Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Distributions to Estimate Population Parameter

Distributions to Estimate Population Parameter

The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...

Probability Distributions

Probability Distributions

The probability of a random variable x is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson probability...

Choosing Between z and t Distribution

Choosing Between z and t Distribution

The z and the Student t distribution estimate the population mean using the sample mean and standard deviation. However, to decide which distribution to use for a calculation, one needs to determine the sample size, the nature of the distribution, and whether the population standard deviation is known. If the population standard deviation is known and the population is normally distributed, or if the sample size is greater than 30, the z distribution is preferred. The Student t distribution is...

Parametric Survival Analysis: Weibull and Exponential Methods

Parametric Survival Analysis: Weibull and Exponential Methods

Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...

Probability Histograms

Probability Histograms

A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.

Uniform Distribution

Uniform Distribution

The uniform distribution is a continuous probability distribution of events with an equal probability of occurrence. This distribution is rectangular.Two essential properties of this distribution are The area under the rectangular shape equals 1. There is a correspondence between the probability of an event and the area under the curve.Further, the mean and standard deviation of the uniform distribution can be calculated when the lower and upper cut-offs, denoted as a and b,...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Practical Consequences of the Bias in the Laplace Approximation to Marginal Likelihood for Hierarchical Models.

Entropy (Basel, Switzerland)·2025

Same author

Simultaneous estimation of the temporal and spatial extent of animal migration using step lengths and turning angles.

Movement ecology·2024

Same author

Profile Likelihood for Hierarchical Models Using Data Doubling.

Entropy (Basel, Switzerland)·2023

Same author

Errors in Statistical Inference Under Model Misspecification: Evidence, Hypothesis Testing, and AIC.

Frontiers in ecology and evolution·2021

Same author

A unifying theory for two-dimensional spatial redistribution kernels with applications in population spread modelling.

Journal of the Royal Society, Interface·2020

Same author

Estimating the intensity of use by interacting predators and prey using camera traps.

The Journal of animal ecology·2019

Search research articles

Related Experiment Video

Updated: Jul 17, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Weighted distributions and estimation of resource selection probability functions.

Subhash R Lele¹, Jonah L Keim

¹Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, Alberta T6G 2G1, Canada. slele@ualberta.ca

|January 26, 2007

Summary

Wildlife managers can now estimate the resource selection probability function (RSPF) directly, not just a proportional function. This new method improves resource use estimation for better wildlife management strategies.

Related Experiment Videos

Last Updated: Jul 17, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Area of Science:

Ecology and Wildlife Management
Quantitative Ecology
Statistical Modeling in Biology

Background:

Understanding animal resource selection is crucial for effective wildlife management.
The resource selection probability function (RSPF) quantifies individual resource use based on environmental factors.
Current methods often estimate a proportional resource selection function (RSF), not the RSPF directly, limiting precise estimation.

Purpose of the Study:

To demonstrate a novel method for directly estimating the resource selection probability function (RSPF).
To establish a connection between RSPF estimation and weighted distribution theory.
To provide fully efficient, maximum likelihood estimators for RSPF in wildlife management.

Main Methods:

Leveraged the connection between RSPF estimation and the weight function in weighted distribution theory.
Developed maximum likelihood estimators for RSPF applicable to common wildlife survey designs.
Utilized GPS collar data from mountain goats in British Columbia for method validation.

Main Results:

Established a statistically sound method to directly estimate the resource selection probability function (RSPF).
Demonstrated that RSPF is estimable even when unused sites are not fully known.
Successfully applied the method to GPS data, yielding efficient estimators for mountain goat resource selection.

Conclusions:

The study provides a significant advancement in estimating animal resource selection probability.
This new approach allows for more accurate quantification of resource use, enhancing wildlife management strategies.
The method offers a powerful tool for ecologists and wildlife managers utilizing animal tracking data.