Consumerism in urban Russia in the 1880s and 90s: Exploring conceptions of time, space, and modernity through bicycle and watch advertisements

This paper discusses the urban consumer culture in Moscow and Petersburg during the 1880s and 1890s and uses the consumption of bicycles and watches as a lens through which to explore changing perceptions of time and space within the experience of modernity at the end of the nineteenth century. Specifically, I argue that the way in which consumers and merchants constructed a dialogue of meaning around particular objects; the way in which objects are consumed by a culture gives insight into the values, morals, and tenure of that culture. The paper preferences newspaper ads and photographs as the mouthpieces of merchants and consumers respectively as they constructed a dialogue in the language of consumerism, and explores the ways in which both parties sought to assign meaning to objects during the experience of modernity. I am particularly interested in the way consumers perform elements of cultural modernity in photographs and how these instances of performance relate to their negotiation of modernity. The paper takes as its focus large section of the urban Russian population, much of whom can traditionally be called “middle class” but whose diversity has led me to the adoption of the term “consumer community,” and whose makeup is described in detail. The paper contributes to the continuing scholarly discourse on the makeup of the middle class in Russia and the social boundaries of late tsarist society. It speaks to the the developing sensibilities and values of a generation struggling to define itself in a rapidly changing world, to the ways in which conceptualizations of public and private space, as well as feminine and masculine space were redefined, and to the developing visual culture of the Russian consumer society, largely predicated on the display of objects to signify socially desirable traits. Whereas other explorations of consumer culture and advertisements have portrayed the relationship between merchants and consumers as a one-sided monologue in which merchants convince consumers that certain objects have cultural value, I emphasis the dialogue between merchants and consumers, and their mutual negotiation of cultural meaning through objects.

Statistical modeling of protein lysate array data

The protein lysate array is an emerging technology for quantifying the protein concentration ratios in multiple biological samples. It is gaining popularity, and has the potential to answer questions about post-translational modifications and protein pathway relationships. Statistical inference for a parametric quantification procedure has been inadequately addressed in the literature, mainly due to two challenges: the increasing dimension of the parameter space and the need to account for dependence in the data. Each chapter of this thesis addresses one of these issues. In Chapter 1, an introduction to the protein lysate array quantification is presented, followed by the motivations and goals for this thesis work. In Chapter 2, we develop a multi-step procedure for the Sigmoidal models, ensuring consistent estimation of the concentration level with full asymptotic efficiency. The results obtained in this chapter justify inferential procedures based on large-sample approximations. Simulation studies and real data analysis are used to illustrate the performance of the proposed method in finite-samples. The multi-step procedure is simpler in both theory and computation than the single-step least squares method that has been used in current practice. In Chapter 3, we introduce a new model to account for the dependence structure of the errors by a nonlinear mixed effects model. We consider a method to approximate the maximum likelihood estimator of all the parameters. Using the simulation studies on various error structures, we show that for data with non-i.i.d. errors the proposed method leads to more accurate estimates and better confidence intervals than the existing single-step least squares method.