পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| ফিশার এক্সাক্ট র্যান্ডমাইজেশন ইনফারেন্স× | বুুটস্ট্র্যাপ অনুমান× | সাধারণ ন্যূনতম বর্গক্ষেত্র (OLS) রিগ্রেশন× | |
|---|---|---|---|
| ক্ষেত্র≠ | পরিসংখ্যান | পরিসংখ্যান | অর্থমিতি |
| পরিবার | Regression model | Regression model | Regression model |
| উদ্ভবের বছর≠ | 1935 | 1979 | 2019 |
| প্রবর্তক≠ | Ronald A. Fisher | Bradley Efron | Wooldridge (textbook treatment); classical least squares |
| ধরন≠ | Exact permutation-based inference | Resampling-based inference | Linear regression |
| মৌলিক উৎস≠ | Fisher, R. A. (1935). The Design of Experiments. Oliver & Boyd. link ↗ | Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. Annals of Statistics, 7(1), 1-26. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| অপর নাম | fisher randomization test, permutation inference, exact randomization test, randomizasyon çıkarımı (fisher exact randomization) | bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| সম্পর্কিত | 5 | 5 | 5 |
| সারসংক্ষেপ≠ | Randomization inference, introduced by Ronald A. Fisher in The Design of Experiments (1935), computes an exact p-value by evaluating a test statistic across all possible treatment assignments under Fisher's sharp null hypothesis. It is regarded as the gold standard for analysing designed experiments because its validity rests on the known assignment mechanism rather than on distributional assumptions. | Bootstrap inference, introduced by Bradley Efron in 1979, estimates the sampling distribution of a statistic by repeatedly resampling the observed data with replacement. It requires no distributional assumption and produces reliable confidence intervals even in small samples. | Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE). |
| ScholarGateডেটাসেট ↗ |
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