The Secret Aria on Shame : An Analysis of Narrative Structure and Theme in Coetzee's "Diary of a Bad Year"

Coetzee’s last novel Diary of a Bad Year (2007) has an intriguing triple-voiced narrative structure and deals with the grey area of shame. The narrative is divided between a writer, his written contribution to a book called “Strong Opinions”, and his secretary’s thoughts about both the opinions in the manuscript and her employer’s circumstances. This essay explores the relation between form and theme in Diary of a Bad Year; to see in what way these two fundamental elements of the novel intervene and support each other. By doing so the narrative structure is read through Freud’s structural model of personality, whereby each narrator’s voice is related to the notions of the super-ego, the ego and the id. In other words, this essay argues that the specific threefold narrative structure in Diary of a Bad Year, by reflecting the interrelated parts of human identity, helps in creating and developing the theme of shame, which only exists connected to the human psyche. This connection in turn gives special meaning to the entire narratology of the novel.

A Note on Proxy Variables and Instrument Variable Regression

This is a note about proxy variables and instruments for identification of structural parameters in regression models. We have experienced that in the econometric textbooks these two issues are treated separately, although in practice these two concepts are very often combined. Usually, proxy variables are inserted in instrument variable regressions with the motivation they are exogenous. Implicitly meaning they are exogenous in a reduced form model and not in a structural model. Actually if these variables are exogenous they should be redundant in the structural model, e.g. IQ as a proxy for ability. Valid proxies reduce unexplained variation and increases the efficiency of the estimator of the structural parameter of interest. This is especially important in situations when the instrument is weak. With a simple example we demonstrate what is required of a proxy and an instrument when they are combined. It turns out that when a researcher has a valid instrument the requirements on the proxy variable is weaker than if no such instrument exists

Review of the literature on credit risk modeling : development of the past 10 years

This paper traces the developments of credit risk modeling in the past 10 years. Our work can be divided into two parts: selecting articles and summarizing results. On the one hand, by constructing an ordered logit model on historical Journal of Economic Literature (JEL) codes of articles about credit risk modeling, we sort out articles which are the most related to our topic. The result indicates that the JEL codes have become the standard to classify researches in credit risk modeling. On the other hand, comparing with the classical review Altman and Saunders(1998), we observe some important changes of research methods of credit risk. The main finding is that current focuses on credit risk modeling have moved from static individual-level models to dynamic portfolio models.

Estimation of genetic variance for macro- and micro-environmental sensitivity using double hierarchical generalized linear models

Background: Genetic variation for environmental sensitivity indicates that animals are genetically different in their response to environmental factors. Environmental factors are either identifiable (e.g. temperature) and called macro-environmental or unknown and called micro-environmental. The objectives of this study were to develop a statistical method to estimate genetic parameters for macro- and micro-environmental sensitivities simultaneously, to investigate bias and precision of resulting estimates of genetic parameters and to develop and evaluate use of Akaike’s information criterion using h-likelihood to select the best fitting model. Methods: We assumed that genetic variation in macro- and micro-environmental sensitivities is expressed as genetic variance in the slope of a linear reaction norm and environmental variance, respectively. A reaction norm model to estimate genetic variance for macro-environmental sensitivity was combined with a structural model for residual variance to estimate genetic variance for micro-environmental sensitivity using a double hierarchical generalized linear model in ASReml. Akaike’s information criterion was constructed as model selection criterion using approximated h-likelihood. Populations of sires with large half-sib offspring groups were simulated to investigate bias and precision of estimated genetic parameters. Results: Designs with 100 sires, each with at least 100 offspring, are required to have standard deviations of estimated variances lower than 50% of the true value. When the number of offspring increased, standard deviations of estimates across replicates decreased substantially, especially for genetic variances of macro- and micro-environmental sensitivities. Standard deviations of estimated genetic correlations across replicates were quite large (between 0.1 and 0.4), especially when sires had few offspring. Practically, no bias was observed for estimates of any of the parameters. Using Akaike’s information criterion the true genetic model was selected as the best statistical model in at least 90% of 100 replicates when the number of offspring per sire was 100. Application of the model to lactation milk yield in dairy cattle showed that genetic variance for micro- and macro-environmental sensitivities existed. Conclusion: The algorithm and model selection criterion presented here can contribute to better understand genetic control of macro- and micro-environmental sensitivities. Designs or datasets should have at least 100 sires each with 100 offspring.

Internal Control application : Empirical evidence from Sweden

Aim The aim of this study is to explore based on internationally recognised frameworks: 1. how internal control structures are applied in Sweden among different sectors; 2. how organizational size and environment affect internal control structures; and 3. the impact of internal control structures on organizational performance. Methods A quantitative method was used in the data collection and analysis. The sample consisted of 1117 organizations operating in Sweden. A mean analysis was conducted to measure the level of internal control structures among different industries, organizational sizes, and different choices of listing in the stock exchange market. Person’s correlation analysis was then used to explore possible correlations between external environmental factors and internal control structures, and internal control structures and organizational performance. Lastly, a structural model was built to measure the impact of internal control structures on organizational performance. The measurements of internal control structures and organizational performance are based on COSO framework’s principles and objectives. Results This study gives an insight on how internal control structures are applied across industrial sectors in Sweden, with financial institutions and manufacturing organizations having notably higher levels of internal control structures. Additionally, it provides evidence of the impact external environmental factors have on internal control structures. Furthermore, it shows that organizations that are listed in the Swedish stock exchange market have an equivalent level of internal control structures to those registered in the American stock exchange market. In contrast, organisations that are not listed in the stock exchange market have a notably lower level of internal control structures. Lastly, it illustrates the positive impact the presence of internal control structures has on organizational performance. 3 | P a g e Conclusion The results highlight a crucial role the supervisory authority Finansinspektionen (FI) has in regulating the Swedish financial market. They also show that the stability of the Swedish business environment has had a positive impact on the level of internal control structures.

A pharmacokinetic-pharmacodynamic model for duodenal levodopa infusion

Objective Levodopa in presence of decarboxylase inhibitors is following two-compartment kinetics and its effect is typically modelled using sigmoid Emax models. Pharmacokinetic modelling of the absorption phase of oral distributions is problematic because of irregular gastric emptying. The purpose of this work was to identify and estimate a population pharmacokinetic- pharmacodynamic model for duodenal infusion of levodopa/carbidopa (Duodopa®) that can be used for in numero simulation of treatment strategies. Methods The modelling involved pooling data from two studies and fixing some parameters to values found in literature (Chan et al. J Pharmacokinet Pharmacodyn. 2005 Aug;32(3-4):307-31). The first study involved 12 patients on 3 occasions and is described in Nyholm et al. Clinical Neuropharmacology 2003:26:156-63. The second study, PEDAL, involved 3 patients on 2 occasions. A bolus dose (normal morning dose plus 50%) was given after a washout during night. Plasma samples and motor ratings (clinical assessment of motor function from video recordings on a treatment response scale between -3 and 3, where -3 represents severe parkinsonism and 3 represents severe dyskinesia.) were repeatedly collected until the clinical effect was back at baseline. At this point, the usual infusion rate was started and sampling continued for another two hours. Different structural absorption models and effect models were evaluated using the value of the objective function in the NONMEM package. Population mean parameter values, standard error of estimates (SE) and if possible, interindividual/interoccasion variability (IIV/IOV) were estimated. Results Our results indicate that Duodopa absorption can be modelled with an absorption compartment with an added bioavailability fraction and a lag time. The most successful effect model was of sigmoid Emax type with a steep Hill coefficient and an effect compartment delay. Estimated parameter values are presented in the table. Conclusions The absorption and effect models were reasonably successful in fitting observed data and can be used in simulation experiments.