Publications by Zhongming Jiang
Statistical Modeling XIII - The Formulation of Model-Ready Panel Data Frame (PDF) by Time-Series Imputation and Cross-Sectional Feature Engineering
knitr::opts_chunk$set(echo = TRUE) 1 Package Invoking library(dplyr) ## ## Attaching package: 'dplyr' ## The following objects are masked from 'package:stats': ## ## filter, lag ## The following objects are masked from 'package:base': ## ## intersect, setdiff, setequal, union library(tidyr) library(zoo) ## ## Attaching package: 'zoo' #...
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Statistical Modeling XII - Advanced Data Manipulation and Feature Engineering Towards A Comprehensive Version of X
knitr::opts_chunk$set(echo = TRUE) 1 Package Invoking library(dplyr) ## ## Attaching package: 'dplyr' ## The following objects are masked from 'package:stats': ## ## filter, lag ## The following objects are masked from 'package:base': ## ## intersect, setdiff, setequal, union library(tidyr) library(zoo) ## ## Attaching package: 'zoo' #...
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Statistical Modeling XI a - Recency Mapping, Logarithmic Transformation and Adjusted Min-Max Normalization on Outcome Data, Reversal Function and Proof of Injectivity, Customer-Level, Project-Level, and Customer/Project Grouped-Level Covariate Data
knitr::opts_chunk$set(echo = TRUE) 1 Package Invoking library(dplyr) ## ## Attaching package: 'dplyr' ## The following objects are masked from 'package:stats': ## ## filter, lag ## The following objects are masked from 'package:base': ## ## intersect, setdiff, setequal, union library(tidyr) library(zoo) ## ## Attaching package: 'zoo' #...
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Statistical Modeling X - Bayesian Hierarchical Dynamic Multi-Outcome Model With Counterfactual Trend Analysis & ATT Estimation With Staggered Adoption
knitr::opts_chunk$set(echo = TRUE) 1 Package Invoking library(tidyverse) ## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ── ## ✔ dplyr 1.1.2 ✔ readr 2.1.4 ## ✔ forcats 1.0.0 ✔ stringr 1.5.0 ## ✔ ggplot2 3.4.2 ✔ tibble 3...
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Statistical Modeling IX - Time-Series Cross-Sectional Data with Staggered Adoption
knitr::opts_chunk$set(echo = TRUE) 1 Package Invoking library(tidyverse) ## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ── ## ✔ dplyr 1.1.2 ✔ readr 2.1.4 ## ✔ forcats 1.0.0 ✔ stringr 1.5.0 ## ✔ ggplot2 3.4.2 ✔ tibble 3...
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Statistical Modeling VII a - Covariate Project Data Manipulation
knitr::opts_chunk$set(echo = TRUE) library(dplyr) ## ## Attaching package: 'dplyr' ## The following objects are masked from 'package:stats': ## ## filter, lag ## The following objects are masked from 'package:base': ## ## intersect, setdiff, setequal, union 1 Original Data Loading df_brand_tags <- read.csv("/Users/apple/Quantitative\...
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Statistical Modeling IV - Unsupervised Learning for RFM Clustering with K-Means and Gaussian Mixture Modeling
1 RFM with K-Means Clustering 1.1 Data Normalization First, we need to filter out rows with R_trans == Inf and then scale the features to have zero mean and unit variance. RFM_score_t_filtered <- RFM_score_t[RFM_score_t$R_trans != Inf,] data_t <- RFM_score_t_filtered[, c("R_trans", "F_trans", "M_trans")] data_t_normalized <- scale(data_t) 1.2 D...
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Statistical Modeling III - Panel View and Supervised Learning for RFM Score Ranking Classification
1 Panel View Let’s first take a look at the structure of customer/project pair over time in the unit of bi-week. imputed_PDF_biweek_indiv_clean$account_created_at <- as.Date(imputed_PDF_biweek_indiv_clean$account_created_at) imputed_PDF_biweek_indiv_clean <- imputed_PDF_biweek_indiv_clean |> group_by(user_project_pair) |> mutate(min_biwe...
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Statistical Modeling II - Visualization of Causal Panel Analysis on inKind Data
1 Reconciliation Adjustment We first correct the definition of reconciliation. It is now based on the sums rather than cumsum defined in Reconciliation Analysis I [1]. PDF <- PDF |> group_by(user_id, project_id) PDF$is_reconcilable <- NULL sums <- PDF |> summarise(sum_total_redemption_amount = sum(total_redemption_amount), sum_credi...
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Statistical Modeling I - Bayesian Synthetic Control With Different Latent Factor Models on Germany Reunification
1 Pre-processing the Germany Reunification data rstan_options(auto_write = TRUE) options(mc.cores = parallel::detectCores()) d <- read.dta("/Users/apple/Desktop/Path\ Towards\ Quant\ Mkt\ PhD/Collected\ Data/Germany\ Reunification/repgermany.dta") df_avg <- d |> group_by(index, country) |> summarize_at(c("gdp", "infrate", "trade", "industry"...
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