Search results for "weighting"

Pollution models and inverse distance weighting: some critical remarks

2013

International audience; When evaluating the impact of pollution, measurements from remote stations are often weighted by the inverse of distance raised to some nonnegative power (IDW). This is derived from Shepard's method of spatial interpolation (1968). The paper discusses the arbitrary character of the exponent of distance and the problem of monitoring stations that are close to the reference point. From elementary laws of physics, it is determined which exponent of distance should be chosen (or its upper bound) depending on the form of pollution encountered, such as radiant pollution (including radioactivity and sound), air pollution (plumes, puffs, and motionless clouds by using the cl…

Context-dependent minimisation of prediction errors involves temporal-frontal activation

2020

According to the predictive coding model of perception, the brain constantly generates predictions of the upcoming sensory inputs. Perception is realised through a hierarchical generative model which aims at minimising the discrepancy between predictions and the incoming sensory inputs (i.e., prediction errors). Notably, prediction errors are weighted depending on precision of prior information. However, it remains unclear whether and how the brain monitors prior precision when minimising prediction errors in different contexts. The current study used magnetoencephalography (MEG) to address this question. We presented participants with repetition of two non-predicted probes embedded in cont…

Geological context and micromammal fauna characterisation from the karstic infilling of La Pedrera (Albaida, Valencia, E Spain)

2020

La Pedrera is a new palaeontological site located south of the province of Valencia, between the Betic and Iberian Ranges, in a cavity filled with sediments inside a tufa formation. Roughly 260 fossil remains, corresponding to 14 taxa, have been recovered and studied from Unit III. Six rodents (Microtus sp., M. sp. gr. M. (Terricola) duodecimcostatus-lusitanicus, Microtus sp. gr. M. brecciensis-cabrerae, Arvicola sapidus, Eliomys quercinus, and Apodemus sp. gr. sylvaticusfl avicollis), one lagomorph (Oryctolagus cf. cuniculus), three insectivores (Soricinae indet., Crocidura sp., and Talpa cf. europaea) and four bats (Myotis blythii, Rhinolophus cf. ferrumequinum, Myotis bechsteinii, and Rh…

Sampling Design and Weighting Strategies in the Second Wave of SHARE

2008

Market Timing with a Robust Moving Average

2015

In this paper we entertain a method of finding the most robust moving average weighting scheme to use for the purpose of timing the market. Robustness of a weighting scheme is defined its ability to generate sustainable performance under all possible market scenarios regardless of the size of the averaging window. The method is illustrated using the long-run historical data on the Standard and Poor's Composite stock price index. We find the most robust moving average weighting scheme, demonstrates its advantages, and discuss its practical implementation.

Automatically Modeling Linguistic Categories in Spanish

2010

This paper presents an approach to process Spanish linguistic categories automatically. The approach is based in a module of a prototype named WIH (Word Intelligent Handler), which is a project to develop a conversational bot. It basically learns category usage sequence in a sentence. It extracts a weighting metric to discriminate most common structures in real dialogs. Such a metric is important to define the preferred organization to be used by the robot to build an answer.

A Posteriori Methods

1998

A posteriori methods could also be called methods for generating Pareto optimal solutions. After the Pareto optimal set (or a part of it) has been generated, it is presented to the decision maker, who selects the most preferred among the alternatives. The inconveniences here are that the generation process is usually computationally expensive and sometimes in part, at least, difficult. On the other hand, it is hard for the decision maker to select from a large set of alternatives. One more important question is how to present or display the alternatives to the decision maker in an effective way. The working order in these methods is: 1) analyst, 2) decision maker.

Building Composite Indicators With Unweighted-TOPSIS

2023

Composite indicators have been widely used in a large number of fields, including innovation and entrepreneurship as a useful tool for conveying summary information about overall performance in a relatively simple way. The construction of composite indicators implies several stages concerning collection of data, selection of criteria and individual indicators, normalization and weighting of criteria and indicators, aggregation, and comparison of overall performance of the alternatives or options. This article aims at contributing to the construction of synthetic indicators by showing with a real example, how the proposed methodology can overcome the problem of the establisment of the decisi…

Sample design and weighting strategies in SHARE Wave 5

2015

This chapter provides a description of the sampling design and weighting strategies adopted in the fifth wave of SHARE. We begin by defining the target population that SHARE aims to represent. Next, we describe the sampling design focusing on the basic principles guiding the construction of the SHARE sample, the role played by sampling frames for coverage of the target population, and other important aspects of sampling - such as stratification, clustering and variation in selection probabilities - that affect the efficiency of sample-based inference. The chapter concludes with a description of the weighting strategies adopted by SHARE to handle problems of unit nonresponse in the baseline …

Basics of Moving Averages

2017

This chapter introduces the notion of a general weighted moving average and shows that each specific moving average can be uniquely characterized by either a price weighting function or a price-change weighting function. It also demonstrates how to quantitatively assess the average lag time and smoothness of a moving average. Finally, the analysis provided in this chapter reveals two important properties of moving averages when prices trend steadily.