Search results for "multivariate statistic"
showing 10 items of 327 documents
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,…
Who is willing to pay for science? On the relationship between public perception of science and the attitude to public funding of science.
2012
This article examines the relationship between the general public's understanding of science and the attitude towards public funding of scientific research. It applies a multivariate and discriminant analysis (Wilks' Lambda), in addition to a more commonly used bivariate analysis (Cramer's V), to data compiled from the Third National Survey on the Social Perception of Science and Technology in Spain (FECYT, 2006). The general conclusion is that the multivariate analysis produces information complementary to the bivariate analysis, and that the variables commonly applied in public perception studies have limited predictive value with respect to the attitude towards public funding of scientif…
Probabilistic Flood Hazard Mapping Using Bivariate Analysis Based on Copulas
2017
This study presents a methodology to extract probabilistic flood hazard maps in an area subject to flood risk, taking into account uncertainties in the definition of design hydrographs. Particularly, the authors present a new method to produce probabilistic inundation and flood hazard maps in which the hydrological input (i.e., synthetic flood design event) to a 2D hydraulic model has been obtained by using a bivariate statistical analysis (copulas) to generate flood peak discharges and volumes. This study also aims to quantify the contribution of boundary conditions’ uncertainty in order to evaluate the effect of this uncertainty source on probabilistic flood hazard mapping. Different comb…
Testing Equality of Multiple Power Spectral Density Matrices
2018
This paper studies the existence of optimal invariant detectors for determining whether P multivariate processes have the same power spectral density. This problem finds application in multiple fields, including physical layer security and cognitive radio. For Gaussian observations, we prove that the optimal invariant detector, i.e., the uniformly most powerful invariant test, does not exist. Additionally, we consider the challenging case of close hypotheses, where we study the existence of the locally most powerful invariant test (LMPIT). The LMPIT is obtained in the closed form only for univariate signals. In the multivariate case, it is shown that the LMPIT does not exist. However, the c…
Automated facies identification by Direct Push-based sensing methods (CPT, HPT) and multivariate linear discriminant analysis to decipher geomorpholo…
2021
In ad 1362, a major storm surge drowned wide areas of cultivated medieval marshland along the north‐western coast of Germany and turned them into tidal flats. This study presents a new methodological approach for the reconstruction of changing coastal landscapes developed from a study site in the Wadden Sea of North Frisia. Initially, we deciphered long‐term as well as event‐related short‐term geomorphological changes, using a geoscientific standard approach of vibracoring, analyses of sedimentary, geochemical and microfaunal palaeoenvironmental parameters and radiocarbon dating. In a next step, Direct Push (DP)‐based Cone Penetration Testing (CPT) and the Hydraulic Profiling Tool (HPT) wer…
Statistical Multivariate Techniques for the Stock Location Assignment Problem
1998
In previous papers we proposed to apply multivariate statistical methodologies, like Multidimensional Scaling (MDS) and Seriation to the stock location assignment problem of a warehouse, often solved by considering the Cube per Order Index (COI). In this paper we compare the results by MDS, Seriation, a COI based method and the Maximum Path criterion, considering the data of a whole year of a Sicilian supermarket chain warehouse. The comparison is based on the simulated times to satisfy a sample of real orders.