Publications by Zhongming Jiang
Reconciliation Analysis III - Binary Logistic Regression Modeling of the Dynamic Reconciliation Perspective in Customer Lifetime Value
1 Invoking packages and loading data frames from Reconciliation Analysis I, II library("purrr") library("plm") library("rjson") library("DT") library("data.table") ## ## Attaching package: 'data.table' ## The following object is masked from 'package:plm': ## ## between ## The following object is masked from 'package:purrr': ## ## transpo...
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Reconciliation Analysis II - Density-based Estimation of Suspicious Promotions and Pseudo-stochastic Selection of Emblematic Non-reconcilable Dynamics
1 Storing data frames from Reconciliation Analysis I We will first restore necessary data frames from Reconciliation Analysis I [1] to continue the study. Invoked packages are hide. PDF_daily_impute_char_cumulated <- read.csv("/Users/apple/Quantitative\ Marketing\ Research/Reconciliation\ Analysis\ I/Reconciliation\ Analysis\ I\ Data/PDF_daily_i...
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Reconciliation Analysis I - Modeling and Extracting Emblematic Non-reconcilable Dynamics
1 More panel data reshaping Loadings in previous EDAs have been hide away. We should notice that our output DF does not incorporate the other two raw data frames, namely, df_brand_tags and df_projects. However, both three have common keys in project_id, we should consider joining them for being more informative. Notice that we discard the fourth co...
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Exploratory Data Analysis VIII - Daily Panel Data Frame with Marketing Campaigns and Time-varying Variables
Loading the data frame from EDA VII Codes from EDA VII and package loadings have been hide. Some arguments made in EDA VII have been overturned. We will start from the very initial data frame DF and reshape in a way we desire. DF dim(DF) ## [1] 416996 34 We can still exclude confounding customers defined in EDA VII [1] by removing customers 1...
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Exploratory Data Analysis VII - Repeat Observations on Time-varying Variables for Individual Customers
library("plm") library("rjson") library("DT") library("data.table") ## ## Attaching package: 'data.table' ## The following object is masked from 'package:plm': ## ## between library("tidyverse") ## ── Attaching packages ## ───────────────────────────────────────...
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Exploratory Data Analysis VI - Reproducing German Reunification with Bayesian Synthetic Control Method (BSCM)
library(rstan) ## Warning: package 'rstan' was built under R version 4.2.3 ## Loading required package: StanHeaders ## ## rstan version 2.26.22 (Stan version 2.26.1) ## For execution on a local, multicore CPU with excess RAM we recommend calling ## options(mc.cores = parallel::detectCores()). ## To avoid recompilation of unchanged Stan programs, w...
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Exploratory Data Analysis V - Reproducing German Reunification with Generalized Synthetic Control Method (GSCM)
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(loo) ## This is loo version 2.5.1 ## - Online documentation and vignettes at mc-stan.org/loo ## - As of v2....
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Exploratory Data Analysis IV - Further Revision on Redemptions and Revenue
library("rjson") library("DT") library("data.table") library("tidyverse") ## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ── ## ✔ ggplot2 3.4.0 ✔ purrr 0.3.4 ## ✔ tibble 3.2.1 ✔ dplyr 1.1.2 ## ✔ tidyr 1.2.0...
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Exploratory Data Analysis III - Redemptions and Revenue
Codes from previous EDAs redemptions_2021 <- fromJSON(file = "/Users/apple/Desktop/2023\ Feb\ Transfer/redemptions_2021.json") redemptions_2022 <- fromJSON(file = "/Users/apple/Desktop/2023\ Feb\ Transfer/redemptions_2022.json") redemptions_2023_Jan <- fromJSON(file = "/Users/apple/Desktop/2023\ Feb\ Transfer/redemptions_2023_Jan.json")...
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Exploratory Data Analysis II with focus on customers
Prompts / Suggestions from May 9’s meeting: hist. x-axis on number of transc; y-axis on users’ transc times (freq table; use group_by) avg number of spend !!; individual spend -> # transc ~ 20/21 repeated obs??? -> impact the model (HMM) given a user, what is dist. of unique project id accross customer? -> summary stats 1, 2, or 3…? -> more ...
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