How standard are standardized drought indices? Uncertainty contributions for the SPI & SPEI case

2020 ◽  
Author(s):  
Johannes Laimighofer ◽  
Gregor Laaha
Keyword(s):  
Mixed Model ◽  
Linear Mixed Model ◽  
Estimation Method ◽  
Drought Indices ◽  
Drought Severity ◽  
Main Question ◽  
Data Set ◽  
Total Uncertainty ◽  
Sources Of Uncertainty ◽  
Shed Light

<p><span lang="en-US">Standardized drought indices such as SPI are frequently used around the world to assess drought severity across a continent or a larger region covering different meteorological regimes. But how standard are the standardized indices? In this paper we quantify the uncertainty of SPI and SPEI based on an Austrian data set to shed light on what are the main sources of uncertainty in the study area. Here we analyze the uncertainty contributions by a linear mixed model that employs a restrictive maximum likelihood estimator in order to produce unbiased variance and covariance components. Five factors that either defy the control of the analyst (record length, observation period), or need to be subjectively decided during the steps of the calculation (choice of the distribution, parameter estimation method, and GOF-test of the fitted distribution) are considered. The results show that, overall, the choice of the distribution and the observational window are the most important sources of uncertainty. We quantify the relative uncertainty contributions in greater detail in order to give guidance how to make estimates most accurate for a given data set. We finally analyze the total uncertainty of SPI and SPEI to shed light on our main question whether the indices are skillful enough to provide a quantification of atmospheric drought that is standardized enough to allow the intended comparisons across various data situations and meteorological regimes. </span></p>

scholarly journals Categorical Visual Score Traits of a Nellore Beef Cattle Population

2017 ◽  
Vol 9 (8) ◽  
pp. 63
Author(s):  
Jairo Azevedo Junior ◽  
Juliana Petrini ◽  
Gerson Barreto Mourão ◽  
José Bento Sterman Ferraz
Keyword(s):  
Variance Components ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Genetic Correlations ◽  
Estimation Method ◽  
Analysis Data ◽  
Suitable Model ◽  
Linear Distribution ◽  
Tropical Regions ◽  
Trait Analysis

Variance components and genetic parameters of economically relevant traits in livestock, whether continuous or categorical, can be estimated by methods computationally available providing support for the selection and mating of animals in breeding programs. The objectives of this paper were to obtain and compare the variance components estimates for visual traits under continuous or categorical distribution in single-trait analysis and their correlations with continuous productive traits in two-trait analysis. Data of conformation (CONF), precocity of fat deposition (PREC) and muscling (MUSC) visual scores evaluated at 18 months of age as well as the weight at 18 months of age (YW) were collected from animals born from 2000 to 2012, in Nellore cattle herds raised in Southeastern and Central Western tropical regions of Brazil. Methods III of Henderson, Restricted Maximum Likelihood (REML), Bayesian Inference and generalized linear mixed model (GLMM) were tested. Variance components obtained from single-trait analysis were similar to those obtained from two-trait analysis. The estimates of heritability (h2) for the visual scores ranged from 0.1081 to 0.2190. Heritability estimates for traits evaluated by visual scores have moderate to high magnitude justifying the inclusion of visual scores as selection criteria in animal breeding and the selection of animals with higher scores for mating. High genetic correlations between yearling weight and morphological traits were verified. For visual scores of conformation, precocity and muscling, the most suitable model based on one-trait or two-trait analyses considered an animal model, a linear distribution of the data and the estimation method of the components of (co)variance based on Bayesian methodology.

scholarly journals Prediction of Missing Values in Microarray and Use of Mixed Models to Evaluate the Predictors

2005 ◽  
Vol 4 (1) ◽  
Author(s):  
Guri Feten ◽  
Trygve Almøy ◽  
Are H. Aastveit
Keyword(s):  
Gene Expression ◽  
Missing Values ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Correlation Structure ◽  
Data Sets ◽  
Data Set ◽  
Nearest Neighbours ◽  
Complete Matrix ◽  
The Ideal

Gene expression microarray experiments generate data sets with multiple missing expression values. In some cases, analysis of gene expression requires a complete matrix as input. Either genes with missing values can be removed, or the missing values can be replaced using prediction. We propose six imputation methods. A comparative study of the methods was performed on data from mice and data from the bacterium Enterococcus faecalis, and a linear mixed model was used to test for differences between the methods. The study showed that different methods' capability to predict is dependent on the data, hence the ideal choice of method and number of components are different for each data set. For data with correlation structure methods based on K-nearest neighbours seemed to be best, while for data without correlation structure using the average of the gene was to be preferred.

International Swimming League: Do Successive Events Lead to Improve Swimming Performance?

2021 ◽  
pp. 1-4
Author(s):  
Robin Pla ◽  
Arthur Leroy ◽  
Yannis Raineteau ◽  
Philippe Hellard
Keyword(s):  
Mixed Model ◽  
Linear Mixed Model ◽  
Swimming Performance ◽  
Support Staff ◽  
The Other ◽  
Data Set ◽  
Age And Gender ◽  
World Class ◽  
And Gender ◽  
The Impact

Purpose: To quantify the impact of successive competitions on swimming performance in world-class swimmers. Methods: An entire data set of all events swum during a new competition named the International Swimming League was collected. A Bayesian linear mixed model has been proposed to evaluate whether a progression could be observed during the International Swimming League’s successive competitions and to quantify this effect according to event, age, and gender. Results: An overall progression of 0.0005 (0.0001 to 0.0010) m/s/d was observed. The daily mean progression (ie, faster performance) was twice as high for men as for women (0.0008 [0.00 to 0.0014] vs 0.0003 [−0.0003 to 0.0009] m·s−1). A tendency toward higher progression for middle distances (200 and 400 m) and for swimmers of a higher caliber (above 850 FINA [Fédération Internationale de Natation] points) was also observed. Swimmers between 23 and 26 years of age seemed to improve their swimming speed more in comparison with the other swimmers. Conclusions: This new league format, which involves several competitions in a row, seems to allow for an enhancement in swimming performance. Coaches and their support staff can now adapt their periodization plan in order to promote competition participation.

scholarly journals Estimation of Effects of Quantitative Trait Loci in Large Complex Pedigrees

Genetics ◽  
1997 ◽  
Vol 146 (1) ◽  
pp. 409-416 ◽  
Author(s):  
T H E Meuwissen ◽  
M E Goddard
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Simulated Data ◽  
Data Set ◽  
Genotype Information ◽  
Trait Loci ◽  
Basic Equations ◽  
Marker Information

A method was derived to estimate effects of quantitative trait loci (QTL) using incomplete genotype information in large outbreeding populations with complex pedigrees. The method accounts for background genes by estimating polygenic effects. The basic equations used are very similar to the usual linear mixed model equations for polygenic models, and segregation analysis was used to estimate the probabilities of the QTL genotypes for each animal. Method R was used to estimate the polygenic heritability simultaneously with the QTL effects. Also, initial allele frequencies were estimated. The method was tested in a simulated data set of 10,000 animals evenly distributed over 10 generations, where 0, 400 or 10,000 animals were genotyped for a candidate gene. In the absence of selection, the bias of the QTL estimates was <2%. Selection biased the estimate of the Aa genotype slightly, when zero animals were genotyped. Estimates of the polygenic heritability were 0.251 and 0.257, in absence and presence of selection, respectively, while the simulated value was 0.25. Although not tested in this study, marker information could be accommodated by adjusting the transmission probabilities of the genotypes from parent to offspring according to the marker information. This renders a QTL mapping study in large multi-generation pedigrees possible.

scholarly journals Multi Task Deep Learning for Genomic Predictions

2021 ◽  
Author(s):  
Baohong Guo
Keyword(s):  
Deep Learning ◽  
Ridge Regression ◽  
Prediction Accuracy ◽  
Large Scale ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Moderate Increase ◽  
Data Set ◽  
Test Set ◽  
Genomic Predictions

ABSTRACTGenomic predictions have been recognized as a new promising technique in animal and plant breeding. Linear mixed model is a widely used statistical technique, but it may not be desirable for large training sets and number of molecular markers, because it is intensive in computation. Deep learning is a subfield of machine learning and it can be used for complex predictions on a large scale. Multi task deep learning (MT-DL) incorporates related tasks(labels or traits) into one learning process to enable the learning model to perform better than single task deep learning (ST-DL). I applied MT-DL to genotype by environment genomic predictions to predict the performances of breeding lines at multiple environments. I compared MT-DL with linear mixed model-based Bayesian genotype × environment method (BGGE) and separate genomic predictions on single environments with widely used rrBLUP, ridge regression and ST-DL using cross validations. Compared with rrBLUP, MT-DL and non-linear BGGE showed a moderate increase of 9.4 and 7.6%, respectively, ST-DL has a small increase of 5.4%, ridge regression had a similar prediction accuracy and linear BGGE had a small decrease of −2.0% for prediction accuracy. I also found that all methods including rrBLUP had an overfitting, this is likely because yield genomic predictions are complex and the data set used in this study are small. rrBLUP, ridge regression, ST-DL and MT-DL has similar overfitting. Difference between training and test set prediction accuracies was between 0.344 and 0. 387. Linear and nonlinear BGGE methods seem to have much worse overfitting than other methods. Difference between training and test set prediction accuracies were 0.429 and 0.472, respectively. I also discussed the potential applications of ST-DL and MT-DL in genomic predictions of hybrid crops such as maize

scholarly journals Comprehensive Drought Analysis Using Statistical and Meteorological Indices Approach: A Case Study of Badin, Sindh

2019 ◽  
pp. 594-604
Author(s):  
Aisha Akber ◽  
Syed Feroz Shah ◽  
Muhammad Wajid Ijaz ◽  
Hira Soomro ◽  
Nimra Alam ◽  
...  
Keyword(s):  
Arid Zone ◽  
Standardized Precipitation Index ◽  
Drought Indices ◽  
Drought Severity ◽  
Climate Projections ◽  
Arid Zones ◽  
Data Set ◽  
Drought Analysis ◽  
Drought Prediction ◽  
Average Annual Temperature

Drought is a global phenomenon that can occur in any ecological zone and render significant damages to both the natural environment and human lives. However, hydro-climatic stresses are growing distinctly in the arid zones across the globe. Literature suggests that the analysis of a long-term data-set could help in strengthening of mitigation planes and rationalization of disaster management policies. Thus, the present study is aimed to analyze the evidence-based historical drought events happened in arid-zone Badin, Pakistan and predict its occurrence and severity for the next 82 years (2018-2099). Drought indices viz standardized precipitation index and reconnaissance drought index have been used to detect the severity of the drought events. Thirty years (1988 to 2017) past data of precipitation and temperature were used to categorize the drought severity and validated against the local data. Climate projections based on RCP 4.5 and 8.5 made at 25x25 km resolution used for future drought analysis. The results demonstrate that the region faced severe to extreme drought in 1990-91 and 2001-04. While, in future 2020-21, 2036-37, 2038-39 would be the extreme driest years under RCP 4.5 and 2029-30, 2089-90 under RCP 8.5. Further insight revealed that the average annual temperature has increased and precipitation has decreased w.r.t the base year 1988. It is concluded that drought detection with SPI and RDI is suitable and drought prediction with the RCP 4.5 and 8.5 could be a better option.

scholarly journals A Small Area Estimation Method for Investigating the Relationship between Public Perception of Drug Problems with Neighborhood Prognostics: Trends in London between 2012 and 2019

2021 ◽  
Vol 18 (17) ◽  
pp. 9016
Author(s):  
Arun Sondhi ◽  
Alessandro Leidi ◽  
Emily Gilbert
Keyword(s):  
Public Perception ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Statistical Significance ◽  
Local Population ◽  
Random Effect ◽  
Gaussian Quadrature ◽  
Estimation Method ◽  
Likelihood Method ◽  
Drug Problems

The correlation of the public’s perception of drug problems with neighborhood characteristics has rarely been studied. The aim of this study was to investigate factors that correlate with public perceptions in London boroughs using the Mayor’s Office for Policing and Crime (MOPAC) Public Attitude Survey between 2012 and 2019. A subject-specific random effect deploying a Generalized Linear Mixed Model (GLMM) using an Adaptive Gaussian Quadrature method with 10 integration points was applied. To obtain time trends across inner and outer London areas, the GLMM was fitted using a Restricted Marginal Pseudo Likelihood method. The perception of drug problems increased with statistical significance in 17 out of 32 London boroughs between 2012 and 2019. These boroughs were geographically clustered across the north of London. Levels of deprivation, as measured by the English Index of Multiple Deprivation, as well as the percentage of local population who were non-UK-born and recorded vehicle crime rates were shown to be positively associated with the public’s perception of drug problems. Conversely, recorded burglary rate was negatively associated with the public’s perception of drug problems in their area. The public are influenced in their perception of drug problems by neighborhood factors including deprivation and visible manifestations of antisocial behavior.

scholarly journals Bayesian analysis of additive epistasis arising from new mutations in mice

2014 ◽  
Vol 96 ◽  
Author(s):  
JOAQUIM CASELLAS ◽  
DANIEL GIANOLA ◽  
JUAN F. MEDRANO
Keyword(s):  
Mixed Model ◽  
Linear Mixed Model ◽  
Inbred Mice ◽  
Deviance Information Criterion ◽  
Information Criterion ◽  
Inbred Strains ◽  
Data Set ◽  
Biological Relevance ◽  
Interaction Terms ◽  
New Mutations

SummaryThe continuous uploading of polygenic additive mutational variability has been reported in several studies in laboratory species with an inbred genetic background. These studies have focused on the direct contribution of new mutations without considering the possibility of epistatic effects derived from the interaction of new mutations with pre-existing polymorphisms. In this work we focused on this main topic and analysed the statistical and biological relevance of the epistatic variance for 9 week body weight in two populations of inbred mice. We developed a new linear mixed model parameterization where founder-related additive genetic variability, additive mutational variability and the interaction terms between both sources of variation were accounted for under a Bayesian design and without requiring the inversion of a matrix of epistatic genetic covariances. The analyses focused on a six-generations data set from C57BL/6J mice (n = 3736) and a five-generations data set from C57BL/6Jhg/hg mice (n = 2843). The deviance information criterion (DIC) clearly favoured the model accounting for epistatic variability with reductions larger than 50 DIC units in both populations. Modal estimates for founder related, mutational and epistatic heritabilities were 0·068, 0·011 and 0·095 in C57BL/6J and 0·060, 0·010 and 0·113 in C57BL/6Jhg/hg, ruling out any doubt about the biological relevance of epistasis originating from new mutations in mice. These results contribute new insights on the relevance of epistasis in the genetic architecture of mammals and serve as an important component of an additional source of genetic heterogeneity for inbred strains of laboratory mice.

scholarly journals The Green Toad Example: A Comparison of Pattern Recognition Software

2020 ◽  
Author(s):  
Stephan Burgstaller ◽  
Günter Gollmann ◽  
Lukas Landler
Keyword(s):  
Pattern Recognition ◽  
Population Size ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Cost Effective ◽  
Individual Identification ◽  
Data Set ◽  
Naturally Occurring ◽  
Green Toad ◽  
Recognition Software

Abstract Background Individual identification of animals is important for assessing the size and status of populations. Photo-based approaches, where animals are recognized by naturally occurring and visually identifiable features, such as color patterns, are cost-effective methods for this purpose. We compared five available programs for their power to semi-automatically identify dorsal patterns of the European green toad (Bufotes viridis). Method We created a data set of 200 pictures of known identity, two pictures for each individual, and analyzed the percentage of correctly identified animals for each software. Furthermore, we employed a generalized linear mixed model to identify important factors contributing to correct identifications. We used these results to estimate the population size of our hypothetical population. Conclusions The freely available HotSpotter application was the software which performed by far the best for our green toad example, identifying close to 100% of the photos correctly. The animals’ sex highly significantly influenced detection probability, presumably because of sex-specific differences in the pattern contrast. Population estimates were close to the expected 100 for HotSpotter, but for the other applications population size was highly overestimated. Given the clarity of our results we strongly recommend the HotSpotter software, which is a highly efficient tool for individual pattern recognition.

Value co-creation on social media

2018 ◽  
Vol 30 (4) ◽  
pp. 2153-2174 ◽  
Author(s):  
Shan Lin ◽  
Shuai Yang ◽  
Minghui Ma ◽  
Jian Huang
Keyword(s):  
Social Media ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Advertising Effectiveness ◽  
Data Set ◽  
Content Type ◽  
Display Advertising ◽  
Social Media Networks ◽  
Brand Engagement ◽  
Social Media Platforms

Purpose In recent years, hotels in China have been interested in leveraging social media platforms to facilitate interactions with and among consumers. Such brand engagement efforts on social media networks are believed to promote brands through co-creation of consumer experiences and values. This study was conducted in the context of Chinese hotels. The paper aims to identify two forms of brand engagement via social media platforms – consumer-initiated engagement and firm-initiated engagement – and to examine their effects on hotels’ display advertising effectiveness. Design/methodology/approach This study collected a comprehensive data set. First, the authors collected display advertisement data from two hotel chains in China. Second, the authors gathered the two hotels’ engagement data from Weibo. A generalized linear mixed model was used in data analysis. Findings The findings of the study indicate that both forms of brand engagement on social media network sites positively influence display advertising effectiveness. Moreover, for a strong brand, consumer-initiated engagement is more influential in increasing display advertising effectiveness; however, for a weak brand, firm-initiated engagement gains more clicks and conversions from advertisements. Practical implications As hotels in China continue to leverage online media platforms to reach, engage with and co-create value with potential and existing consumers, this study provides managers with insight as to how they can achieve higher advertising effectiveness by engaging with consumers on a consistent basis on social media. Originality/value This study mainly contributes to recent increasing research on engagement and value co-creation by providing a lens through which to assess the relationship between brand engagement via social media networks and online display advertising effectiveness.

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