Applied Hierarchical Modeling in Ecology-经管之家官网！

经济学管理学金融学统计学

您当前的位置> 会计>>

Applied Hierarchical Modeling in Ecology

发布：crazydancer | 分类：会计库

关于本站

人大经济论坛-经管之家：分享大学、考研、论文、会计、留学、数据、经济学、金融学、管理学、统计学、博弈论、统计年鉴、行业分析包括等相关资源。
经管之家是国内活跃的在线教育咨询平台!

提供"微信号、微博、抖音、快手、头条、小红书、百家号、企鹅号、UC号、一点资讯"等虚拟账号交易，真正实现买卖双方的共赢。【请点击这里访问】

TOP热门关键词

专题页面精选

TableofContentsPart1.PreludeChapter1.Distribution,Abundance,andSpeciesRichnessinEcology1.1.PointProcesses,Distribution,Abundance,andSpeciesRichness1.2.Meta-populationDesigns1.3.StateandRateParameters1 ...

坛友互助群

扫码加入各岗位、行业、专业交流群

Table of Contents

Part 1. Prelude
- Chapter 1. Distribution, Abundance, and Species Richness in Ecology
  - 1.1. Point Processes, Distribution, Abundance, and Species Richness
  - 1.2. Meta-population Designs
  - 1.3. State and Rate Parameters
  - 1.4. Measurement Error Models in Ecology
  - 1.5. Hierarchical Models for Distribution, Abundance, and Species Richness
  - 1.6. Summary and Outlook
  - Exercises
- Chapter 2. What Are Hierarchical Models and How Do We Analyze Them?
  - 2.1. Introduction
  - 2.2. Random Variables, Probability Density Functions, Statistical Models, Probability, and Statistical Inference
  - 2.3. Hierarchical Models (HMs)
  - 2.4. Classical Inference Based on Likelihood
  - 2.5. Bayesian Inference
  - 2.6. Basic Markov Chain Monte Carlo (MCMC)
  - 2.7. Model Selection and Averaging
  - 2.8. Assessment of Model Fit
  - 2.9. Summary and Outlook
  - Exercises
- Chapter 3. Linear Models, Generalized Linear Models (GLMs), and Random Effects Models: The Components of Hierarchical Models
  - 3.1. Introduction
  - 3.2. Linear Models
  - 3.3. Generalized Linear Models (GLMs)
  - 3.4. Random Effects (Mixed) Models
  - 3.5. Summary and Outlook
  - Exercises
- Chapter 4. Introduction to Data Simulation
  - 4.1. What Do We Mean by Data Simulation, and Why Is It So Tremendously Useful?
  - 4.2. Generation of a Typical Point Count Data Set
  - 4.3. Packaging Everything in a Function
  - 4.4. Summary and Outlook
  - Exercises
- Chapter 5. Fitting Models Using the Bayesian Modeling Software BUGS and JAGS
  - 5.1. Introduction
  - 5.2. Introduction to BUGS Software: WinBUGS, OpenBUGS, and JAGS
  - 5.3. Linear Model with Normal Response (Normal GLM): Multiple Linear Regression
  - 5.4. The R Package rjags
  - 5.5. Missing values (NAs) in a Bayesian Analysis
  - 5.6. Linear Model with Normal Response (Normal GLM): Analysis of Covariance (ANCOVA)
  - 5.7. Proportion of Variance Explained (R2)
  - 5.8. Fitting a Model with Nonstandard Likelihood Using the Zeros or the Ones Tricks
  - 5.9. Poisson GLM
  - 5.10. GoF Assessment: Posterior Predictive Checks and the Parametric Bootstrap
  - 5.11. Binomial GLM (Logistic Regression)
  - 5.12. Moment-Matching in a Binomial GLM to Accommodate Underdispersion
  - 5.13. Random-Effects Poisson GLM (Poisson GLMM)
  - 5.14. Random-Effects Binomial GLM (Binomial GLMM)
  - 5.15. General Strategy of Model Building with BUGS
  - 5.16. Summary and Outlook
  - Exercises
Part 2. Models for Static Systems
- Chapter 6. Modeling Abundance with Counts of Unmarked Individuals in Closed Populations: Binomial N-mixture Models
  - 6.1. Introduction to the Modeling of Abundance
  - 6.2. An Exercise in Hierarchical Modeling: Derivation of Binomial N-mixture Models from First Principles
  - 6.3. Simulation and Analysis of the Simplest Possible N-mixture Model
  - 6.4. A Slightly More Complex N-mixture Model with Covariates
  - 6.5. A Very General Data Simulation Function for N-mixture Models: simNmix
  - 6.6. Study Design, Bias, and Precision of the Binomial N-mixture Model Estimator
  - 6.7. Study of Some Assumption Violations Using Function simNmix
  - 6.8. Goodness-of-Fit (GoF)
  - 6.9. Abundance Mapping of Swiss Great Tits with unmarked
  - 6.10. The Issue of Space, or: What Is Your Effective Sample Area?
  - 6.11. Bayesian Modeling of Swiss Great Tits with BUGS
  - 6.12. Time-for-Space Substitution
  - 6.13. The Royle-Nichols Model and Other Nonstandard N-mixture Models
  - 6.14. Multiscale N-mixture Models
  - 6.15. Summary and Outlook
  - Exercises
- Chapter 7. Modeling Abundance Using Multinomial N-Mixture Models
  - 7.1. Introduction
  - 7.2. Multinomial N-Mixture Models in Ecology
  - 7.3. Simulating Multinomial Observations in R
  - 7.4. Likelihood Inference for Multinomial N-Mixture Models
  - 7.5. Example 1: Bird Point Counts Based on Removal Sampling
  - 7.6. Bayesian Analysis in BUGS Using the Conditional Multinomial (Three-Part) Model
  - 7.7. Building Custom Multinomial Models in unmarked
  - 7.8. Spatially Stratified Capture-Recapture Models
  - 7.9. Example 3: Jays in the Swiss MHB
  - 7.10. Summary and Outlook
  - Exercises
- Chapter 8. Modeling Abundance Using Hierarchical Distance Sampling
  - 8.1. Introduction
  - 8.2. Conventional Distance Sampling
  - 8.3. Bayesian Conventional Distance Sampling
  - 8.4. Hierarchical Distance Sampling (HDS)
  - 8.5. Bayesian HDS
  - 8.6. Summary
  - Exercises
- Chapter 9. Advanced Hierarchical Distance Sampling
  - 9.1. Introduction
  - 9.2. Distance Sampling (DS) with Clusters, Groups, or Other Individual Covariates
  - 9.3. Time-Removal and DS Combined
  - 9.4. Mark-Recapture/Double-Observer DS
  - 9.5. Open HDS Models: Temporary Emigration
  - 9.6. Open HDS Models: Implicit Dynamics
  - 9.7. Open HDS Models: Modeling Population Dynamics
  - 9.8. Spatial Distance Sampling: Modeling Within-Unit Variation in Density
  - 9.9. Summary
  - Exercises
- Chapter 10. Modeling Static Occurrence and Species Distributions Using Site-occupancy Models
  - 10.1. Introduction to the Modeling of Occurrence—Including Species Distributions
  - 10.2. Another Exercise in Hierarchical Modeling: Derivation of the Site-Occupancy Model
  - 10.3. Simulation and Analysis of the Simplest Possible Site-Occupancy Model
  - 10.4. A Slightly More Complex Site-Occupancy Model with Covariates
  - 10.5. A General Data Simulation Function for Static Occupancy Models: simOcc
  - 10.6. A Model with Lots of Covariates: Use of R Function model.matrix with BUGS
  - 10.7. Study Design, and Bias and Precision of Site-Occupancy Estimators
  - 10.8. Goodness-of-Fit
  - 10.9. Distribution Modeling and Mapping of Swiss Red Squirrels
  - 10.10. Multiscale Occupancy Models
  - 10.11. Space-for-Time Substitution
  - 10.12. Models for Data along Transects: Poisson, Exponential, Weibull, and Removal Observation Models
  - 10.13. Occupancy Modeling of a Community of Species
  - 10.14. Modeling Wiggly Covariate Relationships: Penalized Splines in Hierarchical Models
  - 10.15. Summary and Outlook
  - Exercises
- Chapter 11. Hierarchical Models for Communities
  - 11.1. Introduction
  - 11.2. Simulation of a Metacommunity
  - 11.3. Metacommunity Data from the Swiss Breeding Bird Survey MHB
  - 11.4. Overview of Some Models for Metacommunities
  - 11.5. Community Models That Ignore Species Identity
  - 11.6. Community Models that Fully Retain Species Identity
  - 11.7. The Dorazio/Royle (DR) Community Occupancy Model with Data Augmentation (DA)
  - 11.8. Inferences Based on the Estimated Z Matrix: Similarity among Sites and Species
  - 11.9. Species Richness Maps and Species Accumulation Curves
  - 11.10. Community N-mixture (or Dorazio/Royle/Yamaura - DRY) Models
  - 11.11. Summary and Outlook
  - Exercises
Summary and Conclusion
References
Author Index
Subject Index

扫码或添加微信号：坛友素质互助

「经管之家」APP：经管人学习、答疑、交友，就上经管之家！
免流量费下载资料----在经管之家app可以下载论坛上的所有资源，并且不额外收取下载高峰期的论坛币。
涵盖所有经管领域的优秀内容----覆盖经济、管理、金融投资、计量统计、数据分析、国贸、财会等专业的学习宝库，各类资料应有尽有。
来自五湖四海的经管达人----已经有上千万的经管人来到这里，你可以找到任何学科方向、有共同话题的朋友。
经管之家（原人大经济论坛），跨越高校的围墙，带你走进经管知识的新世界。
扫描下方二维码下载并注册APP

本文关键词：

本文论坛网址：https://bbs.pinggu.org/thread-7072886-1-1.html

上一篇 | 今年1级JC基础班financial report部分应该 ...

下一篇 | There's a hope that's waiting for you ...

会计库精彩帖子推荐更多

您可能感兴趣的文章

本站推荐的文章

人气文章

本文标题：Applied Hierarchical Modeling in Ecology

本文链接网址：https://bbs.pinggu.org/jg/huiji_huijiku_7072886_1.html

1.凡人大经济论坛-经管之家转载的文章,均出自其它媒体或其他官网介绍,目的在于传递更多的信息,并不代表本站赞同其观点和其真实性负责；
2.转载的文章仅代表原创作者观点,与本站无关。其原创性以及文中陈述文字和内容未经本站证实,本站对该文以及其中全部或者部分内容、文字的真实性、完整性、及时性，不作出任何保证或承若；
3.如本站转载稿涉及版权等问题,请作者及时联系本站,我们会及时处理。

Applied Hierarchical Modeling in Ecology-经管之家官网！

会计库