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The measures studied in this thesis were a measure of disorder, D, and a measure of the individual part of the disagreement, the measure of relative rank variance, RV, proposed by Svensson in 1993. The measure of disorder is a useful measure of order consistency in paired assessments of scales with a different number of possible values. The measure of relative rank variance is a useful measure in evaluating reliability and for evaluating change in qualitative outcome variables. In Paper I an overview of methods used in the analysis of dependent ordinal data and a comparison of the methods regarding the assumptions, specifications, applicability, and implications for use were made. In Paper II an application, and a comparison of the results of some standard models, tests, and measures to two different research problems were made. The sampling distribution of the measure of disorder was studied both analytically and by a simulation experiment in Paper III. The asymptotic normal distribution was shown by the theory of U-statistics and the simulation experiments for finite sample sizes and various amount of disorder showed that the sampling distribution was approximately normal for sample sizes of about 40 to 60 for moderate sizes of D and for smaller sample sizes for substantial sizes of D. The sampling distribution of the relative rank variance was studied in a simulation experiment in Paper IV. The simulation experiment showed that the sampling distribution was approximately normal for sample sizes of 60-100 for moderate size of RV, and for smaller sample sizes for substantial size of RV. In Paper V a procedure for inference regarding relative rank variances from two or more samples was proposed. Pair-wise comparison by jackknife technique for variance estimation and the use of normal distribution as approximation in inference for parameters in independent samples based on the results in Paper IV were demonstrated. Moreover, an application of Kruskal-Wallis test for independent samples and Friedman’s test for dependent samples were conducted. / Statistical methods for ordinal data

In this work, I will describe a new statistical tool: the canonical bicoherence, which is a combination of the canonical coherence and the bicoherence. I will provide its definitions, properties, estimation by multitaper methods and statistics, and estimate the variance of the estimates by the weighted jackknife method. I will discuss its applicability and usefulness in nonlinear quadratic phase coupling detection and analysis for multivariate random processes. Furthermore, I will develop the time-varying canonical bicoherence for the nonlinear analysis of non-stationary random processes. In this thesis, the canonical bicoherence is mainly applied in two types of data: a) three-component geomagnetic field data, and b) high-dimensional brain electroencephalogram data. Both results obtained will be linked with physical or physiological interpretations. In particular, this thesis is the first work where the novel method of ``canonical bicoherence'' is introduced and applied to the nonlinear quadratic phase coupling detection and analysis for multivariate random processes. / Thesis (Ph.D, Mathematics & Statistics) -- Queen's University, 2008-10-31 15:03:57.596

Neste trabalho foi proposto um modelo de regressão para dados com censura intervalar utilizando a distribuição Weibull-exponenciada, que possui como característica principal a função de taxa de falha que assume diferentes formas (unimodal, forma de banheira, crescente e decrescente). O atrativo desse modelo de regressão é a sua utilização para discriminar modelos, uma vez que o mesmo possui como casos particulares os modelos de regressão Exponencial, Weibull, Exponencial-exponenciada, entre outros. Também foi estudado um modelo de regressão para dados de sobrevivência grupados na qual a abordagem é fundamentada em modelos de tempo discreto e em tabelas de vida. A estrutura de regressão representada por uma probabilidade é modelada adotando-se diferentes funções de ligação, tais como, logito, complemento log-log, log-log e probito. Em ambas as pesquisas, métodos de validação dos modelos estatísticos propostos são descritos e fundamentados na análise de sensibilidade. Para detectar observações influentes nos modelos propostos, foram utilizadas medidas de diagnóstico baseadas na deleção de casos, denominadas de influência global e medidas baseadas em pequenas perturbações nos dados ou no modelo proposto, denominada de influência local. Para verificar a qualidade de ajuste do modelo e detectar pontos discrepantes foi realizada uma análise de resíduos nos modelos propostos. Os resultados desenvolvidos foram aplicados a dois conjuntos de dados reais. / In this study, a regression model for interval-censored data were developed, using the Exponentiated- Weibull distribution, that has as main characteristic the hazard function which assumes different forms (unimodal, bathtub shape, increase, decrease). A good feature of that regression model is their use to discriminate models, that have as particular cases, the models of regression: Exponential, Weibull, Exponential-exponentiated, amongst others. Also a regression model were studied for grouped survival data in which the approach is based in models of discrete time and in life tables, the regression structure represented by a probability is modeled through the use of different link function, logit, complementary log-log, log-log or probit. In both studies, validation methods for the statistical models studied are described and based on the sensitivity analysis. To find influential observations in the studied models, diagnostic measures were used based on case deletion, denominated as global influence and measures based on small perturbations on the data or in the studied model, denominated as local influence. To verify the goodness of fitting of the model and to detect outliers it was performed residual analysis for the proposed models. The developed results were applied to two real data sets.

L'imputation est souvent utilisée dans les enquêtes pour traiter la non-réponse partielle. Il est bien connu que traiter les valeurs imputées comme des valeurs observées entraîne une sous-estimation importante de la variance des estimateurs ponctuels. Pour remédier à ce problème, plusieurs méthodes d'estimation de la variance ont été proposées dans la littérature, dont des méthodes adaptées de rééchantillonnage telles que le Bootstrap et le Jackknife. Nous définissons le concept de double-robustesse pour l'estimation ponctuelle et de variance sous l'approche par modèle de non-réponse et l'approche par modèle d'imputation. Nous mettons l'emphase sur l'estimation de la variance à l'aide du Jackknife qui est souvent utilisé dans la pratique. Nous étudions les propriétés de différents estimateurs de la variance à l'aide du Jackknife pour l'imputation par la régression déterministe ainsi qu'aléatoire. Nous nous penchons d'abord sur le cas de l'échantillon aléatoire simple. Les cas de l'échantillonnage stratifié et à probabilités inégales seront aussi étudiés. Une étude de simulation compare plusieurs méthodes d'estimation de variance à l'aide du Jackknife en terme de biais et de stabilité relative quand la fraction de sondage n'est pas négligeable. Finalement, nous établissons la normalité asymptotique des estimateurs imputés pour l'imputation par régression déterministe et aléatoire. / Imputation is often used in surveys to treat item nonresponse. It is well known that treating the imputed values as observed values may lead to substantial underestimation of the variance of the point estimators. To overcome the problem, a number of variance estimation methods have been proposed in the literature, including appropriate versions of resampling methods such as the jackknife and the bootstrap. We define the concept of doubly robust point and variance estimation under the so-called nonresponse and imputation model approaches. We focus on jackknife variance estimation, which is widely used in practice. We study the properties of several jackknife variance estimators under both deterministic and random regression imputation. We first consider the case of simple random sampling without replacement. The case of stratified simple random sampling and unequal probability sampling is also considered. A limited simulation study compares various jackknife variance estimators in terms of bias and relative stability when the sampling fraction is not negligible. Finally, the asymptotic normality of imputed estimator is established under both deterministic and random regression imputation.

[pt] A finalidade desta tese é propor estimadores pontuais para os coeficientes de escalonabilidade associados à Teoria de Resposta ao Item não Paramétrica (TRIN), a saber: Hij, Hi e H, e seus respectivos estimadores da variância, baseados na abordagem da amostragem de populações finitas. Com o objetivo de investigar empiricamente a qualidade destes estimadores são consideradas as populações de referência que são formadas pelos alunos que frequentavam o 9° ano do Ensino Fundamental, na rede pública, em áreas urbanas dos Estados de Roraima e do Rio de Janeiro, que participaram da Prova Brasil 2007. As respostas obtidas destes alunos a um conjunto de 10 itens dicotomizados que mensuram o capital econômico da sua família foram usadas na construção dos coeficientes de escalonabilidade do Modelo de Homogeneidade Monótona da TRIN. Repetidas amostras foram selecionadas de cada população de referência empregando dois planos amostrais: AC1S (amostragem por conglomerados em único estágio) e AC2-SAEB (com seleção de escolas e turmas, estratificação e sorteio das unidades do primeiro estágio com probabilidade proporcional a uma medida de tamanho da escola. A estimação pontual é baseada no Modelo de Superpopulação. Duas técnicas foram tratadas para a estimação da variância: método do Conglomerado Primário e Delete - 1 Jackknife. As medidas usuais: vício relativo, erro relativo médio, intervalo de confiança e efeito do plano amostral são usadas para a avaliação da qualidade dos estimadores em termos das propriedades de vício e precisão. O estudo assinala que os estimadores pontuais apresentam boas propriedades e, além disto, o estimador da variância corrigido pelo fator de correção de população finita é o mais apropriado em termos de vício e precisão. O plano amostral complexo adotado teve impacto na estimação pontual e da variância dos estimadores dos coeficientes de escalonabilidade. / [en] The purpose of this thesis is to propose estimators for the coefficients of scalability associated with Non Parametric Item Response Theory (NIRT), namely: Hij, Hi and H, and their variance estimators, based on the approach of sampling finite populations. To investigate empirically the quality of these estimators are considered the reference populations that are formed by students attending the 9th year of elementary school, in public, in urban areas of the states of Roraima and Rio de Janeiro, who participated Prova Brasil 2007. The responses of students to a set of 10 dichotomized items that measure the economic status of their families were used in the construction of the coefficients of scalability of the Homogeneity Model. Repeated samples were selected from each reference population using two sampling plans: AC1S (cluster sampling single stage) and AC2-SAEB (with selecting schools and classes, stratification and draw units of the first stage with probability proportional to a measure of school size). The point estimate is based on the approach of the Model Overpopulation. Two techniques were treated to estimate the variance: Ultimate Cluster method and Delete - 1 Jackknife. The usual measures: relative bias, mean relative error, confidence intervals and effect of the sampling plan is used to assess the quality of the estimators in terms of the properties of bias and accuracy. The study notes that the estimators have good properties and, in addition, the estimator of the variance corrected by the correction factor for finite population is the most appropriate in terms of accuracy and bias. The complex sampling (AC2-SAEB) impacted the point estimate and variance of the estimators of the coefficients of scalability.

Este trabajo presenta dos líneas de investigación desarrolladas en los últimos años en torno a la etapa de evaluación en datos de recuento. Los campos de estudio han sido: los datos de recuento, concretamente el estudio del modelo de regresión de Poisson y sus extensiones y la etapa de evaluación como punto de inflexión en el proceso de modelado estadístico. Los resultados obtenidos ponen de manifiesto la importancia de aplicar el modelo adecuado a las características de los datos así como de evaluar el ajuste del mismo. Por otra parte la comparación de pruebas, índices, estimadores y modelos intentan señalar la adecuación o la preferencia de unos sobre otros en determinadas circunstancias y en función de los objetivos del investigador. / This paper presents two lines of research that have been developed in recent years on the evaluation stage in count data. The areas of study have been both count data, specifically the study of Poisson regression modelling and its extension, and the evaluation stage as a point of reflection in the statistical modelling process. The results obtained demonstrate the importance of applying appropriate models to the characteristics of data as well as evaluating their fit. On the other hand, comparisons of trials, indices, estimators and models attempt to indicate the suitability or preference for one over the others in certain circumstances and according to research objectives.

L'imputation est souvent utilisée dans les enquêtes pour traiter la non-réponse partielle. Il est bien connu que traiter les valeurs imputées comme des valeurs observées entraîne une sous-estimation importante de la variance des estimateurs ponctuels. Pour remédier à ce problème, plusieurs méthodes d'estimation de la variance ont été proposées dans la littérature, dont des méthodes adaptées de rééchantillonnage telles que le Bootstrap et le Jackknife. Nous définissons le concept de double-robustesse pour l'estimation ponctuelle et de variance sous l'approche par modèle de non-réponse et l'approche par modèle d'imputation. Nous mettons l'emphase sur l'estimation de la variance à l'aide du Jackknife qui est souvent utilisé dans la pratique. Nous étudions les propriétés de différents estimateurs de la variance à l'aide du Jackknife pour l'imputation par la régression déterministe ainsi qu'aléatoire. Nous nous penchons d'abord sur le cas de l'échantillon aléatoire simple. Les cas de l'échantillonnage stratifié et à probabilités inégales seront aussi étudiés. Une étude de simulation compare plusieurs méthodes d'estimation de variance à l'aide du Jackknife en terme de biais et de stabilité relative quand la fraction de sondage n'est pas négligeable. Finalement, nous établissons la normalité asymptotique des estimateurs imputés pour l'imputation par régression déterministe et aléatoire. / Imputation is often used in surveys to treat item nonresponse. It is well known that treating the imputed values as observed values may lead to substantial underestimation of the variance of the point estimators. To overcome the problem, a number of variance estimation methods have been proposed in the literature, including appropriate versions of resampling methods such as the jackknife and the bootstrap. We define the concept of doubly robust point and variance estimation under the so-called nonresponse and imputation model approaches. We focus on jackknife variance estimation, which is widely used in practice. We study the properties of several jackknife variance estimators under both deterministic and random regression imputation. We first consider the case of simple random sampling without replacement. The case of stratified simple random sampling and unequal probability sampling is also considered. A limited simulation study compares various jackknife variance estimators in terms of bias and relative stability when the sampling fraction is not negligible. Finally, the asymptotic normality of imputed estimator is established under both deterministic and random regression imputation.

Dans cette thèse, nous étudions l'effet de l'auto-sélection des salariés sur l'estimation de la productivité, des différentiels de salaires et de qualité du travail entre les secteurs. Afin de prendre en compte l'auto-sélection des employés dans l'estimation des différentiels le long de la distribution des salaires, nous construisons une approche innovante composée de trois caractéristiques: (i) nous nous intéressons aux effets par quantile inconditionnel; (ii) nous incorporons des effets fixes spécifiques à chaque quantile; (iii) nous proposons une méthode de correction de l'incidental parameter bias. Cette approche permet de produire des résultats exploitables en terme de politiques publiques. Nous montrons dans un premier temps que la sélection positive dans le secteur public tend à se dégrader. Elle disparaît totalement en haut de la distribution des salaires dans la période récente, suggérant un effet négatif du gel des salaires nominaux. Dans notre deuxième article, nous mettons en évidence une sélection négative substantielle dans le secteur informel concernant les hommes et les bas salaires. Cette sélection négative apparaît à la suite de la Grande Récession, indiquant une réallocation des salariés les moins productifs vers le secteur informel. Dans le dernier article, nous montrons pour la période récente que le niveau de productivité des SCOP n'est pas significativement différent de celui des autres entreprises. Nous confirmons l'hypothèse selon laquelle les motivations non-pécunières des employés expliquent une partie importante de la productivité des SCOP dans deux des secteurs étudiés (secteur manufacturier et secteur des transports). / This PhD thesis studies the effect of workers' self-selection when estimating productivity, wages and job quality differentials between sectors. In order to account for the self-selection of employees in the estimation of differentials along the wage distribution, we develop an innovative approach comprising three features: (i) we rely on unconditional quantile effects ; (ii) we incorporate quantile-specific fixed effects; (iii) we suggest a treatment of the incidental parameter bias. This method allows to provide public policies relevant comparisons. We show first that the positive selection into public jobs tends to decline. It totally disappears among top earners in the recent period, suggesting the detrimental effect of nominal wage freeze. In the second paper, we unveil that there is a substantial negative selection into informal salary work for men on average and particularly at low wages. It arises in the wake of the Great Recession, pointing to a shakeout of less productive workers in the formal sector. In the last paper, we account for employees' non-pecuniary motives in our comparison of the productivity of labour-managed firms and other for-profits company. We confirm for the recent period and on a large scale that the SCOP total factor productivity level is not significantly different from the other firms'. We find also results that support the hypothesis that employees non-pecuniary motives accounts for a substantial part of French labour-managed firms productivity in two of the three industries studied (manufacturing and transports).

在生態學與生物學上，物種數常作為生物多樣性的指標，以估計單一群體物種數為例，較知名的方法首推Good (1953)以在樣本中出現一次的物種為基礎，提出的物種數估計方法堪稱的先驅，隨後許多文獻延伸Good的想法，發展出許多的估計方法，例如Burham and Overton (1978)的摺刀估計法，Chao and Lee (1992)則以涵蓋機率方式估計。相對而言，兩群體的共有物種數的研究少有人探討，目前以Chao et al. (2000)的估計式較為知名。 本研究參考Good (1953)提出估計未發現物種出現機率的想法，估計未發現共有物種的機率，並以Burham and Overton (1978)中應用摺刀法估計物種數的概念，建立一階摺刀估計式與變異數，且另行以多項分配公式推導變異數估計式，進行電腦模擬與實際資料驗證並與Chao et al. (2000)提出的共有物種估計式比較。最後根據Rasmussen and Starr (1979)以抽樣成本建立最適停止規則的概念，應用於本研究所提出的估計式，並經由電腦模擬找出抽樣成本與物種分佈均勻程度的關聯，可作為設定停止規則的依據。 / The number of species is often used to measure the biodiversity of a population in ecology and biology. Good (1953) proposed a famous estimate for the number of species based on the probability of unseen species. Subsequently, many studies applied Good’s idea to create new estimation methods, For example, the Jackknife estimate by Burham and Overton (1978), and the estimate by using the sample coverage probability in Chao and Lee (1992) are two famous examples. However, not many studies focus on estimating the number of shared species of two populations, except the method by Chao et al. (2000). In this study, we modify Good’s idea and extend the Jackknife method of Burham and Overton (1978) to develop the estimate for the number of shared species of two populations. In addition, we also establish the variance formula of the estimator by using the multinomial distribution. Subsequently, we use computer simulation and real data sets to evaluate the proposed method, and compare them with the estimator by Chao et al. (2000). Finally, we adapt the idea of optimal stopping rule by Rasmussen and Starr (1979) and combine it with the proposed jackknife estimate. We found that using the sampling cost as the stopping rule is a feasible approach for estimating the number of shared species.