Sampling (statistics)

Flow sampling

In statistics, in flow sampling, as opposed to stock sampling, observations are collected as they enter the particular state of interest during a particular interval. When dealing with duration data (such as employment spells or mortality outcomes), the data sampling method has a direct impact on subsequent analyses and inference. An example in demography would be sampling the number of people who die within a given time frame (e.g. a specific calendar year); a popular example in economics would be the number of people leaving unemployment within a given time frame (e.g. a specific quarter). Researchers imposing similar assumptions but using different sampling methods, can reach fundamentally different conclusions if the joint distribution across the flow and stock samples differ. Typically, flow samples suffer from right censoring. After a certain amount of time, as the sampling interval ends, the individuals in the sample are not followed any longer, outcomes are recorded and the data is analyzed. In the unemployment example outlined above, we observe the exact duration for individuals leaving unemployment within the time frame. For people that haven't left unemployment yet, we only observe the lower bound of the unemployment spell. The difference between stock and flow sampling can also help explain why certain statistics that measure similar duration measures can differ in important ways. Consider, for instance, the Average Interrupted Duration (AID), the average period for which people that are currently unemployed have been unemployed, and ACD, the average duration of the complete unemployment spell for employed people. Salant shows that heterogeneity in hazard rates between the stock and the flow distribution provides a key to understanding why these two statistics differ. For instance, if the probability of getting a job offer goes down with time unemployed, E[T] < E[S], where S and T stand for observed and actual duration respectively. Renewal theory is the appropriate tool for handling these issues, and a wide range of estimators have been proposed. These estimators range from fully parametric models such as the Mixed Proportional Hazard model, to nonparametric and semiparametric methods. (Wikipedia).

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Quantization and Coding in A/D Conversion

http://AllSignalProcessing.com for more great signal-processing content: ad-free videos, concept/screenshot files, quizzes, MATLAB and data files. Real sampling systems use a limited number of bits to represent the samples of the signal, resulting in quantization of the signal amplitude t

From playlist Sampling and Reconstruction of Signals

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Statistics Lesson #1: Sampling

This video is for my College Algebra and Statistics students (and anyone else who may find it helpful). It includes defining and looking at examples of five sampling methods: simple random sampling, convenience sampling, systematic sampling, stratified sampling, cluster sampling. We also l

From playlist Statistics

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Stochastic Normalizing Flows

Introduction to the paper https://arxiv.org/abs/2002.06707

From playlist Research

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Quota Sampling

What is quota sampling? Advantages and disadvantages. General steps and an example of how to find a quote sample. Check out my e-book, Sampling in Statistics, which covers everything you need to know to find samples with more than 20 different techniques: https://prof-essa.creator-spring.

From playlist Sampling

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Frequency Domain Interpretation of Sampling

http://AllSignalProcessing.com for more great signal-processing content: ad-free videos, concept/screenshot files, quizzes, MATLAB and data files. Analysis of the effect of sampling a continuous-time signal in the frequency domain through use of the Fourier transform.

From playlist Sampling and Reconstruction of Signals

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Statistics: Sampling Methods

This lesson introduces the different sample methods when conducting a poll or survey. Site: http://mathispower4u.com

From playlist Introduction to Statistics

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Snowball Sampling Overview

Brief Introduction to Snowball Sampling. Advantages and disadvantages. Check out my e-book, Sampling in Statistics, which covers everything you need to know to find samples with more than 20 different techniques: https://prof-essa.creator-spring.com/listing/sampling-in-statistics

From playlist Sampling

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Probability Sampling Methods

What is "Probability sampling?" A brief overview. Four different types, their advantages and disadvantages: cluster, SRS (Simple Random Sampling), Systematic and Stratified sampling. Check out my e-book, Sampling in Statistics, which covers everything you need to know to find samples with

From playlist Sampling

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Purposive Sampling

What is purposive (deliberate) sampling? Types of purposive sampling, advantages and disadvantages. Check out my e-book, Sampling in Statistics, which covers everything you need to know to find samples with more than 20 different techniques: https://prof-essa.creator-spring.com/listing/sam

From playlist Sampling

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Sign Problem and the Generalized Thimble Method by Andrei Alexandru

Nonperturbative and Numerical Approaches to Quantum Gravity, String Theory and Holography DATE:27 January 2018 to 03 February 2018 VENUE:Ramanujan Lecture Hall, ICTS Bangalore The program "Nonperturbative and Numerical Approaches to Quantum Gravity, String Theory and Holography" aims to

From playlist Nonperturbative and Numerical Approaches to Quantum Gravity, String Theory and Holography

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Debmalya Panigrahi: Isolating Cuts: A New Tool for Minimum Cut Algorithms

Minimum cut problems are among the most well-studied questions in combinatorial optimization. In this talk, I will introduce a simple but powerful new tool for solving minimum cut problems called the isolating cuts lemma. I will show how this tool can be employed to obtain faster algorithm

From playlist Workshop: Continuous approaches to discrete optimization

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23C3: sFlow

Speaker: Elisa Jasinska I can feel your traffic The explosion of internet traffic is leading to higher bandwidths and an increased need for high speed networks. To analyze and optimize such networks an efficient monitoring system is required. The sFlow standard describes a mechanism to

From playlist 23C3: Who can you trust

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Chris Jones - Does the problem matter

PROGRAM: Nonlinear filtering and data assimilation DATES: Wednesday 08 Jan, 2014 - Saturday 11 Jan, 2014 VENUE: ICTS-TIFR, IISc Campus, Bangalore LINK:http://www.icts.res.in/discussion_meeting/NFDA2014/ The applications of the framework of filtering theory to the problem of data assimi

From playlist Nonlinear filtering and data assimilation

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Brittany Carr (6/2/20): Zigzag persistence to extract non-linear statistics from optical flow

Title: Zigzag persistence to extract non-linear statistics from optical flow Abstract: Optical flow is the term used to describe the way 3-dimensional movement is conveyed on a 2-dimensional screen. The true movement of the 3-dimensional object cannot be reconstructed from the video, but

From playlist SIAM Topological Image Analysis 2020

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Gabriele Steidl: Stochastic normalizing flows and the power of patches in inverse problems

CONFERENCE Recording during the thematic meeting : "Learning and Optimization in Luminy" the October 4, 2022 at the Centre International de Rencontres Mathématiques (Marseille, France) Filmmaker: Guillaume Hennenfent Find this video and other talks given by worldwide mathematicians on C

From playlist Probability and Statistics

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GRCon19 - Managing Latency in Continuous GNU Radio Flowgraphs by Matt Ettus

Managing Latency in Continuous GNU Radio Flowgraphs by Matt Ettus

From playlist GRCon 2019

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Elaine Spiller - Importance Sampling

PROGRAM: Nonlinear filtering and data assimilation DATES: Wednesday 08 Jan, 2014 - Saturday 11 Jan, 2014 VENUE: ICTS-TIFR, IISc Campus, Bangalore LINK:http://www.icts.res.in/discussion_meeting/NFDA2014/ The applications of the framework of filtering theory to the problem of data assimi

From playlist Nonlinear filtering and data assimilation

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Convenience Sampling

What is convenience sampling? Advantages and disadvantages of grab sampling. How to analyze data from convenience sampling. Check out my e-book, Sampling in Statistics, which covers everything you need to know to find samples with more than 20 different techniques: https://prof-essa.creato

From playlist Sampling

Related pages

Statistical inference | Stock sampling | Censoring (statistics) | Sampling (statistics) | Mortality rate | Renewal theory