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Upsampling

Upsampling

Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...

Super-resolution Fluorescence Microscopy

Super-resolution Fluorescence Microscopy

Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been...

Downsampling

Downsampling

When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...

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Related Experiment Video

Updated: Dec 21, 2025

AMEBaS: Automatic Midline Extraction and Background Subtraction of Ratiometric Fluorescence Time-Lapses of Polarized Single Cells

AMEBaS: Automatic Midline Extraction and Background Subtraction of Ratiometric Fluorescence Time-Lapses of Polarized Single Cells

Published on: June 23, 2023

Resampling-based methods for biologists.

John R Fieberg¹, Kelsey Vitense¹, Douglas H Johnson¹

¹Department of Fisheries, Wildlife, and Conservation Biology, University of Minnesota, St. Paul, MN, USA.

|May 19, 2020

Summary

This summary is machine-generated.

Ecological statistics often violate standard assumptions. Resampling methods offer an accessible alternative for inference and understanding core statistical concepts in ecological research.

Keywords:

Bootstrap Model uncertainty Permutation Randomization Replication Resampling Statistical inference

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Last Updated: Dec 21, 2025

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Published on: June 23, 2023

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Area of Science:

Ecology
Statistics
Ecological Modeling

Background:

Ecological data frequently violate assumptions of traditional parametric statistics, such as normality, homogeneity of variance, and independence.
Modern statistical methods can address these violations but pose challenges for non-statisticians.
Complex statistical methods are not always necessary when alternative approaches exist.

Purpose of the Study:

To demonstrate the utility of resampling-based methods in ecological statistics.
To provide an accessible alternative to complex parametric methods for ecological data analysis.
To enhance understanding of fundamental frequentist statistical concepts.

Main Methods:

Resampling-based statistical methods (e.g., bootstrapping, permutation tests).
Application of methods to ecological data with non-standard distributions or dependencies.
Use of simple examples and case studies for illustration.

Main Results:

Resampling methods provide a viable alternative for statistical inference with ecological data.
These methods facilitate a clearer understanding of sampling distributions, confidence intervals, and p-values.
The approach is applicable across diverse ecological research areas.

Conclusions:

Resampling methods are valuable tools for ecological data analysis, offering both practical solutions and pedagogical benefits.
They empower ecologists to perform robust statistical inference without resorting to overly complex techniques.
Understanding resampling enhances foundational statistical knowledge for ecological applications.