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Handbook of regression analysis
\"Written by two established experts in the field, the purpose of this handbook is to provide a practical, one-stop reference on regression analysis. The focus is on the tools that both practitioners and researchers use in real life. It is intended to be a comprehensive collection of the theory, methods, and applications of the subject matter, but it is deliberately written at an accessible level. The handbook will provide a quick and convenient reference or \"refresher\" on ideas and methods that are useful for the accurate analysis of data and its resulting interpretations. Students can use the book as an introduction to and/or summary of key concepts in regression and related course work (such as linear, nonlinear, and nonparametric regressions). Plentiful references are supplied for the more motivated readers. Theory is presented when necessary, and always supplemented by hands-on examples. Software routines are available via an author-maintained web site\"-- Provided by publisher.
Book
Enhanced Bayesian model for multienvironmental selection of winter hybrids maize: assessing grain yield using ‘ProbBreed’
Background
Crossover interactions stemming from phenotypic plasticity complicate selection decisions when evaluating hybrid maize with superior grain yield and consistent performance. Consequently, a two-year, region-wide investigation of 45 hybrids maize across Nepal was performed with the aim of disclosing both site and wide adapted hybrids. Utilizing an innovative “ProbBreed” package, based on Bayesian probability analysis of randomized complete block designs with three replicated trials at each station, this study substantively streamlines hybrids maize selection.
Results
This finding revealed substantial genetic, environmental, and interactive influences on grain yield (
p
< 0.05). Among the hybrids, DKC9149 (8.8 tons/ha) emerged as the elite with probability coefficient of (0.39), followed by NK6607(0.35 & 8.6 tons/ha). Joint probability analysis identified RMH1899 super (0.23 & 8.3 tons/ha), followed by RMH 666 (0.15 & 8.4 tons/ha) and Uttam 121 (0.11 & 8.6 tons/ha), all of which accounted for overall environmental conditions. Additionally, over the years, DKC 9149, NK 6607(0.18 & 8.6 tons/ha), GK 3254(0.18 & 8.5 tons/ha), Shann 111(0.12 & 8.4 tons/ha), Sweety 1(0.13 & 8.4 tons/ha), and ADV 756(0.10 & 8.2 tons/ha) consistently demonstrated superior performance and stability. Delving with site specific recommendations include Nepalgunj: RMH 9999(8.5 tons/ha), NK 6607(8.6 tons/ha); Parwanipur: DKC 9149, MM 2033(8.5 tons/ha); Rampur: ADV 756, DKC 9149, MM 2929(8.6 tons/ha); and Tarahara: GK 3254(8.5 tons/ha), NK 6607(8.6 tons/ha), Uttam 121.
Conclusion
Thus, Selected hybrids are predicted to outperform within the recommended domain. Over and above, integrating genomic information into Bayesian models expected to enhance prediction accuracy and expedite breeding progress.
Journal Article
Joint Flood Risks in the Grand River Watershed
According to the World Meteorological Organization, since 2000, there has been an increase in global flood-related disasters by 134 percent compared to the previous decades. Efficient flood risk management strategies necessitate a holistic approach to evaluating flood vulnerabilities and risks. Catastrophic losses can occur when the peak flow values in the rivers in a basin coincide. Therefore, estimating the joint flood risks in a region is vital, especially when frequent occurrences of extreme events are experienced. This study focuses on estimating the joint flood risks due to river flow extremes in the Grand River watershed in Canada. For this purpose, the study uses copula analysis to investigate the joint occurrence of extreme river flow events in the Speed and Grand Rivers in the Grand River Watershed in Ontario, Canada. By estimating the joint return period for extreme flows in both rivers, we demonstrate the interdependence of the two river flows and how this interdependence influences the behavior of river flow extreme patterns. Our findings suggest that the interdependence between the two river flows leads to changes in the river flow extreme pattern. Determining the interdependence of floods at multiple locations using state-of-the-art tools will benefit various stakeholders, such as the insurance industry, the disaster management sector, and most importantly, the public.
Journal Article
Stochastic finance : an introduction with market examples
\"This comprehensive text presents an introduction to pricing and hedging in financial models, with an emphasis on analytical and probabilistic methods. It demonstrates both the power and limitations of mathematical models in finance. The book starts with the basics of finance and stochastic calculus and builds up to special topics, such as options, derivatives, and credit default and jump processes. Many real examples illustrate the topics and classroom-tested exercises are included in each chapter, with selected solutions at the back of the book\"-- Provided by publisher.
Book
Balancing Covariates via Propensity Score Weighting
Covariate balance is crucial for unconfounded descriptive or causal comparisons. However, lack of balance is common in observational studies. This article considers weighting strategies for balancing covariates. We define a general class of weights-the balancing weights-that balance the weighted distributions of the covariates between treatment groups. These weights incorporate the propensity score to weight each group to an analyst-selected target population. This class unifies existing weighting methods, including commonly used weights such as inverse-probability weights as special cases. General large-sample results on nonparametric estimation based on these weights are derived. We further propose a new weighting scheme, the overlap weights, in which each unit's weight is proportional to the probability of that unit being assigned to the opposite group. The overlap weights are bounded, and minimize the asymptotic variance of the weighted average treatment effect among the class of balancing weights. The overlap weights also possess a desirable small-sample exact balance property, based on which we propose a new method that achieves exact balance for means of any selected set of covariates. Two applications illustrate these methods and compare them with other approaches.
Journal Article
Impact of Climate Change on the Stability of the Miacher Slope, Upper Hunza, Gilgit Baltistan, Pakistan
Especially in recent years, the study of landslide phenomena is considered as very important because of the effects of climate change. The aim of this paper is to examine the stability of the slope located in Miacher Nagar village along the Hunza River (HR), using the Limit Equilibrium Method (LEM). The Miacher slope rises to a height of 900 m from the foot of the Hunza River and has a base angle of 50 degrees. Meta-sediments and quaternary recent glaciated deposits make up the majority of the slope’s composition. The slope movement of Miacher was first triggered in 1995, and was further triggered in 2010 and 2013. The slope was geologically, geomorphologically, geotechnically and geochemically investigated as well as modeled by Slope/w to determine the safety factor. Soil samples were analyzed for their geotechnical, geological and geomorphological properties. The Limit Equilibrium Method (LEM) was employed in this study to analyze the Factor of Safety (FOS) of the slope, based on assumptions of the Morgenstern and Price, Ordinary, Janbu and Bishop Methods, using the Slope/w software. Various factors, including pore water pressure, unit weight, cohesion, angle of internal friction and overburden, were examined by analyzing different scenarios. The findings showed that an increase in cohesion and angle of internal friction resulted in an increase in FOS, whereas an increase in unit weight and overburden caused a decrease in FOS. The influence of pore water pressure was positive to a certain extent, but a further increase led to a significant reduction in FOS. The results showed that the Miacher slope is currently stable, as all FOS values were greater than one, based on the existing strength parameters and simulated results obtained using Slope/w.
Journal Article
MatHaz: a Matlab code to assist with probabilistic spatio-temporal volcanic hazard assessment in distributed volcanic fields
This paper introduces an open source computer code to perform an integrated probabilistic spatio-temporal volcanic hazard assessment in distributed volcanic fields. The program, named MatHaz, is a set of Matlab scripts that follows a sequential methodology. After the user has provided a set of input files, this tool first estimates the spatial probability of future volcanic vents, then the temporal probability of future volcanic events, and finally models up to five volcanic phenomena (pyroclastic density currents, ballistic projectiles, lava flows, lahars, and tephra fallout) following a probabilistic approach. These results can be combined and depicted as an integrated quantitative (and/or qualitative) volcanic hazard map, with weightings of hazard factors chosen by the user. We illustrate the use of this tool by applying it to the Carrán-Los Venados Volcanic Field in southern Chile. The open-source, replicable, and user-friendly nature of the code allows its application to any volcanic region of the world, regardless of its extent, type, and amount of volcano-structural data.
Journal Article
The Spectre of Too Many Species
Recent simulation studies examining the performance of Bayesian species delimitation as implemented in the BPP program have suggested that BPP may detect population splits but not species divergences and that it tends to over-split when data of many loci are analyzed. Here, we confirm these results and provide the mathematical justifications. We point out that the distinction between population and species splits made in the protracted speciation model (PSM) has no influence on the generation of gene trees and sequence data, which explains why no method can use such data to distinguish between population splits and speciation. We suggest that the PSM is unrealistic as its mechanism for assigning species status assumes instantaneous speciation, contradicting prevailing taxonomic practice. We confirm the suggestion, based on simulation, that in the case of speciation with gene flow, Bayesian model selection as implemented in BPP tends to detect population splits when the amount of data (the number of loci) increases. We discuss the use of a recently proposed empirical genealogical divergence index (gdi) for species delimitation and illustrate that parameter estimates produced by a full likelihood analysis as implemented in BPP provide much more reliable inference under the gdi than the approximate method PHRAPL. We distinguish between Bayesian model selection and parameter estimation and suggest that the model selection approach is useful for identifying sympatric cryptic species, while the parameter estimation approach may be used to implement empirical criteria for determining species status among allopatric populations.
Journal Article
Flood susceptibility mapping using integrated bivariate and multivariate statistical models
Flooding can have catastrophic effects on human lives and livelihoods and thus comprehensive flood management is needed. Such management requires information on the hydrologic, geotechnical, environmental, social, and economic aspects of flooding. The number of flood events that took place in Busan, South Korea, in 2009 exceeded the normal situation for that city. Mapping the susceptible areas helps us to understand flood trends and can aid in appropriate planning and flood prevention. In this study, a combination of bivariate probability analysis and multivariate logistic regression was used to produce flood susceptibility maps of Busan City. The main aim of this research was to overcome the weakness of logistic regression regarding bivariate probability capabilities. A flood inventory map with a total of 160 flood locations was extracted from various sources. Then, the flood inventory was randomly split into a testing dataset 70 % for training the models and the remaining 30 %, which was used for validation. Independent variables datasets included the rainfall, digital elevation model, slope, curvature, geology, green farmland, rivers, slope, soil drainage, soil effect, soil texture, stream power index, timber age, timber density, timber diameter, and timber type. The impact of each independent variable on flooding was evaluated by analyzing each independent variable with the dependent flood layer. The validation dataset, which was not used for model generation, was used to evaluate the flood susceptibility map using the prediction rate method. The results of the accuracy assessment showed a success rate of 92.7 % and a prediction rate of 82.3 %.
Journal Article