Search results for "Multivariate statistics"
showing 10 items of 290 documents
On the internal multivariate quality control of analytical laboratories. A case study: the quality of drinking water
2001
Abstract Multivariate statistical process control (MSPC) tools, based on principal component analysis (PCA), partial least squares (PLS) regression and other regression models, are used in the present study for automatic detection of possible errors in the methods used for routine multiparametric analysis in order to design an internal Multivariate Analytical Quality Control (iMAQC) program. Such tools could notice possible failures in the analytical methods without resorting to any external reference since they use their own analytical results as a source for the diagnosis of the method's quality. Pseudo-univariate control charts provide an attractive alternative to traditional univariate …
Multivariate SPC of a sequencing batch reactor for wastewater treatment
2007
Data from a sequencing batch reactor (SBR) operated for enhanced biological phosphorus removal from wastewater have been analysed in order to propose an efficient MSPC scheme of the process. Different multivariate bilinear approaches have been applied and compared in terms of their capabilities for on-line and off-line fault detection and diagnosis. The typical three-way data structure from a batch process was unfolded batch-wise and variable-wise. In the latter case, two models were built: with (AT) and without (WKFH) removing the main non-linear behaviour of the process data. Since the process consists of several stages, the monitoring strategies tested include: one model for all stages a…
Les déterminants de la réussite scolaire à l'école primaire au Brésil: une analyse multifactorielle
2016
RESUMEN: Una problemática educativa actual en Brasil es el alto porcentaje de alumnos en la escolaridad primaria que muestran un bajo nivel académico. Esta situación justifica el análisis de los determinantes del rendimiento académico en matemáticas para la totalidad de alumnos y su comparación al colectivo con un rendimiento considerado insuficiente. Para ello, se emplea una aproximación lineal y multivariante a partir de los datos contenidos en la base de datos del Sistema de Avaliação da Educação Básica 2005. En conjunto, los resultados obtenidos permiten afirmar que las dimensiones asociadas a la educabilidad resultan determinantes para comprender los rendimientos observados en la educa…
Stochastic Approximation for Multivariate and Functional Median
2010
We propose a very simple algorithm in order to estimate the geometric median, also called spatial median, of multivariate (Small (1990)) or functional data (Gervini (2008)) when the sample size is large. A simple and fast iterative approach based on the Robbins-Monro algorithm (Duflo (1997)) as well as its averaged version (Polyak and Juditsky (1992)) are shown to be effective for large samples of high dimension data. They are very fast and only require O(Nd) elementary operations, where N is the sample size and d is the dimension of data. The averaged approach is shown to be more effective and less sensitive to the tuning parameter. The ability of this new estimator to estimate accurately …
Data Banks and Multivariate Statistics in Physical Anthropology
1984
In recent decades, the fields of administration and economy, the press and - last but not least - the sciences have been characterized by an “explosion of knowledge”, and, as a consequence, by the problem of managing the rapidly increasing mass of information. It has been estimated that knowledge doubles each five years, and even that the interval of doubling seems to decrease. The main response to this challenge are computerized and structured data collections called data banks. “Data banks are systems of data collections which are organized according to logical and/or formal criteria; they should make it possible to reproduce the data of the total collection arranged according to differen…
An extended Benthic Quality Index for assessment of lake profundal macroinvertebrates: addition of indicator taxa by multivariate ordination and weig…
2014
AbstractThe chironomid Benthic Quality Index (BQI) is a widely used metric in assessments of lake status. The BQI is based on 7 indicator taxa, which like most profundal fauna, often occur sporadically in low densities. Hence, a major weakness of the index is that it cannot be calculated when indicator taxa are not captured. Thus, an extension of the BQI that incorporates more macroinvertebrate taxa is desirable. We used 2 statistical approaches (Detrended Correspondence Analysis and Weighted Averaging) to estimate new benthic quality indicator scores for profundal macroinvertebrate taxa and to construct modified BQIs called Profundal Invertebrate Community Metrics (PICMs). We calibrated th…
Modelling systemic price cojumps with Hawkes factor models
2015
Instabilities in the price dynamics of a large number of financial assets are a clear sign of systemic events. By investigating a set of 20 high cap stocks traded at the Italian Stock Exchange, we find that there is a large number of high frequency cojumps. We show that the dynamics of these jumps is described neither by a multivariate Poisson nor by a multivariate Hawkes model. We introduce a Hawkes one factor model which is able to capture simultaneously the time clustering of jumps and the high synchronization of jumps across assets.
GIS-based groundwater potential mapping in Shahroud plain, Iran. A comparison among statistical (bivariate and multivariate), data mining and MCDM ap…
2019
Abstract In arid and semi-arid areas, groundwater resource is one of the most important water sources by the humankind. Knowledge of groundwater distribution over space, associated flow and basic exploitation measures can play a significant role in planning sustainable development, especially in arid and semi-arid areas. Groundwater potential mapping (GWPM) fits in this context as the tool used to predict the spatial distribution of groundwater. In this research we tested four GIS-based models for GWPM, consisting of: i) random forest (RF); ii) weight of evidence (WoE); iii) binary logistic regression (BLR); and iv) technique for order preference by similarity to ideal solution (TOPSIS) mul…
On the use of adaptive spatial weight matrices from disease mapping multivariate analyses
2020
Conditional autoregressive distributions are commonly used to model spatial dependence between nearby geographic units in disease mapping studies. These distributions induce spatial dependence by means of a spatial weights matrix that quantifies the strength of dependence between any two neighboring spatial units. The most common procedure for defining that spatial weights matrix is using an adjacency criterion. In that case, all pairs of spatial units with adjacent borders are given the same weight (typically 1) and the remaining non-adjacent units are assigned a weight of 0. However, assuming all spatial neighbors in a model to be equally influential could be possibly a too rigid or inapp…
Visitor arrivals forecasts amid COVID-19: A perspective from the Africa team
2021
Abstract COVID-19 disrupted international tourism worldwide, subsequently presenting forecasters with a challenging conundrum. In this competition, we predict international arrivals for 20 destinations in two phases: (i) Ex post forecasts pre-COVID; (ii) Ex ante forecasts during and after the pandemic up to end 2021. Our results show that univariate combined with cross-sectional hierarchical forecasting techniques (THieF-ETS) outperform multivariate models pre-COVID. Scenarios were developed based on judgemental adjustment of the THieF-ETS baseline forecasts. Analysts provided a regional view on the most likely path to normal, based on country-specific regulations, macroeconomic conditions,…