Master of Science / Department of Statistics / Weixin Yao / In practice, when applying a statistical method it often occurs that some observations deviate from the usual model assumptions. Least-squares (LS) estimators are very sensitive to outliers. Even one single atypical value may have a large effect on the regression parameter estimates. The goal of robust regression is to develop methods that are resistant to the possibility that one or several unknown outliers may occur anywhere in the data. In this paper, we review various robust regression methods including: M-estimate, LMS estimate, LTS estimate, S-estimate, [tau]-estimate, MM-estimate, GM-estimate, and REWLS estimate. Finally, we compare these robust estimates based on their robustness and efficiency through a simulation study. A real data set application is also provided to compare the robust estimates with traditional least squares estimator.

Master of Science / Statistics / Weixing Song / In this report, an improved estimation procedure for the regression parameter in simple linear regression models with the Laplace measurement error is proposed. The estimation procedure is made feasible by a Tweedie type equality established for E(X|Z), where Z = X + U, X and U are independent, and U follows a Laplace distribution. When the density function of X is unknown, a kernel estimator for E(X|Z) is constructed in the estimation procedure. A leave-one-out cross validation bandwidth selection method is designed. The finite sample performance of the proposed estimation procedure is evaluated by simulation studies. Comparison study is also conducted to show the superiority of the proposed estimation procedure over some existing estimation methods.

One of my previous works introduced a new data mining technique to analyze multiple experiments called TAME: Trained Across Multiple Experiments. TAME detects treatment effects of a randomized controlled experiment by utilizing data from outside of the experiment of interest. TAME with linear regression showed promising result; in all simulated scenarios, TAME was at least as good as a standard method, ANOVA, and was significantly better than ANOVA in certain scenarios. In this work, I further investigated and improved TAME by altering how TAME assembles data and creates subject models. I found that mean-centering “prior� data and treating each experiment as equally important allow TAME to detect treatment effects better. In addition, we did not find Random Forest to be compatible with TAME.

This paper is analyzing the unemployment duration difference of individuals under different high school programs in Sweden. The cause of the analysis has from stemmed from different fields of study and its effect on employment positions in the labour market. We see education as a factor of key importance. In addition to research on an education level, the type of study also plays an important role. Therefore, how different school programs affect unemployment duration becomes the central question we try to answer in this paper. To pursue a clear analysis structure, we start with previous studies on an education level and show the author’s interest into the research of the field of study’s effect on unemployment. Job search theory is applied to do analysis on the data. Then, an empirical analysis of unemployment duration is conducted. This is done through linear regression using the least-squares method. Finally, conclusions are made as well as some policy implications and ideas for further research.

This thesis deals with regression analysis of experimentally measured data of processed cheese. There is a polynomial regression used. The choice of regressors is based on Stepwise Regression and Mallows's Statistics. The estimation of the mean value is used to find the best mixture of the emulsifying salts with regards to the observed characteristic of the processed cheese. Analysis of the experiment and its results are well documented graphically.

El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. / The current study is based on a multiple linear regression analysis with an objective to formulate an equation related to the productivity analysis of LHD equipment using independent variables such as the effective utilization of the equipment. To identify the independent variables, main productive factors, such as the actual capacity of the buckets, the transport cycles in the cleaning process, and the performance by means of curves, were analyzed. Comparisons of a Peruvian underground mine case study exhibited that the battery-powered equipment denoted similar production efficiencies to that exhibited by its diesel counterparts; however, the three-tier approach observed that the battery-powered equipment could achieve production efficiencies that are up to 13.8% more as compared to that achieved using its diesel counterparts because of increased effective utilization that can be attributed to long MTBF. The results of this study exhibit that LHDs under battery-powered storage are feasible for underground mining not only because of the fact that they do not emit any polluting gases, which helps to mitigate pollution, but also because of their good production performance that can be considered to be an important pillar in deep mining. Copyright 2019.

Metodologie Six Sigma se dnes používá v mnoha firmách a společnostech ke zlepšování kvality procesů a výrobků. Využívá k tomu různé statistické nástroje a jedním z hlavních je lineární regrese. Cílem této práce je stručný popis metodologie Six Sigma. V dalším kroku pak pomocí lineární regrese ale i jiných statistických nástojů eliminovat jednu výrobní operaci v konkrétním technickém procesu.

Dissertação(mestrado) - Universidade Federal do Rio Grande, Programa de Pós-Graduação em Engenharia Oceânica, Escola de Engenharia, 2009. / Submitted by Lilian M. Silva (lilianmadeirasilva@hotmail.com) on 2013-04-22T19:51:54Z No. of bitstreams: 1 Modelos para estimativa do Grau de Saturação do concreto mediante Variáveis Ambientais que influenciam na sua variação.pdf: 2786682 bytes, checksum: df174dab02a19756db94fc47c6bb021d (MD5) / Approved for entry into archive by Bruna Vieira(bruninha_vieira@ibest.com.br) on 2013-06-03T19:20:55Z (GMT) No. of bitstreams: 1 Modelos para estimativa do Grau de Saturação do concreto mediante Variáveis Ambientais que influenciam na sua variação.pdf: 2786682 bytes, checksum: df174dab02a19756db94fc47c6bb021d (MD5) / Made available in DSpace on 2013-06-03T19:20:55Z (GMT). No. of bitstreams: 1 Modelos para estimativa do Grau de Saturação do concreto mediante Variáveis Ambientais que influenciam na sua variação.pdf: 2786682 bytes, checksum: df174dab02a19756db94fc47c6bb021d (MD5) Previous issue date: 2009 / Nas engenharias, é fundamental estimar o tempo de vida útil das estruturas construídas, o que neste trabalho significa o tempo que os íons cloretos levam para atingirem a armadura do concreto. Um dos coeficientes que influenciam na vida útil do concreto é o de difusão, sendo este diretamente influenciado pelo grau de saturação (GS) do concreto. Recentes estudos levaram ao desenvolvimento de um método de medição do GS. Embora esse método seja eficiente, ainda assim há um grande desperdício de tempo e dinheiro em utilizá-lo. O objetivo deste trabalho é reduzir estes custos calculando uma boa aproximação para o valor do GS com modelos matemáticos que estimem o seu valor através de variáveis ambientais que influenciam na sua variação. As variáveis analisadas nesta pesquisa, são: pressão atmosférica,temperatura do ar seco, temperatura máxima, temperatura mínima, taxa de evaporação interna (Pichê), taxa de precipitação, umidade relativa, insolação, visibilidade, nebulosidade e taxa de evaporação externa. Todas foram analisadas e comparadas estatisticamente com medidas do GS obtidas durante quatro anos de medições semanais, para diferentes famílias de concreto. Com essas análises, pode-se medir a relação entre estes dados verificando que os fatores mais influentes no GS são, temperatura máxima e umidade relativa. Após a verificação desse resultado, foram elaborados modelos estatísticos, para que, através dos dados ambientais, cedidos pelo banco de dados meteorológicos, se possam calcular, sem desperdício de tempo e dinheiro, as médias aproximadas do GS para cada estação sazonal da região sul do Brasil, garantindo assim uma melhor estimativa do tempo de vida útil em estruturas de concreto. / In engineering, it is fundamental to estimate the life-cycle of built structures, which in this study means the period of time required for chlorides to reach the concrete reinforcement. One of the coefficients that affect the life-cycle of concrete is the diffusion, which is directly influenced by the saturation degree (SD) of concrete. Recent studies have led to the development of a measurement method for the SD. Although this method is efficient, there is still waste of time and money when it is used. The objective of this study is to reduce costs by calculating a good approximation for the SD value with mathematical models that predict its value through environmental variables that affect its variation. The variables analysed in the study are: atmospheric pressure, temperature of the dry air, maximum temperature, minimum temperature, internal evaporation rate (Pichê), precipitation rate, relative humidity, insolation, visibility, cloudiness and external evaporation rate. All of them were statistically analysed and compared with measurements of SD obtained during four years of weekly assessments for different families of concrete. By considering these analyses, the relationship among these data can be measured and it can be verified that the most influent variables affecting the SD are the maximum temperature and the relative humidity. After verifying this result, statistical models were developed aiming to calculate, based on the environmental data provided by the meteorological database and without waste of time and money, the approximate averages of SD for each seasonal station of the south region of Brazil, thus providing a better estimative of life-cycle for concrete structures.

The importance of bottomland hardwood (BLH) forests to support the economy through timber production and carbon sequestration is acknowledged; however, their full potential is yet to be explored. This study developed variable density yield models for BLH oak-gum-cypress forests along the US Gulf Coast and Lower Mississippi River Delta. The models, with an adjusted R2 of 98% for cubic foot growing stock volume and 77% for Doyle board foot sawlog volume, are expected to be valuable tools for landowners and managers seeking to make informed decisions about BLH forest management. A carbon stock model was also developed, and carbon sequestration was explored based on basal area increment. The results showed potential for carbon sequestration with an average carbon stock of 30.56 tons/acre and a maximum average discounted present value of carbon accumulation of $15.94/ton/acre/year. This provides valuable information to managers and landowners willing to participate in carbon credit markets.

Title: The influence of swimming part of triathlon the total performance in competitions of world championships 2000-2010. Objectives: The aim is to investigate the influence of the swimming part of the total performance in triathlon in the lost 10 years. Methods: The relation between two variables (times from swimming and the total time of competitions from competitions of world championships 2000-2010 were described with the help of linear regression. Individual years were then compared in a point graph. Results: Our hypothese were confirmmed. It means that the result from the swimming part explains the result from the competition in less than 30%, which means the R2 is smaller than 0,3. We also found, that the influnence of the swimming part on the final result in triathlon has not changed in the last years. The relationship times in the swimming part and total (final) times was closest in 2005 (R2= 0,21). In other years, this dependence is lower, R2 was smaller than 0,07. Keywords: linear regression, longitudinal analysis, performance