Design-based random permutation models with auxiliary information

We extend the random permutation model to obtain the best linear unbiased estimator of a finite population mean accounting for auxiliary variables under simple random sampling without replacement (SRS) or stratified SRS. The proposed method provides a systematic design-based justification for well-known results involving common estimators derived under minimal assumptions that do not require specification of a functional relationship between the response and the auxiliary variables.

Employing Distances in Design-based Spatial Estimation

In the last couple of decades we assisted to a reappraisal of spatial design-based techniques. Usually the spatial information regarding the spatial location of the individuals of a population has been used to develop efficient sampling designs. This thesis aims at offering a new technique for both inference on individual values and global population values able to employ the spatial information available before sampling at estimation level by rewriting a deterministic interpolator under a design-based framework. The achieved point estimator of the individual values is treated both in the case of finite spatial populations and continuous spatial domains, while the theory on the estimator of the population global value covers the finite population case only. A fairly broad simulation study compares the results of the point estimator with the simple random sampling without replacement estimator in predictive form and the kriging, which is the benchmark technique for inference on spatial data. The Monte Carlo experiment is carried out on populations generated according to different superpopulation methods in order to manage different aspects of the spatial structure. The simulation outcomes point out that the proposed point estimator has almost the same behaviour as the kriging predictor regardless of the parameters adopted for generating the populations, especially for low sampling fractions. Moreover, the use of the spatial information improves substantially design-based spatial inference on individual values.

Process design based on CO 2-expanded liquids as solvents

Magdeburg, Univ., Fak. für Verfahrens- und Systemtechnik, Diss., 2014

Finite-Sample Simulation-Based Inference in VAR Models with Applications to Order Selection and Causality Testing

Statistical tests in vector autoregressive (VAR) models are typically based on large-sample approximations, involving the use of asymptotic distributions or bootstrap techniques. After documenting that such methods can be very misleading even with fairly large samples, especially when the number of lags or the number of equations is not small, we propose a general simulation-based technique that allows one to control completely the level of tests in parametric VAR models. In particular, we show that maximized Monte Carlo tests [Dufour (2002)] can provide provably exact tests for such models, whether they are stationary or integrated. Applications to order selection and causality testing are considered as special cases. The technique developed is applied to quarterly and monthly VAR models of the U.S. economy, comprising income, money, interest rates and prices, over the period 1965-1996.

Extracting Urban Food Potential: design-based methods for digital and bodily cartography

Sweden’s recent report on Urban Sustainable Development calls out a missing link between the urban design process and citizens. This paper investigates if engaging citizens as design agents by providing a platform for alternate participation can bridge this gap, through the transfer of spatial agency and new modes of critical cartography. To assess whether this is the case, the approaches are applied to Stockholm’s urban agriculture movement in a staged intervention. The aim of the intervention was to engage citizens in locating existing and potential places for growing food and in gathering information from these sites to inform design in urban agriculture. The design-based methodologies incorporated digital and bodily interfaces for this cartography to take place. The Urban CoMapper, a smartphone digital app, captured real-time perspectives through crowd-sourced mapping. In the bodily cartography, participant’s used their bodies to trace the site and reveal their sensorial perceptions. The data gathered from these approaches gave way to a mode of artistic research for exploring urban agriculture, along with inviting artists to be engaged in the dialogues. In sum, results showed that a combination of digital and bodily approaches was necessary for a critical cartography if we want to engage citizens holistically into the urban design process as spatial agents informing urban policy. Such methodologies formed a reflective interrogation and encouraged a new intimacy with nature, in this instance, one that can transform our urban conduct by questioning our eating habits: where we get our food from and how we eat it seasonally.

The relative success of recognition-based inference in multichoice decisions

The utility of an "ecologically rational" recognition-based decision rule in multichoice decision problems is analyzed, varying the type of judgment required (greater or lesser). The maximum size and range of a counterintuitive advantage associated with recognition-based judgment (the "less-is-more effect") is identified for a range of cue validity values. Greater ranges of the less-is-more effect occur when participants are asked which is the greatest of to choices (m > 2) than which is the least. Less-is-more effects also have greater range for larger values of in. This implies that the classic two-altemative forced choice task, as studied by Goldstein and Gigerenzer (2002), may not be the most appropriate test case for less-is-more effects.

Residual-based Inference for Common Nonlinear Features

This paper investigates common nonlinear features in multivariate nonlinear autore-gressive models via testing the estimated residuals. A Wald-type test is proposed and itis asymptotically Chi-squared distributed. Simulation studies are given to examine thefinite-sample properties of the proposed test.

Advances in drug design based on the amino acid approach: Taurine analogues for the treatment of CNS diseases

Amino acids are well known to be an important class of compounds for the maintenance of body homeostasis and their deficit, even for the polar neuroactive aminoacids, can be controlled by supplementation. However, for the amino acid taurine (2-aminoethanesulfonic acid) this is not true. Due its special physicochemical properties, taurine is unable to cross the blood-brain barrier. In addition of injured taurine transport systems under pathological conditions, CNS supplementation of taurine is almost null. Taurine is a potent antioxidant and anti-inflammatory semi-essential amino acid extensively involved in neurological activities, acting as neurotrophic factor, binding to GABA A/glycine receptors and blocking the excitotoxicity glutamate-induced pathway leading to be a neuroprotective effect and neuromodulation. Taurine deficits have been implicated in several CNS diseases, such as Alzheimer's, Parkinson's, epilepsy and in the damage of retinal neurons. This review describes the CNS physiological functions of taurine and the development of new derivatives based on its structure useful in CNS disease treatment.&; 2012 by the authors; licensee MDPI, Basel, Switzerland.

Development of Cationic Solid Lipid Nanoparticles with Factorial Design-Based Studies for Topical Administration of Doxorubicin

Topical chemotherapy using doxorubicin, a powerful anticancer drug, can be used as an alternative with reduced systemic toxicity when treating skin cancer. The aim of the present work was to use factorial design-based studies to develop cationic solid lipid nanoparticles containing doxorubicin; further investigations into the influence of these particles on the drug's cytotoxicity and cellular uptake in B16F10 murine melanoma cells were performed. A 3(2) full factorial design was applied for two different lipid phases; one phase used stearic acid and the other used a 1:2 mixture of stearic acid and glyceryl behenate. The two factors investigated included the ratio between the lipid and the water phase and the ratio between the surfactant (poloxamer) and the co-surfactant (cetylpyridinium chloride). It was observed that the studied factors did not affect the mean diameter or the polydispersity of the obtained nanoparticles; however, they did significantly affect the zeta potential values. Optimised formulations with particle sizes ranging from 251 to 306 nm and positive zeta potentials were selected for doxorubicin incorporation. High entrapment efficiencies were achieved (97%) in formulations with higher amounts of stearic acid, suggesting that cationic charges on doxorubicin molecules may interact with the negative charges in stearic acid. Melanoma culture cell experiments showed that cationic solid lipid nanoparticles without drug were not cytotoxic to melanoma cells. The encapsulation of doxorubicin significantly increased cytotoxicity, indicating the potential of these nanoparticles for the treatment of skin cancer.

Likelihood-based inference for power distributions

This paper considers likelihood-based inference for the family of power distributions. Widely applicable results are presented which can be used to conduct inference for all three parameters of the general location-scale extension of the family. More specific results are given for the special case of the power normal model. The analysis of a large data set, formed from density measurements for a certain type of pollen, illustrates the application of the family and the results for likelihood-based inference. Throughout, comparisons are made with analogous results for the direct parametrisation of the skew-normal distribution.