scholarly journals cRacle: R Tools for Estimating Climate from Vegetation

2019 ◽  
Author(s):  
Robert S. Harbert ◽  
Alex A. Baryiames
Keyword(s):  
Plant Community ◽  
Best Practice ◽  
Likelihood Estimation ◽  
Data Access ◽  
Alternative Methods ◽  
Model Parameters ◽  
Mean Annual Temperature ◽  
Modeling Tools ◽  
Model Correction ◽  
Community Data

ABSTRACTPremise of the studyThe Climate Reconstruction Analysis using Coexistence Likelihood Estimation (CRACLE) method for the estimation of climate from vegetation is a robust set of modeling tools for estimating climate and paleoclimate that makes use of large repositories of biodiversity data and open-access R software.MethodsHere we implement a new R library for the estimation of climate from vegetation. The ‘cRacle’ library implements functions for data access, aggregation, and models to estimate climate from plant community composition. ‘cRacle’ is modular and features many best-practice features.ResultsPerformance tests using modern vegetation survey data from North and South America shows that CRACLE outperforms alternative methods. CRACLE estimates of mean annual temperature (MAT) are usually within 1°C of the actual when optimal model parameters are used. Generalized Boosted Regression (GBR) model correction is also shown here to improve on CRACLE models by reducing bias.DiscussionCRACLE provides accurate estimates of climate from modern plant communities. Non-parametric CRACLE modeling coupled to GBR model correction produces the most accurate results to date. The ‘cRacle’ R library streamlines the estimation of climate from plant community data, and will make this modeling more accessible to a wider range of users.

scholarly journals Maximum Likelihood Estimation of the VAR(1) Model Parameters with Missing Observations

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Helena Mouriño ◽  
Maria Isabel Barão
Keyword(s):  
Stochastic Process ◽  
Missing Data ◽  
Maximum Likelihood ◽  
Moving Average ◽  
Practical Importance ◽  
Likelihood Estimation ◽  
Model Parameters ◽  
Missing Observations ◽  
Data Set ◽  
The Impact

Missing-data problems are extremely common in practice. To achieve reliable inferential results, we need to take into account this feature of the data. Suppose that the univariate data set under analysis has missing observations. This paper examines the impact of selecting an auxiliary complete data set—whose underlying stochastic process is to some extent interdependent with the former—to improve the efficiency of the estimators for the relevant parameters of the model. The Vector AutoRegressive (VAR) Model has revealed to be an extremely useful tool in capturing the dynamics of bivariate time series. We propose maximum likelihood estimators for the parameters of the VAR(1) Model based on monotone missing data pattern. Estimators’ precision is also derived. Afterwards, we compare the bivariate modelling scheme with its univariate counterpart. More precisely, the univariate data set with missing observations will be modelled by an AutoRegressive Moving Average (ARMA(2,1)) Model. We will also analyse the behaviour of the AutoRegressive Model of order one, AR(1), due to its practical importance. We focus on the mean value of the main stochastic process. By simulation studies, we conclude that the estimator based on the VAR(1) Model is preferable to those derived from the univariate context.

scholarly journals The Weibull Birnbaum-Saunders Distribution And Its Applications

2020 ◽  
Vol 9 (1) ◽  
pp. 61-81
Author(s):  
Lazhar BENKHELIFA
Keyword(s):  
Maximum Likelihood ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Real Data ◽  
Reliability Estimation ◽  
Maximum Likelihood Estimates ◽  
Model Parameters ◽  
Data Sets ◽  
Proposed Model ◽  
Modeling Data

A new lifetime model, with four positive parameters, called the Weibull Birnbaum-Saunders distribution is proposed. The proposed model extends the Birnbaum-Saunders distribution and provides great flexibility in modeling data in practice. Some mathematical properties of the new distribution are obtained including expansions for the cumulative and density functions, moments, generating function, mean deviations, order statistics and reliability. Estimation of the model parameters is carried out by the maximum likelihood estimation method. A simulation study is presented to show the performance of the maximum likelihood estimates of the model parameters. The flexibility of the new model is examined by applying it to two real data sets.

scholarly journals Technical Efficiency and Small Scale Maize Producers in Mwanza Region: A Stochastic Frontier Analysis

2021 ◽  
Vol 9 (3) ◽  
Author(s):  
Mukole Kongolo
Keyword(s):  
Technical Efficiency ◽  
Best Practice ◽  
Stochastic Frontier Analysis ◽  
Stochastic Frontier ◽  
Likelihood Estimation ◽  
Estimation Procedure ◽  
Farm Size ◽  
Small Scale ◽  
Maize Production ◽  
Frontier Analysis

This study measured technical efficiency and its determinants in maize production by small-scale producers in Mwanza region, using a stochastic frontier production function approach. A randomly selected sample of participants in the two districts was used. The Maximum Likelihood estimation procedure was followed to obtain the determinants of technical efficiency and technical efficiency levels of small-scale maize producers. The minimum and maximum values of technical efficiency were between 20% and 91%, indicating that the least practices of specific producer operates at a minimum level of 20%, while the best practice producers  operate  at 91% technical efficiency  level respectively. The summary results of the mean technical efficiency was 63%. The main determinants of technical efficiency were labour, farm size, producer’s experience, producer’s age, family size which were all positive and statistically significant. The findings suggest that the average efficiency of small-scale maize producers could be improved by 37% through better use of existing resources and technology. These findings highlight the need for action by government to assist small-scale maize producers improve efficiency.

Driver Reactions to Uphill Grades: Inference from a Stochastic Car-Following Model

2020 ◽  
Vol 2674 (11) ◽  
pp. 343-351 ◽  
Author(s):  
Tu Xu ◽  
Jorge Laval
Keyword(s):  
Maximum Likelihood ◽  
Likelihood Estimation ◽  
Truck Drivers ◽  
Model Parameters ◽  
Gravitational Effects ◽  
Car Following ◽  
Car Following Model ◽  
Operational Impacts ◽  
The Impact ◽  
Engine Power

This paper analyzes the impact of uphill grades on the acceleration drivers choose to impose on their vehicles. Statistical inference is made based on the maximum likelihood estimation of a two-regime stochastic car-following model using Next Generation SIMulation (NGSIM) data. Previous models assume that the loss in acceleration on uphill grades is given by the effects of gravity. We find evidence that this is not the case for car drivers, who tend to overcome half of the gravitational effects by using more engine power. Truck drivers only compensate for 5% of the loss, possibly because of limited engine power. This indicates not only that current models are severely overestimating the operational impacts that uphill grades have on regular vehicles, but also underestimating their environmental impacts. We also find that car-following model parameters are significantly different among shoulder, median and middle lanes but more data is needed to understand clearly why this happens.

scholarly journals Joint Maximum Likelihood of Phylogeny and Ancestral States Is Not Consistent

2019 ◽  
Vol 36 (10) ◽  
pp. 2352-2357
Author(s):  
David A Shaw ◽  
Vu C Dinh ◽  
Frederick A Matsen
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Likelihood Estimation ◽  
Branch Length ◽  
Continuous Model ◽  
Model Parameters ◽  
Infinite Length ◽  
Computationally Efficient ◽  
Ancestral States ◽  
Joint Method

Abstract Maximum likelihood estimation in phylogenetics requires a means of handling unknown ancestral states. Classical maximum likelihood averages over these unknown intermediate states, leading to provably consistent estimation of the topology and continuous model parameters. Recently, a computationally efficient approach has been proposed to jointly maximize over these unknown states and phylogenetic parameters. Although this method of joint maximum likelihood estimation can obtain estimates more quickly, its properties as an estimator are not yet clear. In this article, we show that this method of jointly estimating phylogenetic parameters along with ancestral states is not consistent in general. We find a sizeable region of parameter space that generates data on a four-taxon tree for which this joint method estimates the internal branch length to be exactly zero, even in the limit of infinite-length sequences. More generally, we show that this joint method only estimates branch lengths correctly on a set of measure zero. We show empirically that branch length estimates are systematically biased downward, even for short branches.

scholarly journals Novel methods of estimating relative pollen productivity: A key parameter for reconstruction of past land cover from pollen records

2019 ◽  
Vol 43 (6) ◽  
pp. 731-753 ◽  
Author(s):  
Yiman Fang ◽  
Chunmei Ma ◽  
M Jane Bunting
Keyword(s):  
Land Cover ◽  
Empirical Data ◽  
Alternative Methods ◽  
Model Parameters ◽  
Value Analysis ◽  
Pollen Productivity ◽  
Tropical China ◽  
The Relationship ◽  
Pollen Records ◽  
Key Parameter

Reconstructing land cover from pollen data using mathematical models of the relationship between them has the potential to translate the many thousand pollen records produced over the last 100 years (over 2300 radiocarbon-dated pollen records exist for the UK alone) into formats relevant to ecologists, archaeologists and climate scientists. However, the reliability of these reconstructions depends on model parameters. A key parameter is Relative Pollen Productivity (RPP), usually estimated from empirical data using ‘Extended R Value analysis’ (ERV analysis). Lack of RPP estimates for many regions is currently a major limitation on reconstructing global land cover. We present two alternatives to ERV analysis, the Modified Davis Method and an iteration method, which use the same underlying model of the relationship between pollen and vegetation to estimate RPP from empirical data, but with different assumptions. We test them in simulation against ERV analysis, and use a case study of a problematic empirical dataset to determine whether they have the potential to increase the speed and geographic range of RPP estimation. The two alternative methods are shown to perform at least as well as ERV analysis in simulation. We also present new RPP estimates from southeastern sub-tropical China for nine taxa estimated using the Modified Davis Method. Adding these two methods to the ‘toolkit’ for land cover reconstruction from pollen records opens up the possibility to estimate a key parameter from existing datasets with less field time than using current methods. This can both speed up the inclusion of more of the globe in past land cover mapping exercises such as the PAGES Landcover6k working group and improve our understanding of how this parameter varies within a single taxon and the factors control that variation.

scholarly journals A New Flexible Bathtub-Shaped Modification of the Weibull Model: Properties and Applications

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Qinghu Liao ◽  
Zubair Ahmad ◽  
Eisa Mahmoudi ◽  
G. G. Hamedani
Keyword(s):  
Weibull Distribution ◽  
Hazard Rate ◽  
Likelihood Estimation ◽  
Estimation Procedure ◽  
Weibull Model ◽  
Rate Function ◽  
Model Parameters ◽  
Hazard Functions ◽  
Practical Applications ◽  
Nonnested Models

Many studies have suggested the modifications and generalizations of the Weibull distribution to model the nonmonotone hazards. In this paper, we combine the logarithms of two cumulative hazard rate functions and propose a new modified form of the Weibull distribution. The newly proposed distribution may be called a new flexible extended Weibull distribution. Corresponding hazard rate function of the proposed distribution shows flexible (monotone and nonmonotone) shapes. Three different characterizations along with some mathematical properties are provided. We also consider the maximum likelihood estimation procedure to estimate the model parameters. For the illustrative purposes, two real applications from reliability engineering with bathtub-shaped hazard functions are analyzed. The practical applications show that the proposed model provides better fits than the other nonnested models.

Time-domain-induced polarization: Full-decay forward modeling and 1D laterally constrained inversion of Cole-Cole parameters

Geophysics ◽  
2012 ◽  
Vol 77 (3) ◽  
pp. E213-E225 ◽  
Author(s):  
Gianluca Fiandaca ◽  
Esben Auken ◽  
Anders Vest Christiansen ◽  
Aurélie Gazoty
Keyword(s):  
Time Domain ◽  
Mineral Exploration ◽  
Induced Polarization ◽  
Significant Loss ◽  
Step Response ◽  
Model Parameters ◽  
Full Time ◽  
Inversion Algorithm ◽  
Modeling Tools ◽  
Polarization Response

Time-domain-induced polarization has significantly broadened its field of reference during the last decade, from mineral exploration to environmental geophysics, e.g., for clay and peat identification and landfill characterization. Though, insufficient modeling tools have hitherto limited the use of time-domain-induced polarization for wider purposes. For these reasons, a new forward code and inversion algorithm have been developed using the full-time decay of the induced polarization response, together with an accurate description of the transmitter waveform and of the receiver transfer function, to reconstruct the distribution of the Cole-Cole parameters of the earth. The accurate modeling of the transmitter waveform had a strong influence on the forward response, and we showed that the difference between a solution using a step response and a solution using the accurate modeling often is above 100%. Furthermore, the presence of low-pass filters in time-domain-induced polarization instruments affects the early times of the acquired decays (typically up to 100 ms) and has to be modeled in the forward response to avoid significant loss of resolution. The developed forward code has been implemented in a 1D laterally constrained inversion algorithm that extracts the spectral content of the induced polarization phenomenon in terms of the Cole-Cole parameters. Synthetic examples and field examples from Denmark showed a significant improvement in the resolution of the parameters that control the induced polarization response when compared to traditional integral chargeability inversion. The quality of the inversion results has been assessed by a complete uncertainty analysis of the model parameters; furthermore, borehole information confirm the outcomes of the field interpretations. With this new accurate code in situ time-domain-induced polarization measurements give access to new applications in environmental and hydrogeophysical investigations, e.g., accurate landfill delineation or on the relation between Cole-Cole and hydraulic parameters.

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