How to do research?

科研论文写作指南

How to do research?

  • Find a research point.
  • Read new paper,such as ICCV,CVPR,PAMI.If you find some new idea,which can be made use of in your work,you can do it.
  • Think it before you do it,analyze why you do it like that.
  • If you have a problem,talk about it with your teachers,and colleagues,analyze why it doesn't work.Brain storm...
  • Do not depend on others excessively,you should have your own ideas.
  • When you talk about with others,you should know what you can get from others.If you have any problem, must solve it.Do not leave it alone.
  • Study how to write a good paper,including structure,grammar,...,ensure readers can easily understand what you want to express, and your experiment support your idea.

A “Template” for Good Papers--From the report of Professor Charles Ling

  • Abstract What is the problem? What is your solution and results?
  • Introduction Problem X is important.Previous work A, B, … have been studied A, B have certain weakness (be careful here);We propose D, features of D (how much)Experiment with D.Compared with A, B,D is better than A, B(in certain aspects) ;Outline of the paper
  • Review of Previous Work Previous work:may split to several classes,e.g. Can review each work in one or several sentences;Compare to yours (refer to later sections)May also put it after sections about your work.
  • Our Work Definition, notation (need motivation);Algorithms: pseudo-code; diagram;explanations Answer potential questions from readers;Too much details (such as proof): appendix
  • Experiments and Comparisons Experiment design;Comparisons (Is it scientific?); Discussions (make sense of the results);Draw conclusions;
  • Conclusions Quick summary;Future work

 

zz from http://www.jdl.ac.cn/user/sfxie/Publication.html

To do IPTW by R, you can follow these steps: 1. Import your data into R and create a new variable to indicate treatment status (0 for control, 1 for treatment). 2. Create a new variable to hold the inverse probability weights (IPW). 3. Use R to estimate the propensity score for treatment using logistic regression. This will give you a predicted probability of receiving treatment for each observation. 4. Calculate the IPW for each observation by taking the reciprocal of the propensity score for treated observations, and the reciprocal of (1 - propensity score) for control observations. 5. Apply the IPW to your outcome variable using the survey package in R. Specifically, use the svyglm function to fit a generalized linear model with the IPW as weights. This will give you the weighted estimate of the treatment effect. Here's an example code: ``` # Load the survey package library(survey) # Import your data data <- read.csv("your_data_file.csv") # Create a new variable for treatment status data$treatment <- as.factor(data$treatment) # Estimate the propensity score using logistic regression ps_model <- glm(treatment ~ covariate1 + covariate2 + covariate3, data = data, family = "binomial") propensity_score <- predict(ps_model, data, type = "response") # Calculate the IPW ipw <- ifelse(data$treatment == 1, 1 / propensity_score, 1 / (1 - propensity_score)) # Apply the IPW to your outcome variable outcome_model <- svyglm(outcome ~ treatment, design = svydesign(ids = ~1, weights = ipw, data = data)) summary(outcome_model) ``` Note that this is just a general overview, and the exact steps may vary depending on your specific research question and data. It's important to consult with a statistician or other expert to ensure that you are using the appropriate methods and interpreting the results correctly.
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