This thesis is an attempt to propose an analytical model for estimating and predicting capability in human networks (i.e. work teams). Capability in this context is the ability to utilise the collective inherent and acquired resources of individuals to complete a given task. The motivation of proposing a method for measuring collective capability of teams is to assist project managers and team builders to allocate and assign “The most capable teams” to a project to maximise the likelihood of success. The review of literature in engineering, human sciences and economics has led to a definition of capability. One of the key findings of this research work is that collective capability can be predicted by: 1. Demographic homophily of members of the team, 2. The diversity of skills that each member brings to the team, 3. The past experience or attainments of the members, and 4. The strength of relationship amongst the members of the team. The influence of the four predictors of capability is investigated through the design of empirical surveys conducted among postgraduate students over a period of 2 years. The data collected from the surveys are used to assess the correlation between the predictors and the dependent variable using standard statistical methods. The conclusions of the study confirm that there are positive and significant relationships between the independent predictors and collective capability of project teams. The demographic homophily of the individuals in team and their instrumental (task related) relationships’ strength become the two most effective predictors which have the highest effect on the collective capability of a team as a whole. The skills diversity of the members in a group and their previous level of attainments/experiences in similar projects were also proved to be effective factors (with lower level of effect) in increasing the capability of the whole team in fulfilling the requirements of a pre-defined project.

Binomial group testing has been long recognized as an efficient method of estimating proportion of subjects with a specific characteristic. The method is superior to the classic maximum likelihood estimator (MLE), particularly when the proportion is small. Under the group testing model, we assume the testing is conducted without error. In the present research, a new Bayes estimator will be proposed that utilizes an additional piece of information, the proportion to be estimated is small and within a given range. It is observed that with the appropriate choice of the hyper-parameter our new Bayes estimator has smaller mean squared error (MSE) than the classic MLE, Burrows estimator, and the existing Bayes estimator. Furthermore, on the basis of heavy Monte Carlo simulation we have determined the best hyper-parameters in the sense that the corresponding new Bayes estimator has the smallest MSE. A table of these best hyper-parameters is made for proportions within the considered range.

Logic regression (LR) is a methodology to identify logic combinations of binary predictors in the form of intersections (and), unions (or) and negations (not) that are linearly associated with an outcome variable. Logic regression uses the predictors as inputs and enables us to identify important logic combinations of independent variables using a computationally efficient tree-based stochastic search algorithm, unlike the classical regression models, which only consider pre-determined conventional interactions (the “and” rules). In the thesis, we focused on LR with a binary outcome in a logistic regression framework. Simulation studies were conducted to examine the performance of LR under the assumption of independent and correlated observations, respectively, for various characteristics of the data sets and LR search parameters. We found that the proportion of times that LR selected the correct logic rule was usually low when the signal and/or prevalence of the true logic rule were relatively low. The method performed satisfactorily under easy learning conditions such as high signal, simple logic rules and/or small numbers of predictors. Given the simulation characteristics and correlation structures tested, we found some but not significant difference in performance when LR was applied to dependent observations compared to the independent case. In addition to simulation studies, an advanced application method was proposed to integrate LR and resampling methods in order to enhance LR performance. The proposed method was illustrated using two simulated data sets as well as a data set from a real-life situation. The proposed method showed some evidence of being effective in discerning the correct logic rule, even for unfavorable learning conditions.

The exponential power (EP) distribution is a very important distribution that was used by survival analysis and related with asymmetrical EP distribution. Many researchers have discussed statistical inference about the parameters in EP distribution using i.i.d random samples. However, sometimes available data might contain only record values, or it is more convenient for researchers to collect record values. We aim to resolve this problem. We estimated two parameters of the EP distribution by MLE using upper record values. According to simulation study, we used the Bias and MSE of the estimators for studying the efficiency of the proposed estimation method. Then, we discussed the prediction on the next upper record value by known upper record values. The study concluded that MLEs of EP distribution parameters by upper record values has satisfactory performance. Also, prediction of the next upper record value performed well

Multivariate normal distribution is commonly encountered in any field, a frequent issue is the missing values in practice. The purpose of this research was to estimate the parameters in three-dimensional covariance permutation-symmetric normal distribution with complete data and all possible patterns of incomplete data. In this study, MLE with missing data were derived, and the properties of the MLE as well as the sampling distributions were obtained. A Monte Carlo simulation study was used to evaluate the performance of the considered estimators for both cases when ρ was known and unknown. All results indicated that, compared to estimators in the case of omitting observations with missing data, the estimators derived in this article led to better performance. Furthermore, when ρ was unknown, using the estimate of ρ would lead to the same conclusion.

Orientador: Michel Daoud Yacoub / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação / Made available in DSpace on 2018-08-26T17:46:44Z (GMT). No. of bitstreams: 1 GomezVillavicencio_MirkoAlberto_M.pdf: 2402841 bytes, checksum: 68bbd5454c4ebb5de373b3e095e468b2 (MD5) Previous issue date: 2014 / Resumo: Nesta dissertação, uma distribuição bivariável kappa -mu é apresentada. Exatas expressões para a função densidade de probabilidade conjunta, função distribuição cumulativa conjunta, e os momentos arbitrários conjuntos são encontradas. As estatísticas conjuntas são dadas em termos de seus respectivos parâmetros (kappa1 , mu1 ) e (kappa_2$, mu_2), com mu1 =mu2 = mu> 0 e arbitrários kappa1> 0 e kappa2> 0. Como exemplo de aplicação, a probabilidade de outage para o caso de dois ramos por combinação por seleção pura, combinação por razão máxima e combinação por ganho igual são apresentadas / Abstract: In this thesis, a bivariate kappa-mu model is presented. Exact expressions for the joint probability density function, joint cumulative distribution function, and joint arbitrary moments are found. The joint statistics are given in terms of their respective parameters (kappa1, mu1) and (kappa2, mu2), with mu1=mu2=mu >0 and arbitrary kappa1>0 and kappa2>0. As an application example, the outage probability of a dual-branch pure selection combining, maximo ratio combining and equal gain combining scheme are presented / Mestrado / Telecomunicações e Telemática / Mestre em Engenharia Elétrica

Today in a modern society the pace is high and the need for flexibility and mobilityis big. Despite the increasing access and great digital solutions for online-meetings,the need for short distance transportation remains. With an increasing number ofeasy-to-use mobile applications, the many offered solutions for personal transporta-tion have in the last few years expanded. Besides the effect of an increasing pressureof road space, the pressure on the transportation solutions has escalated. This is the current situation of the company we came in contact with. Their currenttravel service provides on-demand transportation in small electric, emission free andchauffeur driven vehicles they call pods. As a step of claiming their space in thetransportation sector they are developing their application letting customers planand book a transport. For the ability to test their new booking solution this project took form. With asimulation it would be possible to test the various probabilities of a travel outcome.The aim was to develop a simulation software, simulating the events of multipletravels taking place in the system. With the simulation it would be possible to eval-uate the configuration for the booking feature, analyse how it works with the rest ofthe system and to help the company to predict how the intensity of customers in re-lations to the number of available drivers would affect the outcome of a travel request. The project covered the study of how to interpret the factors, that a transportationdirectly depends on, into events in the system and how to use historical events tocreate probabilities of the simulation outcome. The simulator software was not suc-cessful with simulating multiple rides as intended, but parts of the software could beevaluated in relation to historical events. Statistical models were built using aggre-gated events from the system. When comparing the average result of the statisticalmodels with the historical event count of the system, it resulted in an outcomewithin an acceptable range. This shows that it was possible to use aggregated his-torical events to create probabilities and that these probabilities were reliable.

An efficient way of analyzing survival clinical data such as cancer data is a great concern to health experts. In this study, we investigate and propose an efficient way of handling survival clinical data. Simulation studies were conducted to compare performances of various forms of survival model techniques using an R package ``survsim". Models performance was conducted with varying sample sizes as small ($n5000$). For small and mild samples, the performance of the semi-parametric outperform or approximate the performance of the parametric model. However, for large samples, the parametric model outperforms the semi-parametric model. We compared the effectiveness and reliability of our proposed techniques using a real clinical data of mild sample size. Finally, systematic steps on how to model and explain the proposed techniques on real survival clinical data was provided.

In recent years a new field of statistics has become of importance in many branches of experimental science. This is the Monte Carlo Method, so called because it is based on simulation of stochastic processes. By stochastic process, it is meant some possible physical process in the real world that has some random or stochastic element in its structure. This is the subject which may appropriately be called the dynamic part of statistics or the statistics of "change," in contrast with the static statistical problems which have so far been the more systematically studied. Many obvious examples of such processes are to be found in various branches of science and technology, for example, the phenomenon of Brownian Motion, the growth of a bacterial colony, the fluctuating numbers of electrons and protons in a cosmic ray shower or the random segregation and assortment of genes (chemical entities responsible for governing physical traits for the plant and animal systems) under linkage condition. Their occurrences are predominant in the fields of medicine, genetics, physics, oceanography, economics, engineering and industry, to name only a few scientific disciplines. The scientists making measurements in his laboratory, the meteriologist attempting to forecast weather, the control systems engineer designing a servomechanism (such as an aircraft or a thermostatic control), the electrical engineer designing a communication system (such as the radio link between entertainer and audience or the apparatus and cables that transmit messages from one point to another), economist studying price fluctuations in business cycles and the neurosurgion studying brain wave records, all are encountering problems to which the theory of stochastic processes may be relevant. Let us consider a few of these processes in a little more detail. In statistical physics many parts of the theory of stochastic processes were developed in correlation with the study of fluctuations and noise in physical systems (Einstein, 1905; Smoluchowski, 1906; and Schottky, 1918). Consequently, the theory of stochastic processes can be regarded as the mathematical foundation of statistical physics. The stochastic models for population growth consider the size and composition of a population which is constantly fluctuating. These are mostly considered by Bailey (1957), Bartlett (1960), and Bharucha-Reid (1960). In communication theory a wide variety of problems involving communication and/or control such as the problem of automatic tracking of moving objects, the reception of radio signals in the presence of natural and artificial disturbances, the reproduction of sound and images, the design of guidance systems, the design of control systems for industrial processes may be regarded as special cases of the following general problem; that is, let T denote a set of points in a time axis such that at each point t in T an observation has been made of a random variable X(t). Given the observations [x(t), t ϵT] and a quantity Z related to the observation, one desires to from in an optimum manner, estimates of, and tests of hypothesis about Z and various functions h(Z).

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