Endogenous Verifiability and Optimality in Agency: A non-contingent approach

In the context of a principal-agent model where verification of an agent’s effort is endogenously determined through strategic interactions between contracting parties, we derive a necessary and suficient condition to achieve the first best with a non-contingent or incomplete contract. These conditions relate the Principal’s benefit, the Agent’s cost, the probability of winning and the cost of litigation. Also, these conditions are found to be more general than the ones established in Ishiguro (2002) within a similar setup.incomplete contracts, endogenous verifiability, expectation damages.

Propuesta de un sistema de monitoreo y supervisión remoto de calidad fisicoquímica del agua para la piscina gildemeister de la ciudad de Trujillo

El presente trabajo de investigación se centra en realizar “PROPUESTA DE UN SISTEMA DE MONITOREO Y SUPERVISIÓN DE CALIDAD FISICOQUIMICA DEL AGUA PARA LA PISCINA GILDEMEISTER DE LA CIUDAD DE TRUJILLO”. En el Primer Capítulo del presente trabajo, se aborda la problemática en el método actual del análisis y estudio de la calidad del agua realizado por la Gerencia Regional de Salud La Libertad en cuanto a la frecuencia o periodicidad con que son realizados dichos análisis, así como la garantía de que dicho análisis se realice de forma transparente, exponemos nuestro objetivo y damos a conocer la importancia de dar una solución a dicho problema. En el Segundo Capítulo se presenta el marco teórico, en el cual se muestra un sustento valido del porqué la elección de realizar una propuesta de un sistema de monitoreo y supervisión, así como también se refuerza las bases teóricas de la solución que se propone. En el tercer capítulo, se mencionan los materiales y los métodos utilizados en el desarrollo del proyecto, como también los procedimientos para la propuesta del sistema de monitoreo y supervisión de la calidad de agua físicoquímica de la piscina Gildemeister de la ciudad de Trujillo. En el cuarto y quinto capítulo, se dan los resultados de la selección final de los elementos del sistema, así como los resultados de la simulación del sistema y la discusión de los mismos. Finalmente, en el sexto, séptimo y octavo capítulo, se expone las conclusiones a las que se llegaron, recomendaciones para los trabajos futuros, y las referencias bibliográficas relacionadas al proyecto.The present work of investigation centers in realizing ""PROPOSAL OF A SYSTEM OF MONITORING AND SUPERVISION OF PHYSICALCHEMICAL QUALITY OF THE WATER FOR THE POOL GILDEMEISTER OF THE CITY OF TRUJILLO"". In the First Chapter of the work presented, the problem is addressed in the current method of analysis and study of water quality carried out by the Gerencia Regional de Salud La Libertad in terms of the frequency or periodicity with which said said, as well as as the guarantee that this analysis is carried out in a transparent manner, we set out our objective and we make known the importance of giving a solution to this problem. In the Second Chapter, the theoretical framework is presented, in which a valid support is shown of the reason for choosing a proposal for a monitoring and supervision system, as well as reinforcing the theoretical basis of the solution proposed. In the third chapter, the materials and methods used in the development of the project are mentioned, as well as the procedures for the realization of the monitoring and supervision system of the physical and chemical water quality of the Gildemeister pool in the city of Trujillo. In the fourth and fifth chapter, the results of the final selection of the elements of the system as well as the results of the simulation of the system and the discussion thereof are given. Finally, in the sixth, seventh and eighth chapters, the conclusions reached are presented, the recommendations for future work, and bibliographical references related to the project.Tesi

Bayesian model-based clustering for longitudinal ordinal data

Traditional cluster analysis methods used in ordinal data, for instance k-means and hierarchical clustering, are mostly heuristic and lack statistical inference tools to compare among competing models. To address this we propose a latent transitional model, a finite mixture model that includes both observed and latent covariates and apply it for the first time to the case of longitudinal ordinal data. This model-based clustering model is an extension of the proportional odds model and includes a first-order transitional term, occasion effects and interactions which provide flexible ways to capture different time patterns by cluster as well as time-heterogeneous transitions. We estimate model parameters within a Bayesian setting using a Markov chain Monte Carlo scheme and block-wise Metropolis–Hastings sampling. We illustrate the model using 2001–2011 self-reported health status (SRHS) from the Household, Income and Labour Dynamics in Australia survey. SRHS is recorded as an ordinal variable with five levels: poor, fair, good, very good and excellent. Using the Widely Applicable Information Criterion for model comparison, we find evidence for six latent groups. Transitions in the original data and the estimated groups are visualized using heatmaps.Peer ReviewedPostprint (author's final draft

Row mixture-based clustering with covariates for ordinal responses

Existing methods can perform likelihood-based clustering on a multivariate data matrix of ordinal data, using finite mixtures to cluster the rows (observations) of the matrix. These models can incorporate the main effects of individual rows and columns, as well as cluster effects, to model the matrix of responses. However, many real-world applications also include available covariates, which provide insights into the main characteristics of the clusters and determine clustering structures based on both the individuals’ similar patterns of responses and the effects of the covariates on the individuals' responses. In our research we have extended the mixture-based models to include covariates and test what effect this has on the resulting clustering structures. We focus on clustering the rows of the data matrix, using the proportional odds cumulative logit model for ordinal data. We fit the models using the Expectation-Maximization algorithm and assess performance using a simulation study. We also illustrate an application of the models to the well-known arthritis clinical trial data set"This work has been supported by the Ministerio de Ciencia e Innovación (Spain) [PID2019-104830RB-I00/ DOI (AEI): 10.13039/501100011033], and by Grant 2021 SGR 01421 (GRBIO) administrated by the Departament de Recerca i Universitats de la Generalitat de Catalunya (Spain). Daniel Fernández is member of the Centro de Investigación Biomédica en Red de Salud Mental, Instituto de Salud Carlos III (CIBERSAM). Daniel Fernández is a Serra Húnter Fellow"Peer ReviewedPostprint (published version

Unraveling genetic sensitivity of beef cattle to environmental variation under tropical conditions

International audienceAbstractBackgroundSelection of cattle that are less sensitive to environmental variation in unfavorable environments and more adapted to harsh conditions is of primary importance for tropical beef cattle production systems. Understanding the genetic background of sensitivity to environmental variation is necessary for developing strategies and tools to increase efficiency and sustainability of beef production. We evaluated the degree of sensitivity of beef cattle performance to environmental variation, at the animal and molecular marker levels (412 K single nucleotide polymorphisms), by fitting and comparing the results of different reaction norm models (RNM), using a comprehensive dataset of Nellore cattle raised under diverse environmental conditions.ResultsHeteroscedastic RNM (with different residual variances for environmental level) provided better fit than homoscedastic RNM. In addition, spline and quadratic RNM outperformed linear RNM, which suggests the existence of a nonlinear genetic component affecting the performance of Nellore cattle. This nonlinearity indicates that within-animal sensitivity depends on the environmental gradient (EG) level and that animals may present different patterns of sensitivity according to the range of environmental variations. The spline RNM showed that sensitivity to environmental variation from harsh to average EG is lowly correlated with sensitivity from average to good EG, at both the animal and molecular marker levels. Although the genomic regions that affect sensitivity in harsher environments were not the same as those associated with less challenging environments, the candidate genes within those regions participate in common biological processes such as those related to inflammatory and immune response. Some plausible candidate genes were identified.ConclusionsSensitivity of tropical beef cattle to environmental variation is not continuous along the environmental gradient, which implies that animals that are less sensitive to harsher conditions are not necessarily less responsive to variations in better environmental conditions, and vice versa. The same pattern was observed at the molecular marker level, i.e. genomic regions and, consequently, candidate genes associated with sensitivity to harsh conditions were not the same as those associated with sensitivity to less challenging conditions

Genetic control of temperament traits across species: association of autism spectrum disorder risk genes with cattle temperament

peer-reviewedBackground Temperament traits are of high importance across species. In humans, temperament or personality traits correlate with psychological traits and psychiatric disorders. In cattle, they impact animal welfare, product quality and human safety, and are therefore of direct commercial importance. We hypothesized that genetic factors that contribute to variation in temperament among individuals within a species will be shared between humans and cattle. Using imputed whole-genome sequence data from 9223 beef cattle from three cohorts, a series of genome-wide association studies was undertaken on cattle flight time, a temperament phenotype measured as the time taken for an animal to cover a short-fixed distance after release from an enclosure. We also investigated the association of cattle temperament with polymorphisms in bovine orthologs of risk genes for neuroticism, schizophrenia, autism spectrum disorders (ASD), and developmental delay disorders in humans. Results Variants with the strongest associations were located in the bovine orthologous region that is involved in several behavioural and cognitive disorders in humans. These variants were also partially validated in independent cattle cohorts. Genes in these regions (BARHL2, NDN, SNRPN, MAGEL2, ABCA12, KIFAP3, TOPAZ1, FZD3, UBE3A, and GABRA5) were enriched for the GO term neuron migration and were differentially expressed in brain and pituitary tissues in humans. Moreover, variants within 100 kb of ASD susceptibility genes were associated with cattle temperament and explained 6.5% of the total additive genetic variance in the largest cattle cohort. The ASD genes with the most significant associations were GABRB3 and CUL3. Using the same 100 kb window, a weak association was found with polymorphisms in schizophrenia risk genes and no association with polymorphisms in neuroticism and developmental delay disorders risk genes. Conclusions Our analysis showed that genes identified in a meta-analysis of cattle temperament contribute to neuron development functions and are differentially expressed in human brain tissues. Furthermore, some ASD susceptibility genes are associated with cattle temperament. These findings provide evidence that genetic control of temperament might be shared between humans and cattle and highlight the potential for future analyses to leverage results between species

Using Stata's capabilities to assess the performance of Latin American students in Mathematics, Reading and Science

Stata is a very good tool to analyze survey data. It is able to consider many important aspects of complex survey design and the availability of alternative variance estimation methods. Through the use of matrix and macro language it also allows the user to store and manage output results conveniently in order to automate the entire estimation and testing process. The presentation will discuss the estimation of the main results of the Second Regional Comparative and Explanatory Study – SERCE, an assessment of the performance in the domains of Mathematics, Reading and Science of third and sixth grades students in sixteen countries of Latin America in 2005/2006. In particular, it will consider the estimation of the mean scores and their variability by country, areas, grades and subpopulations. It will also present the comparisons made in order to check for the differences in performance among countries and subpopulations.

Clustering repeated ordinal data

Model based approaches to cluster continuous and cross-sectional data are abundant and well established. In contrast to that, equivalent approaches for repeated ordinal data are less common and an active area of research. In this dissertation, we propose several models to cluster repeated ordinal data using finite mixtures. In doing so, we explore several ways of incorporating the correlation due to the repeated measurements while taking into account the ordinal nature of the data. In particular, we extend the Proportional Odds model to incorporate latent random effects and latent transitional terms. These two ways of incorporating the correlation are also known as parameter and data dependent models in the time-series literature. In contrast to most of the existing literature, our aim is classification and not parameter estimation. This is, to provide flexible and parsimonious ways to estimate latent populations and classification probabilities for repeated ordinal data. We estimate the models using Frequentist (Expectation-Maximization algorithm) and Bayesian (Markov Chain Monte Carlo) inference methods and compare advantages and disadvantages of both approaches with simulated and real datasets. In order to compare models, we use several information criteria: AIC, BIC, DIC and WAIC, as well as a Bayesian Non-Parametric approach (Dirichlet Process Mixtures). With regards to the applications, we illustrate the models using self-reported health status in Australia (poor to excellent), life satisfaction in New Zealand (completely agree to completely disagree) and agreement with a reference genome of infant gut bacteria (equal, segregating and variant) from baby stool samples