Sysselsättningseffekter i svenska aktiebolag av införandet av RUT-avdraget : En Difference-in-Differenceanalys

Detta arbete har gjorts med syftet att utvärdera sysselsättningseffekterna i svenska aktiebolag av införandet av RUT-avdraget. RUT-avdraget infördes 2007 och innebär att privatpersoner kan få göra skattereduktion för olika typer av hushållsarbeten. Datamaterialet som används i denna studie är bokföringsdata för alla Sveriges aktiebolag mellan 2000 – 2010, aggregerat till tresiffriga SNI-koder för alla de svenska kommunerna. Utifrån datamaterialet har RUT-avdragets sysselsättningseffekter analyserats med hjälp av en Difference-in-Differencemodell. Resultatet visar att RUT-avdraget gjort att 6930 nya arbeten har skapats i de svenska aktiebolag som ingår i RUT-sektorn. Detta innebär alltså att RUT-avdraget har haft en positiv effekt på sysselsättningen.

Sjuksköterskans kunskaper i relation till de äldres läkemedelsbehandling på särskilda boende

Syfte med detta arbete var attbelysa den aktuella situationen i läkemedelsanvändningen på fyra äldreboende i mellersta Sverige. Studien utvisade, med hjälp av en enkät innehållande 27 frågor, skillnader i åtta sjuksköterskors kunskaper om läkemedel och prioritering av läkemedelsgenomgångar på arbetsplatserna. Granskningen visade även hur många läkemedel som användes genomsnittligt per boendeenhet, hur många fallolyckor som inträffade samt hur ofta och länge sjukhusvård behövdes under den sexmånader långa undersökningsperioden. Dessutom undersöktes om antalet läkemedel och antalet fallolyckor var relaterade till den omvårdnadsansvariga sjuksköterskans kunskaper om läkemedel. Antalet förskrivna läkemedel fanns tillgängliga med hjälp av Apotekens e-dos system. Omvård-nadsansvariga sjuksköterskor tillhandahöll sammanlagt 134 läkemedelslistor. Resultatet visade på basen av läkemedelslistorna på de fyra äldreboende att dessa låg på signifikant olika nivåer beträffande antalet genomsnittligt förskrivna läkemedel per enhet. Vidare skiljde sig sjuksköterskornas kunskaper om läkemedel för äldre åt och kunskaperna relaterade till prioriteringar av läkemedelsgenomgångar, användning av professionella hjälpmedel samt till vilja till utbildning på egen tid. Vidare hade äldreboenden olika antal fallolyckor och utnyttjade i olika grad sjukhusvård. Antal fallolyckor förklarades av antalet förskrivna läkemedel per boende per dag till 95,2 % och följaktligen antal sjukhusdagar förklarades med antal läkemedel per person per dag till 86,5 % men hjälp av linjär regression, modell Enter. Resultatet diskuterades utgående från Dorothea Orems omvårdnadsteori att den äldre har egenvårdsbehov i egen farmakologisk terapi men saknar egenvårdskapacitet i densamma, vilket innebär att sjuksköterskan har största ansvaret att tolka och förmedla patientens läkemedelsbehov.

Elicitator : an expert elicitation tool for regression in ecology

Expert elicitation is the process of retrieving and quantifying expert knowledge in a particular domain. Such information is of particular value when the empirical data is expensive, limited, or unreliable. This paper describes a new software tool, called Elicitator, which assists in quantifying expert knowledge in a form suitable for use as a prior model in Bayesian regression. Potential environmental domains for applying this elicitation tool include habitat modeling, assessing detectability or eradication, ecological condition assessments, risk analysis, and quantifying inputs to complex models of ecological processes. The tool has been developed to be user-friendly, extensible, and facilitate consistent and repeatable elicitation of expert knowledge across these various domains. We demonstrate its application to elicitation for logistic regression in a geographically based ecological context. The underlying statistical methodology is also novel, utilizing an indirect elicitation approach to target expert knowledge on a case-by-case basis. For several elicitation sites (or cases), experts are asked simply to quantify their estimated ecological response (e.g. probability of presence), and its range of plausible values, after inspecting (habitat) covariates via GIS.

Comparison of three expert elicitation methods for logistic regression on predicting the presence of the threatened brush-tailed rock-wallaby petrogale penicillata

Numerous expert elicitation methods have been suggested for generalised linear models (GLMs). This paper compares three relatively new approaches to eliciting expert knowledge in a form suitable for Bayesian logistic regression. These methods were trialled on two experts in order to model the habitat suitability of the threatened Australian brush-tailed rock-wallaby (Petrogale penicillata). The first elicitation approach is a geographically assisted indirect predictive method with a geographic information system (GIS) interface. The second approach is a predictive indirect method which uses an interactive graphical tool. The third method uses a questionnaire to elicit expert knowledge directly about the impact of a habitat variable on the response. Two variables (slope and aspect) are used to examine prior and posterior distributions of the three methods. The results indicate that there are some similarities and dissimilarities between the expert informed priors of the two experts formulated from the different approaches. The choice of elicitation method depends on the statistical knowledge of the expert, their mapping skills, time constraints, accessibility to experts and funding available. This trial reveals that expert knowledge can be important when modelling rare event data, such as threatened species, because experts can provide additional information that may not be represented in the dataset. However care must be taken with the way in which this information is elicited and formulated.

In vivo knockdown of the androgen receptor results in growth inhibition and regression of well-established, castration-resistant prostate tumours

Purpose: Progression to the castration-resistant state is the incurable and lethal end stage of prostate cancer, and there is strong evidence that androgen receptor (AR) still plays a central role in this process. We hypothesize that knocking down AR will have a major effect on inhibiting growth of castration-resistant tumors. Experimental Design: Castration-resistant C4-2 human prostate cancer cells stably expressing a tetracycline-inducible AR-targeted short hairpin RNA (shRNA) were generated to directly test the effects of AR knockdown in C4-2 human prostate cancer cells and tumors. Results:In vitro expression of AR shRNA resulted in decreased levels of AR mRNA and protein, decreased expression of prostate-specific antigen (PSA), reduced activation of the PSA-luciferase reporter, and growth inhibition of C4-2 cells. Gene microarray analyses revealed that AR knockdown under hormone-deprived conditions resulted in activation of genes involved in apoptosis, cell cycle regulation, protein synthesis, and tumorigenesis. To ensure that tumors were truly castration-resistant in vivo, inducible AR shRNA expressing C4-2 tumors were grown in castrated mice to an average volume of 450 mm3. In all of the animals, serum PSA decreased, and in 50% of them, there was complete tumor regression and disappearance of serum PSA. Conclusions: Whereas castration is ineffective in castration-resistant prostate tumors, knockdown of AR can decrease serum PSA, inhibit tumor growth, and frequently cause tumor regression. This study is the first direct evidence that knockdown of AR is a viable therapeutic strategy for treatment of prostate tumors that have already progressed to the castration-resistant state.

Iteratively specified tree-based regression : theory and trip generation example

Focuses on a study which introduced an iterative modeling method that combines properties of ordinary least squares (OLS) with hierarchical tree-based regression (HTBR) in transportation engineering. Information on OLS and HTBR; Comparison and contrasts of OLS and HTBR; Conclusions.

Poisson, Poisson-gamma and zero-inflated regression models of motor vehicle crashes: balancing statistical fit and theory

There has been considerable research conducted over the last 20 years focused on predicting motor vehicle crashes on transportation facilities. The range of statistical models commonly applied includes binomial, Poisson, Poisson-gamma (or negative binomial), zero-inflated Poisson and negative binomial models (ZIP and ZINB), and multinomial probability models. Given the range of possible modeling approaches and the host of assumptions with each modeling approach, making an intelligent choice for modeling motor vehicle crash data is difficult. There is little discussion in the literature comparing different statistical modeling approaches, identifying which statistical models are most appropriate for modeling crash data, and providing a strong justification from basic crash principles. In the recent literature, it has been suggested that the motor vehicle crash process can successfully be modeled by assuming a dual-state data-generating process, which implies that entities (e.g., intersections, road segments, pedestrian crossings, etc.) exist in one of two states—perfectly safe and unsafe. As a result, the ZIP and ZINB are two models that have been applied to account for the preponderance of “excess” zeros frequently observed in crash count data. The objective of this study is to provide defensible guidance on how to appropriate model crash data. We first examine the motor vehicle crash process using theoretical principles and a basic understanding of the crash process. It is shown that the fundamental crash process follows a Bernoulli trial with unequal probability of independent events, also known as Poisson trials. We examine the evolution of statistical models as they apply to the motor vehicle crash process, and indicate how well they statistically approximate the crash process. We also present the theory behind dual-state process count models, and note why they have become popular for modeling crash data. A simulation experiment is then conducted to demonstrate how crash data give rise to “excess” zeros frequently observed in crash data. It is shown that the Poisson and other mixed probabilistic structures are approximations assumed for modeling the motor vehicle crash process. Furthermore, it is demonstrated that under certain (fairly common) circumstances excess zeros are observed—and that these circumstances arise from low exposure and/or inappropriate selection of time/space scales and not an underlying dual state process. In conclusion, carefully selecting the time/space scales for analysis, including an improved set of explanatory variables and/or unobserved heterogeneity effects in count regression models, or applying small-area statistical methods (observations with low exposure) represent the most defensible modeling approaches for datasets with a preponderance of zeros

Multi-step ahead direct prediction for the machine condition prognosis using regression trees and neuro-fuzzy systems

This paper presents an approach to predict the operating conditions of machine based on classification and regression trees (CART) and adaptive neuro-fuzzy inference system (ANFIS) in association with direct prediction strategy for multi-step ahead prediction of time series techniques. In this study, the number of available observations and the number of predicted steps are initially determined by using false nearest neighbor method and auto mutual information technique, respectively. These values are subsequently utilized as inputs for prediction models to forecast the future values of the machines’ operating conditions. The performance of the proposed approach is then evaluated by using real trending data of low methane compressor. A comparative study of the predicted results obtained from CART and ANFIS models is also carried out to appraise the prediction capability of these models. The results show that the ANFIS prediction model can track the change in machine conditions and has the potential for using as a tool to machine fault prognosis.