Sensitivity of principal Hessian direction analysis

We provide sensitivity comparisons for two competing versions of the dimension reduction method principal Hessian directions (pHd). These comparisons consider the effects of small perturbations on the estimation of the dimension reduction subspace via the influence function. We show that the two versions of pHd can behave completely differently in the presence of certain observational types. Our results also provide evidence that outliers in the traditional sense may or may not be highly influential in practice. Since influential observations may lurk within otherwise typical data, we consider the influence function in the empirical setting for the efficient detection of influential observations in practice.Comment: Published at http://dx.doi.org/10.1214/07-EJS064 in the Electronic Journal of Statistics (http://www.i-journals.org/ejs/) by the Institute of Mathematical Statistics (http://www.imstat.org

Principal Sensitivity Analysis

We present a novel algorithm (Principal Sensitivity Analysis; PSA) to analyze the knowledge of the classifier obtained from supervised machine learning techniques. In particular, we define principal sensitivity map (PSM) as the direction on the input space to which the trained classifier is most sensitive, and use analogously defined k-th PSM to define a basis for the input space. We train neural networks with artificial data and real data, and apply the algorithm to the obtained supervised classifiers. We then visualize the PSMs to demonstrate the PSA's ability to decompose the knowledge acquired by the trained classifiers

Hearing and Echolocation in the Australian Grey Swiftlet, Collocalia Spodiopygia

The frequency sensitivity of hearing in the grey swiftlet, Collocalia spodiopygia, was determined by neuronal recordings from the auditory midbrain (MLD). The most sensitive best frequency response thresholds occurred between 0.8 and 4.7 kHz, with the upper frequency limit near 6 kHz. Spectral analysis of echolocation click pairs revealed energy peaks between 3.0 and 8.0kHz for the foreclick, compared to 4.0-6.0 kHz for the principal click. The relationship between good hearing sensitivity and click energy peaks in the swiftlet extends about an octave higher than it does in the oilbird (Steatornis caripensis)

Multiple chemical sensitivity syndrome. A principal component analysis of symptoms

Multiple Chemical Sensitivity (MCS) is a chronic and/or recurrent condition with somatic, cognitive, and affective symptoms following a contact with chemical agents whose concentrations do not correlate with toxicity in the general population. Its prevalence is not well defined; it mainly affects women between 40 and 50 years, without variations in ethnicity, education and economic status. We aimed to assess the core symptoms of this illness in a sample of Italian patients. Two physicians investigated different symptoms with a checklist compilation in 129 patients with MCS (117 women). We conducted a categorical Principal Component Analysis (CATPCA) with Varimax rotation on the checklist dataset. A typical triad was documented: hyperosmia, asthenia, and dyspnoea were the most common symptoms. Patients also frequently showed cough and headache. The CATPCA showed seven main factors: 1, neurocognitive symptoms; 2, physical (objective) symptoms; 3, gastrointestinal symptoms; 4, dermatological symptoms; 5, anxiety-depressive symptoms; 6, respiratory symptoms; 7, hyperosmia and asthenia. Patients showed higher mean prevalence of factors 7 (89.9%), 6 (71.7%), and 1 (62.13%). In conclusion, MCS patients frequently manifest hyperosmia, asthenia, and dyspnoea, which are often concomitant with other respiratory and neurocognitive symptoms. Considering the clinical association that is often made with anxiety, more studies are necessary on the psychosomatic aspects of this syndrome. Further analytical epidemiological studies are needed to support the formulation of aetiological hypotheses of MCS

The Relevance of the Policies of Development in the Agri-Food Sector of Emilia-Romagna Region

The aim of this paper is to analysis the impact of the policies of development and investments in the food industry at geographical and enterprises levels. We will analysis how the different geographical areas inside the Italian region Emilia-Romagna are sensitive to the development policies. A Principal Component Analysis and a Cluster Analysis will be applied to determine the most homogeneous geographical areas with respect to the considered variables. Then for evaluating the sensibility of these areas with respect to changes in investments and policies for food industry enterprise will be applied a Multicriterial Analysis and a Sensitivity Analysis.Agri-food sector, Development Policies, Food industry, Multicriterial Analysis, Region Emilia-Romagna, Sensitivity Analysis, Community/Rural/Urban Development,