics in Mathematics with Applications in Finance

MIT-Topics in Mathematics with Applications in Finance

A flow diagram of a pricing model.

Description

The purpose of the class is to expose undergraduate and graduate students to the mathematical concepts and techniques used in the financial industry. The course will consist of a set of mathematics lectures on topics in Linear Algebra, Probability, Statistics, Stochastic Processes and Numerical Methods. Mathematics lectures will be mixed with lectures illustrating the corresponding application in the financial industry.

MIT mathematicians will teach the mathematics part while industry professionals will give the lectures on applications in finance. We also plan to organize an optional field trip to visit Morgan Stanley offices in New York.

Goals for the Class

  1. Be able to derive price-yield relationship and understand convexity.
  2. Bootstrap a yield curve.
  3. Compute standard Value At Risk and understand assumptions behind it.
  4. Estimate volatility of an option.
  5. Derive Black-Scholes equations using risk-neutral arguments.
  6. Understand decomposition of matrices in statistics (and probability) point of view, e.g. principle component analysis.
  7. Use statistical techniques and methods in data analysis; understand the advantages and limitations of different methods.
  8. Understand basic limiting theorems and assumptions behind them.
  9. Understand Ito’s lemma and it’s applications in financial mathematics.
  10. Understand Girsanov’s theorem and change of measure.

Link: http://ocw.mit.edu/courses/mathematics/18-s096-topics-in-mathematics-with-applications-in-finance-fall-2013/index.htm

Video Lectures

Lecture 1: Introduction, Financial Terms and Concepts

Lecture 2: Linear Algebra

Lecture 3: Probability Theory

Lecture 4: Matrix Primer

Lecture 5: Stochastic Processes I

Lecture 6: Regression Analysis

Lecture 7: Value At Risk (VAR) Models

Lecture 8: Time Series Analysis I

Lecture 9: Volatility Modeling

Lecture 10: Regularized Pricing and Risk Models

Lecture 11: Time Series Analysis II

Lecture 12: Time Series Analysis III

Lecture 13: Commodity Models

Lecture 14: Portfolio Theory

Lecture 15: Factor Modeling

Lecture 16: Portfolio Management

Lecture 17: Stochastic Processes II

Lecture 18: Itō Calculus

Lecture 19: Black-Scholes Formula, Risk-neutral Valuation

Lecture 20: Option Price and Probability Duality

Lecture 21: Stochastic Differential Equations

Lecture 22: Calculus of Variations and its Application in FX Execution

Lecture 23: Quanto Credit Hedging

Lecture 24: HJM Model for Interest Rates and Credit

Lecture 25: Ross Recovery Theorem

Lecture 26: Introduction to Counterparty Credit Risk

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Trang cung cấp các ví dụ ứng dụng của toán ungdungtoan.vn

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Trang chuyên đề này cung cấp những ví dụ ứng dụng trong thực tiễn của toán học, bao gồm:

1. Các thí dụ trong các chương trình trung học và đại học. Các thí dụ này không phải là các thí dụ gốc được sáng tác hoặc bản sao chép hay bản dịch các thí dụ có trong sách khác, chúng là các bài viết dựa trên các thí dụ trong các sách đã xuất bản. Mỗi bài đều có dẫn chứng nguồn trong các sách đó. Các giáo viên và giảng viên có thể sử dụng các thí dụ này trong bài giảng. Xin xem đây như là nguồn tài liệu mở cho việc giảng dạy và xin gởi đến chúng tôi các đóng góp về thí dụ toán trong thực tiễn khác. Các bạn có thể tìm các thí dụ mới trong nguồn sách mà chúng tôi để trên trang web “Thí dụ thực tiễn”. Chúng tôi cũng đề xuất một danh sách các thí dụ cần dịch trên trang web “Thí dụ thực tiển”.

2. Giới thiệu các ứng dụng toán trong các bài báo và công trình có ứng dụng thực tiễn trong và ngoài nước. Ở đây các tác giả và các nhóm tác giả có thể giới thiệu khả năng ứng dụng toán của bản thân, và các nhóm nghiên cứu. Các công ty và cơ quan nhà nước có thể tìm đúng địa chỉ để đặt hàng. Việc này sẽ đẩy mạnh đại học Việt Nam trở thành nơi tạo nguồn nhân lực để giải quyết các vấn đề thực tiễn của Việt Nam.

Trang chủ: http://www.ungdungtoan.vn

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