This dissertation examines the ways in which design and design practitioners have defined value in the design discourse through the vocabulary and concept of goodness since 1870. These definitions of value have been central to design discourse both past and present and deal with the impact and purpose of design work as well as its role in the creation of the approaching future. Since before the coalescence of graphic design as a separate field of design, these questions have been tied to the form, function or social impact of the objects created by designers. Despite the prevalence of this line of inquiry in the discourse, current usage tends toward uncritical and uninformed usage because of a separation of the terminology from the historical context in which this conversation developed. The purpose of this investigation, then, is not to segment terms into “right” or “proper” definitions, but to apply a critical historical lens to the vocabulary of goodness as it has been used in the larger design discourse since 1870 in order to better understand the state of current discussions of value and to inform paths of future inquiry. In order to do so, this investigation developed a framework by which to understand the ways in which goodness has been contextualized over the last 150 years. This historical context does not limit us to a historicist approach to design, and it not presented as an essentialist or teleological marker of things to come. Instead, knowledge of the historical context and various uses of a term makes us better educated practitioners of design who are more capable of combating the individualistic fallacy of the enlightened genius. Through a fuller knowledge of the critical historical context in which our field’s vocabulary developed, we can better understand notions of creativity that position designers as expert guides through networks of information and meaning.

Discrete-time survival analysis as an educational research technique has focused on analysing and interpretating parameter estimates. The purpose of this study was to examine the effects of certain data characteristics on the hazard estimates and goodness of fit statistics. Fifty-four simulated data sets were crossed with four conditions in a 2 (time period) by 3 (distribution of Y = 1) by 3 (distribution of Y = 0) by 3 (sample size) design.

One of the most useful and best known goodness of fit test is the Kolmogorov one-sample test. The assumptions for the Kolmogorov (one-sample test) test are: 1. A random sample; 2. A continuous random variable; 3. F(x) is a completely specified hypothesized cumulative distribution function. The Kolmogorov one-sample test has a wide range of applications. Knowing the effect fromusing the test when an assumption is not met is of practical importance. The purpose of this research is to analyze the robustness of the Kolmogorov one-sample test to sampling from a finite discrete distribution. The standard tables for the Kolmogorov test are derived based on sampling from a theoretical continuous distribution. As such, the theoretical distribution is infinite. The standard tables do not include a method or adjustment factor to estimate the effect on table values for statistical experiments where the sample stems from a finite discrete distribution without replacement. This research provides an extension of the Kolmogorov test when the hypothesized distribution function is finite and discrete, and the sampling distribution is based on sampling without replacement. An investigative study has been conducted to explore possible tendencies and relationships in the distribution of Dn when sampling with and without replacement for various parameter settings. In all, 96 sampling distributions were derived. Results show the standard Kolmogorov table values are conservative, particularly when the sample sizes are small or the sample represents 10% or more of the population.

This thesis deals with the goodness of fit tests in nonparametric model in the presence of unknown parameters of the probability distribution. The first part is devoted to understanding of the theoretical basis. We compare two methodologies for the construction of test statistics with application of empirical characteristic and empirical distribution functions. We use kernel estimates of regression functions and parametric bootstrap method to approximate the critical values of the tests. In the second part of the thesis, the work is complemented with the simulation study for different choices of weighting functions and parameters. Finally we illustrate the use and the comparison of goodness of fit tests on the example with the real data set. Powered by TCPDF (www.tcpdf.org)

By testing a fiber beard, certain cotton fiber length parameters can be obtained rapidly. This is the method used by the High Volume Instrument (HVI). This study is aimed to explore the approaches and obtain the inference of length distributions of HVI beard sam- ples in order to develop new methods that can help us find the distribution of original fiber lengths and further improve HVI length measurements. At first, the mathematical functions were searched for describing three different types of length distributions related to the beard method as used in HVI: cotton fiber lengths of the original fiber population before picked by the HVI Fibrosampler, fiber lengths picked by HVI Fibrosampler, and fiber beard's pro-jecting portion that is actually scanned by HVI. Eight sets of cotton samples with a wide range of fiber lengths are selected and tested on the Advanced Fiber Information System (AFIS). The measured single fiber length data is used for finding the underlying theoreti-cal length distributions, and thus can be considered as the population distributions of the cotton samples. In addition, fiber length distributions by number and by weight are dis- cussed separately. In both cases a mixture of two Weibull distributions shows a good fit to their fiber length data. To confirm the findings, Kolmogorov-Smirnov goodness-of-fit tests were conducted. Furthermore, various length parameters such as Mean Length (ML) and Upper Half Mean Length (UHML) are compared between the original distribution from the experimental data and the fitted distributions. The results of these obtained fiber length distributions are discussed by using Partial Least Squares (PLS) regression, where the dis-tribution of the original fiber length from the distribution of the projected one is estimated.

<div>Networks and point processes provide flexible tools for representing and modeling complex dependencies in data arising from various social and physical domains. Graphs, or networks, encode relational dependencies between entities, while point processes characterize temporal or spatial interactions among events.</div><div><br></div><div>In the first part of this dissertation, we consider dynamic network data (such as communication networks) in which links connecting pairs of nodes appear continuously over time. We propose latent space point process models to capture two different aspects of the data: (i) communication occurs at a higher rate between individuals with similar latent attributes (i.e., homophily); and (ii) individuals tend to reciprocate communications from others, but in a varied manner. Our framework marries ideas from point process models, including Poisson and Hawkes processes, with ideas from latent space models of static networks. We evaluate our models on several real-world datasets and show that a dual latent space model, which accounts for heterogeneity in both homophily and reciprocity, significantly improves performance in various link prediction and network embedding tasks.</div><div><br></div><div>In the second part of this dissertation, we develop nonparametric goodness-of-fit tests for discrete distributions and point processes that contain intractable normalization constants, providing the first generally applicable and computationally feasible approaches under those circumstances. Specifically, we propose and characterize Stein operators for discrete distributions, and construct a general Stein operator for point processes using the Papangelou conditional intensity function. Based on the proposed Stein operators, we establish kernelized Stein discrepancy measures for discrete distributions and point processes, which enable us to develop nonparametric goodness-of-fit tests for un-normalized density/intensity functions. We apply the kernelized Stein discrepancy tests to discrete distributions (including network models) as well as temporal and spatial point processes. Our experiments demonstrate that the proposed tests typically outperform two-sample tests based on the maximum mean discrepancy, which, unlike our goodness-of-fit tests, assume the availability of exact samples from the null model.</div><div><br></div>

Este trabalho trata de modelos de regressão quantílica. Foi feita uma introdução a essa classe de modelos para motivar a discussão. Em seguida, conceitos inferenciais, como estimação, intervalos de confiança, testes de hipóteses para os parâmetros são discutidos, acompanhados de alguns estudos de simulação. Para analisar a qualidade do ajuste, são apresentados o coeficiente de determinação e um teste de falta de ajuste para modelos de regressão quantílica. Também é proposta a utilização de gráficos para análise da qualidade do ajuste considerando a distribuição Laplace Assimétrica. Uma aplicação utilizando um banco de dados com informação sobre renda no Brasil foi utilizado para exemplificar os tópicos discutidos durante o texto. / This work is about quantile regression models. An introduction was made to this class of models to motivate the discussion. Then, inferential concepts, like estimation, confidence intervals, tests of hypothesis for the parameters are discussed, followed by some simulation studies. To analyse goodness of fit, a coefficient of determination and a lack-of-fit test for quantile regression models are presented. Its also proposed the use of graphs for the goodness of fit analysis considering the Asymmetric Laplace Distribution. An application using a data base with information about income in Brazil was used to exemplify the topics discussed during the text.

Este trabalho trata de modelos de regressão quantílica. Foi feita uma introdução a essa classe de modelos para motivar a discussão. Em seguida, conceitos inferenciais, como estimação, intervalos de confiança, testes de hipóteses para os parâmetros são discutidos, acompanhados de alguns estudos de simulação. Para analisar a qualidade do ajuste, são apresentados o coeficiente de determinação e um teste de falta de ajuste para modelos de regressão quantílica. Também é proposta a utilização de gráficos para análise da qualidade do ajuste considerando a distribuição Laplace Assimétrica. Uma aplicação utilizando um banco de dados com informação sobre renda no Brasil foi utilizado para exemplificar os tópicos discutidos durante o texto. / This work is about quantile regression models. An introduction was made to this class of models to motivate the discussion. Then, inferential concepts, like estimation, confidence intervals, tests of hypothesis for the parameters are discussed, followed by some simulation studies. To analyse goodness of fit, a coefficient of determination and a lack-of-fit test for quantile regression models are presented. Its also proposed the use of graphs for the goodness of fit analysis considering the Asymmetric Laplace Distribution. An application using a data base with information about income in Brazil was used to exemplify the topics discussed during the text.

In this thesis I examine the contemporary nexus between White women and the raced and classed institution of White womanhood. More specifically, I focus on White Australian women who are middle class, rich in cultural capital, and generally consider themselves to be progressive; that is race privileged women but women who are not usually associated with overt racism. My analysis unfolds White Australian women in the discursive context of the ideologies of feminism and feminist-influenced anti-racist politics, as well as the ideologies of femininity. The thesis shows how this nexus is enacted through a vision of White women as Good as expressed in the political commitments, mentalities, relationships, narratives and corporeality of such women. The research problem that I identified and worked through in the thesis is as follows: for middle class White women, (who can be seen and see themselves as generic 'women'), Whiteness has been seen and played out as Goodness. Further, in the playing out of this Goodness White women accumulate and defend the prestige and privileges of Whiteness. Specifically, I argue that Whiteness is reproduced in some of the discourses and practices of White feminism, by the progressive White women involved in anti-racist politics, and in the femininity industry and the ways it is taken up. The nub of the problem I identify is that White women's involvement in the structures and narratives that support Whiteness is often grounded in the very qualities of character and conduct that emerge from the colonial and class-constructed ideal of White womanhood and which have historically distinguished them from denigrated others. These qualities- notably virtue, innocence and self-restraint- whilst differently nuanced in other contexts are an ongoing expression of the uses made of White womanhood as the visible sign of race and class superiority. The work examines four key periods: the Australian colony of PNG during the decolonising 1960's and 1970's; the high years of 1970's and 1980's feminism; the race debates of the 1990's; and the bodily practices of present day White women gripped by fears of fat and aging. I explore the ways in which White women's Whiteness is played out in benevolent Black/White relationships, the over-reach of difference feminism, particular kinds of anti-racist identities and activism, and body-improvement practices. In all these cultural sites, White women's Whiteness is often represented as a kind of moral being and deployed as moral authority in ways that are consonant with the raced and classed construction of White women as moral texts. My research approach was determined by the research problem I identified. Given my argument that White women mis-recognise Whiteness as Goodness in a race-structured society, then the collecting of data through interviews or surveys would have yielded material subject to this blindness. Instead, I explored sites and material where moral claims were being pressed, and case studies where 'women' were enacting themselves or being represented or interpellated as moral texts. My selection of primary source material ranges from feminist newsletters, women's and other magazines, literature, film, event programs and flyers, radio and television broadcasts, newspapers and websites, as well as reflections on my own experiences. Secondary source material includes feminist theoretical texts as well as texts drawn from a range of other disciplines, and other historical background materials. I lay out and support my arguments using a technique not dissimilar to collage, aiming to construct a picture that is compelling in its detail as well as coherent in its overall effect. This thesis is a contribution to the de-naturalisation of Whiteness. Navigating a course between the opposing hazards of essentialising Whiteness and understating its effects in contemporary Australian society, I have brought into clearer view some of the strategies which maintain the authority of Whiteness.

The relative power of goodness-of-fit test statistics has long been debated in the literature. Chi-Square type test statistics to determine 'fit' for categorical data are still dominant in the goodness-of-fit arena. Empirical Distribution Function type goodness-of-fit test statistics are known to be relatively more powerful than Chi-Square type test statistics for restricted types of null and alternative distributions. In many practical applications researchers who use a standard Chi-Square type goodness-of-fit test statistic ignore the rank of ordinal classes. This thesis reviews literature in the goodness-of-fit field, with major emphasis on categorical goodness-of-fit tests. The continued use of an asymptotic distribution to approximate the exact distribution of categorical goodness-of-fit test statistics is discouraged. It is unlikely that an asymptotic distribution will produce a more accurate estimation of the exact distribution of a goodness-of-fit test statistic than a Monte Carlo approximation with a large number of simulations. Due to their relatively higher powers for restricted types of null and alternative distributions, several authors recommend the use of Empirical Distribution Function test statistics over nominal goodness-of-fit test statistics such as Pearson's Chi-Square. In-depth power studies confirm the views of other authors that categorical Empirical Distribution Function type test statistics do not have higher power for some common null and alternative distributions. Because of this, it is not sensible to make a conclusive recommendation to always use an Empirical Distribution Function type test statistic instead of a nominal goodness-of-fit test statistic. Traditionally the recommendation to determine 'fit' for multivariate categorical data is to treat categories as nominal, an approach which precludes any gain in power which may accrue from a ranking, should one or more variables be ordinal. The presence of multiple criteria through multivariate data may result in partially ordered categories, some of which have equal ranking. This thesis proposes a modification to the currently available Kolmogorov-Smirnov test statistics for ordinal and nominal categorical data to account for situations of partially ordered categories. The new test statistic, called the Combined Kolmogorov-Smirnov, is relatively more powerful than Pearson's Chi-Square and the nominal Kolmogorov-Smirnov test statistic for some null and alternative distributions. A recommendation is made to use the new test statistic with higher power in situations where some benefit can be achieved by incorporating an Empirical Distribution Function approach, but the data lack a complete natural ordering of categories. The new and established categorical goodness-of-fit test statistics are demonstrated in the analysis of categorical data with brief applications as diverse as familiarity of defence programs, the number of recruits produced by the Merlin bird, a demographic problem, and DNA profiling of genotypes. The results from these applications confirm the recommendations associated with specific goodness-of-fit test statistics throughout this thesis.