Multivariate partial adjustment of financial ratios: a Bayesian hierarchical approach

2008 ◽  
Vol 23 (1) ◽  
pp. 43-64 ◽  
Author(s):  
Jose Luis Gallizo ◽  
Pilar Gargallo ◽  
Manuel Salvador
Keyword(s):  
Financial Ratios ◽  
Hierarchical Approach ◽  
Partial Adjustment ◽  
Bayesian Hierarchical

A multicriteria hierarchical approach for portfolio selection in a stock exchange

2021 ◽  
Vol 40 (2) ◽  
pp. 1945-1955
Author(s):  
Maria Bernal ◽  
Pavel Anselmo Alvarez ◽  
Manuel Muñoz ◽  
Ernesto Leon-Castro ◽  
Diego Alonso Gastelum-Chavira
Keyword(s):  
Decision Maker ◽  
Portfolio Selection ◽  
Stock Exchange ◽  
Multiple Criteria ◽  
Financial Ratios ◽  
Hierarchical Approach

The objective of the paper is to present a multiple criteria hierarchical process (MCHP) approach for portfolio selection in a stock exchange. One of the problems that investors usually face is which stock should be included in the portfolio. This paper helps investors answer that question, and the paper presents an MCHP approach using different criteria based on financial ratios that the decision maker (in this case, the investor) will give different weights to make a portfolio based on her preferences; different importance is given to each criterion. An example using the Mexican Stock Exchange is presented.

A Bayesian hierarchical approach to integrating historical and in-season genetic data for real-time assessment of a mixed stock fishery

2020 ◽  
Vol 77 (10) ◽  
pp. 1721-1732
Author(s):  
Lukas B. DeFilippo ◽  
Daniel E. Schindler ◽  
Kyle Shedd ◽  
Kevin L. Schaberg
Keyword(s):  
Real Time ◽  
Sockeye Salmon ◽  
Alternative Methods ◽  
Hierarchical Approach ◽  
Management Tools ◽  
Genetic Stock ◽  
Bayesian Hierarchical ◽  
Fishing Season ◽  
Management Actions ◽  
Mixed Stock

With advances in molecular genetics, it is becoming increasingly feasible to conduct genetic stock identification (GSI) to inform management actions that occur within a fishing season. While applications of in-season GSI are becoming widespread, such programs seldom integrate data from previous years, underutilizing the full breadth of information available for real-time inference. In this study, we developed a Bayesian hierarchical model that integrates historical and in-season GSI data to estimate temporal changes in the composition of a mixed stock of sockeye salmon (Oncorhynchus nerka) returning to Alaska’s Chignik watershed across the fishing season. Simulations showed that even after accounting for time constraints of transporting and analyzing genetic samples, a hierarchical approach can rapidly achieve accurate in-season stock allocation, outperforming alternative methods that rely solely on historical or in-season data by themselves. As the distribution and phenology of fish populations becomes more variable and difficult to predict under climate change, in-season management tools will likely be increasingly relied upon to protect biocomplexity while maximizing harvest opportunity in mixed stock fisheries.

scholarly journals A Bayesian hierarchical approach to estimate growth parameters from length data of narrow spread

2019 ◽  
Vol 77 (2) ◽  
pp. 613-623
Author(s):  
Shijie Zhou ◽  
Sarah Martin ◽  
Dan Fu ◽  
Rishi Sharma
Keyword(s):  
Growth Parameters ◽  
Fish Growth ◽  
Hierarchical Approach ◽  
Length Frequency ◽  
Age Classes ◽  
Bayesian Hierarchical ◽  
Gear Selectivity ◽  
Length Data ◽  
Longtail Tuna ◽  
Length Frequency Data

Abstract Estimating fish growth from length frequency data is challenging. There is often a lack of clearly separated modes and modal progression in the length samples due to a combination of factors, including gear selectivity, slowing growth with increasing age, and spatial segregation of different year classes. In this study, we present an innovative Bayesian hierarchical model (BHM) that enables growth to be estimated where there are few distinguishable length modes in the samples. We analyse and identify the modes in multiple length frequency strata using a multinormal mixture model and then integrate the modes and associated variances into the BHM to estimate von Bertalanffy growth parameters. The hierarchical approach allows the parameters to be estimated at regional levels, where they are assumed to represent subpopulations, as well as at species level for the whole stock. We carry out simulations to validate the method and then demonstrate its application to Indian Ocean longtail tuna (Thunnus tonggol). The results show that the estimates are generally consistent with the range of estimates reported in the literature, but with less uncertainty. The BHM can be useful for deriving growth parameters for other species even if the length data contain few age classes and do not exhibit modal progression.

scholarly journals Air toxics and birth defects: a Bayesian hierarchical approach to evaluate multiple pollutants and spina bifida

2015 ◽  
Vol 14 (1) ◽  
Author(s):  
Michael D Swartz ◽  
Yi Cai ◽  
Wenyaw Chan ◽  
Elaine Symanski ◽  
Laura E Mitchell ◽  
...  
Keyword(s):  
Spina Bifida ◽  
Birth Defects ◽  
Air Toxics ◽  
Hierarchical Approach ◽  
Bayesian Hierarchical ◽  
Multiple Pollutants

scholarly journals A Bayesian hierarchical approach to multirater correlated ROC analysis

2006 ◽  
Vol 25 (11) ◽  
pp. 1858-1871 ◽  
Author(s):  
Timothy D. Johnson ◽  
Valen E. Johnson
Keyword(s):  
Roc Analysis ◽  
Hierarchical Approach ◽  
Bayesian Hierarchical

scholarly journals Evolution of Human Immunodeficiency Virus Type 1 Coreceptor Usage during Antiretroviral Therapy: a Bayesian Approach

2004 ◽  
Vol 78 (20) ◽  
pp. 11296-11302 ◽  
Author(s):  
Christina M. R. Kitchen ◽  
Sean Philpott ◽  
Harold Burger ◽  
Barbara Weiser ◽  
Kathryn Anastos ◽  
...  
Keyword(s):  
Human Immunodeficiency Virus ◽  
Antiretroviral Therapy ◽  
Human Immunodeficiency Virus Type ◽  
Virus Type ◽  
Coreceptor Usage ◽  
Hierarchical Approach ◽  
Bayesian Hierarchical ◽  
Immunodeficiency Virus ◽  
Hiv 1

ABSTRACT There is substantial evidence for ongoing replication and evolution of human immunodeficiency virus type 1 (HIV-1), even in individuals receiving highly active antiretroviral therapy. Viral evolution in the presence of antiviral therapy needs to be considered when developing new therapeutic strategies. Phylogenetic analyses of HIV-1 sequences can be used for this purpose but may give rise to misleading results if rates of intrapatient evolution differ significantly. To improve analyses of HIV-1 evolution relevant to studies of pathogenesis and treatment, we developed a Bayesian hierarchical model that incorporates all available sequence data while simultaneously allowing the phylogenetic parameters of each patient to vary. We used this method to examine evolutionary changes in HIV-1 coreceptor usage in response to treatment. We examined patients whose viral populations exhibited a shift in coreceptor utilization in response to therapy. CXCR4 (X4) strains emerged in each patient but were suppressed following initiation of new antiretroviral regimens, so that CCR5-utilizing (R5) strains predominated. By phylogenetically reconstructing the evolutionary relationship of HIV-1 obtained longitudinally from each patient, it was possible to examine the origin of the reemergent R5 virus. Using our Bayesian hierarchical approach, we found that the reemergent R5 virus detectable after therapy was more closely related to the predecessor R5 virus than to the X4 strains. The Bayesian hierarchical approach, unlike more traditional methods, makes it possible to evaluate competing hypotheses across patients. This model is not limited to analyses of HIV-1 but can be used to elucidate evolutionary processes for other organisms as well.

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