scholarly journals Designing plots for precise estimation of forest attributes in landscapes and forests of varying heterogeneity

2021 ◽  
Author(s):  
Andrew J. Lister ◽  
Laura Leites
Keyword(s):  
Significant Relationship ◽  
Forest Inventory ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Multiple Scales ◽  
Sampling Efficiency ◽  
Statistical Efficiency ◽  
Model Framework ◽  
Precise Estimation ◽  
Cluster Configuration

Models of relationships among forest inventory sampling efficiency and cluster plot configuration variables inform decisions by inventory planners. However, relationships vary under different spatial heterogeneity scenarios. In order to improve understanding of how spatial patterns of forests affects these relationships, we implemented a factorial experiment by simulating forest pattern at both the landscape and stand scales. We sampled these simulated forests with a variety of cluster plot configurations, calculated coefficient of variation (CV) of trees per hectare (TPH) for each replicate, and tested the relationships among CV and the heterogeneity and cluster plot configuration factors within a linear mixed model framework. Both landscape and stand-scale pattern aggregation had a significant relationship with CV. Changing cluster plot configuration factors did little to change the overall CV when using larger subplots but had some important effects when using smaller subplots. These impacts were stronger in the more uniform landscapes. Results were opposite for stand-scale heterogeneity; changing plot configuration in areas with aggregated patterns had a stronger impact than it did in areas with more uniform patterns. Results of this study reveal the importance of accounting for spatial pattern at multiple scales when making cluster configuration choices if the goal is statistical efficiency.

scholarly journals A Novel Approach to the Design and Analysis of Field Experiments to Study Variation in the Tolerance and Resistance of Cultivars to Root Lesion Nematodes (Pratylenchus spp.)

2020 ◽  
Vol 110 (10) ◽  
pp. 1623-1631
Author(s):  
Karyn L. Reeves ◽  
Clayton R. Forknall ◽  
Alison M. Kelly ◽  
Kirsty J. Owen ◽  
Joshua Fanning ◽  
...  
Keyword(s):  
Mixed Model ◽  
Field Experiments ◽  
Linear Mixed Model ◽  
Random Regression ◽  
Yield Response ◽  
Response Curves ◽  
Model Framework ◽  
Management Options ◽  
Nematode Reproduction ◽  
Yield Response Curves

The root lesion nematode (RLN) species Pratylenchus thornei and P. neglectus are widely distributed within cropping regions of Australia and have been shown to limit grain production. Field experiments conducted to compare the performance of cultivars in the presence of RLNs investigate management options for growers by identifying cultivars with resistance, by limiting nematode reproduction, and tolerance, by yielding well in the presence of nematodes. A novel experimental design approach for RLN experiments is proposed where the observed RLN density, measured prior to sowing, is used to condition the randomization of cultivars to field plots. This approach ensured that all cultivars were exposed to consistent ranges of RLN in order to derive valid assessments of relative cultivar tolerance and resistance. Using data from a field experiment designed using the conditioned randomization approach and conducted in Formartin, Australia, the analysis of tolerance and resistance was undertaken in a linear mixed model framework. Yield response curves were derived using a random regression approach and curves modeling change in RLN densities between sowing and harvest were derived using splines to account for nonlinearity. Groups of cultivars sharing similar resistance levels could be identified. A comparison of slopes of yield response curves of cultivars belonging to the same resistance class identified differing tolerance levels for cultivars with equivalent exposures to both presowing and postharvest RLN densities. As such, the proposed design and analysis approach allowed tolerance to be assessed independently of resistance.

scholarly journals Sparse Unmixing for Hyperspectral Image with Nonlocal Low-Rank Prior

2019 ◽  
Vol 11 (24) ◽  
pp. 2897 ◽  
Author(s):  
Yuhui Zheng ◽  
Feiyang Wu ◽  
Hiuk Jae Shim ◽  
Le Sun
Keyword(s):  
Mixed Model ◽  
Linear Mixed Model ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Simulated Data ◽  
Hyperspectral Data ◽  
Low Rank ◽  
Data Sets ◽  
Self Similarity ◽  
Model Framework

Hyperspectral unmixing is a key preprocessing technique for hyperspectral image analysis. To further improve the unmixing performance, in this paper, a nonlocal low-rank prior associated with spatial smoothness and spectral collaborative sparsity are integrated together for unmixing the hyperspectral data. The proposed method is based on a fact that hyperspectral images have self-similarity in nonlocal sense and smoothness in local sense. To explore the spatial self-similarity, nonlocal cubic patches are grouped together to compose a low-rank matrix. Then, based on the linear mixed model framework, the nuclear norm is constrained to the abundance matrix of these similar patches to enforce low-rank property. In addition, the local spatial information and spectral characteristic are also taken into account by introducing TV regularization and collaborative sparse terms, respectively. Finally, the results of the experiments on two simulated data sets and two real data sets show that the proposed algorithm produces better performance than other state-of-the-art algorithms.

scholarly journals Multivariate Generalized Linear Mixed-Effects Models for the Analysis of Clinical Trial–Based Cost-Effectiveness Data

2021 ◽  
pp. 0272989X2110038
Author(s):  
Felix Achana ◽  
Daniel Gallacher ◽  
Raymond Oppong ◽  
Sungwook Kim ◽  
Stavros Petrou ◽  
...  
Keyword(s):  
Cost Effectiveness ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Simulated Data ◽  
Repeated Measurements ◽  
Mixed Data ◽  
Economic Evaluations ◽  
Data Types ◽  
Model Framework ◽  
New Methods

Economic evaluations conducted alongside randomized controlled trials are a popular vehicle for generating high-quality evidence on the incremental cost-effectiveness of competing health care interventions. Typically, in these studies, resource use (and by extension, economic costs) and clinical (or preference-based health) outcomes data are collected prospectively for trial participants to estimate the joint distribution of incremental costs and incremental benefits associated with the intervention. In this article, we extend the generalized linear mixed-model framework to enable simultaneous modeling of multiple outcomes of mixed data types, such as those typically encountered in trial-based economic evaluations, taking into account correlation of outcomes due to repeated measurements on the same individual and other clustering effects. We provide new wrapper functions to estimate the models in Stata and R by maximum and restricted maximum quasi-likelihood and compare the performance of the new routines with alternative implementations across a range of statistical programming packages. Empirical applications using observed and simulated data from clinical trials suggest that the new methods produce broadly similar results as compared with Stata’s merlin and gsem commands and a Bayesian implementation in WinBUGS. We highlight that, although these empirical applications primarily focus on trial-based economic evaluations, the new methods presented can be generalized to other health economic investigations characterized by multivariate hierarchical data structures.

scholarly journals On Zero-Inflated Hierarchical Poisson Models with Application to Maternal Mortality Data

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Kassim Tawiah ◽  
Samuel Iddi ◽  
Anani Lotsi
Keyword(s):  
Maternal Health ◽  
Maternal Mortality ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Health Planning ◽  
Health Sector ◽  
Generalized Linear Mixed Model ◽  
Mortality Data ◽  
Model Framework ◽  
The Government

Count outcomes are commonly encountered in health sector data. The occurrence of count outcomes that exhibit many zeros has necessitated the extension of the ubiquitous Poisson regression model to accommodate the zero inflation and overdispersion as a result of the extra dispersion. We explored different extensions of the Poisson model including mixed models within the generalized linear mixed model framework to account for the repeated measurement of outcomes. These models are applied to maternal mortality data from fifty-six health facilities in four regions of Ghana. The objective is to identify factors associated with maternal mortality. The best-fitting model, the zero-inflated Poisson generalized linear mixed model, revealed that maternal mortality in hospital facilities is influenced by the number of referrals (into and out) of the hospital facility, number of antenatal visits exceeding four, number of midwives, and number of medical doctors at the facility. To be able to achieve targeted results in reducing maternal mortality and achieve the Sustainable Development Goal 3, the government, together with the ministry of health, should provide adequate maternal health services, especially at the district and community level. Additionally, there is a need for increased investment in Community Health Planning Services and related healthcare infrastructure and systems within the context of the Ouagadougou Declaration, that is, improve the training of skilled birth workers (midwives and doctors) and employ them at clinics to deal with labour complications without referring them to major hospitals. Furthermore, a well-structured awareness campaign is needed with importance given to avoiding adolescent pregnancy and improving antenatal care attendance to, at least, four, the gold standard, before delivery. Also, we recommend quality assessment form an essential part of all services that are directed towards improving maternal health and that more emphasis is needed to be given on research with multiple allied partners.

scholarly journals Linear Mixed Models

2022 ◽  
pp. 141-170
Author(s):  
Osval Antonio Montesinos López ◽  
Abelardo Montesinos López ◽  
Jose Crossa
Keyword(s):  
Parameter Estimation ◽  
Maximum Likelihood ◽  
Mixed Models ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Linear Mixed Models ◽  
Prediction Performance ◽  
Environment Interaction ◽  
Model Framework ◽  
Genotype Environment Interaction

AbstractThe linear mixed model framework is explained in detail in this chapter. We explore three methods of parameter estimation (maximum likelihood, EM algorithm, and REML) and illustrate how genomic-enabled predictions are performed under this framework. We illustrate the use of linear mixed models by using the predictor several components such as environments, genotypes, and genotype × environment interaction. Also, the linear mixed model is illustrated under a multi-trait framework that is important in the prediction performance when the degree of correlation between traits is moderate or large. We illustrate the use of single-trait and multi-trait linear mixed models and provide the R codes for performing the analyses.

Joint Modeling for Longitudinal Data with Missing Values: A Bayesian Perspective on Human Intelligence

2021 ◽  
Vol 13 (2) ◽  
pp. 521-536
Author(s):  
T. Gokul ◽  
M. R. Srinivasan
Keyword(s):  
Longitudinal Data ◽  
Missing Values ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Joint Modeling ◽  
Model Framework ◽  
Response Data ◽  
Count Response ◽  
Longitudinal Count Data

Joint modeling in longitudinal data is an interesting area of research since it predicts the outcome with covariates that are measured repeatedly over the time. However, there is no proper methodology available in literature to incorporate the joint modeling approach for count-count response data. In addition, there are several situations where longitudinal data might not be possible to collect the complete data and the Missingness may occur due to the absence of the subjects at the follow-up. In this paper, joint modelling for longitudinal count data is adopted using Bayesian Generalized Linear Mixed Model framework to understand the association between the variables. Further, an imputation method is used to handle the missing entries in the data and the efficiency of the methodology has been studied using Markov Chain Monte-Carlo (MCMC) technique. An application to the proposed methodology has been discussed and identified the suitable nutritional supplements in Bayesian perspective without eliminating the missing entries in the dataset.

scholarly journals MTG2: An efficient algorithm for multivariate linear mixed model analysis based on genomic information

2015 ◽  
Author(s):  
Sang Hong Lee ◽  
Julius van der Werf
Keyword(s):  
Complex Traits ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Covariance Structure ◽  
Random Regression ◽  
Current Standard ◽  
Model Framework ◽  
Genetic Covariance ◽  
Mixed Model Analysis ◽  
Mixed Model Equation

We have developed an algorithm for genetic analysis of complex traits using genome-wide SNPs in a linear mixed model framework. Compared to current standard REML software based on the mixed model equation, our method could be more than 1000 times faster. The advantage is largest when there is only a single genetic covariance structure. The method is particularly useful for multivariate analysis, including multi-trait models and random regression models for studying reaction norms. We applied our proposed method to publicly available mice and human data and discuss advantages and limitations.

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