Symbolic Factor Analysis

Bock, Hans‐Hermann; Chouakria, Ahlame Douzal; Cazes, Pierre; Diday, Edwin

doi:10.1007/978-3-642-57155-8_9

Cited by 10 publications

(12 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Alternatively, we have used interval-valued data, i.e. chronologically collected intervals of minimum and maximum values of a variable and are generally considered in the field of symbolic data analysis (Bock and Diday 2000). Interval valued data have the advantage of taking into account variability and/or uncertainty present in the values reducing the amount of uncertainty relative to that found in single-valued data (Neto andCarvalho 2010, Xiong et al 2014).…”

Section: Methodsmentioning

confidence: 99%

Disparity in the wages of agricultural labourers in India: An interval-valued data analysis

Yashavanth

Laha

2018

Indian J Agri Sci

View full text Add to dashboard Cite

This study explores the interval-valued data analysis techniques to witness the spatial disparity in the wage rates of farm labourers in India. Farm labourers constitute more than half of the total workforce engaged in Indian agriculture. Also, farmers' expenses towards labour charges account for more than 50 per cent of the total variable cost of production for most crops.Using the time series data on the nominal farm wage rates paid at different agriculturally important states, the interval-valued series are built. The inflation-adjusted real wage rates are found and both nominal and real wage rate data are used to find the average range of the farm wage rates over the agricultural years for a decade. Using the time series analysis techniques, viz. autoregressive integrated moving average-artificial neural network (ARIMA-ANN) hybrid model and vector autoregressive moving average (VARMA) model, the interval-valued data on nominal wage rates are modelled and the best model for forecasting is identified using forecast evaluation methods. The results established the presence of spatial disparity and the forecasts indicated that this disparity is not going to narrow down in future unless some policy intervention takes place.

show abstract

Section: Methodsmentioning

confidence: 99%

Disparity in the wages of agricultural labourers in India: An interval-valued data analysis

Yashavanth

Laha

2018

Indian J Agri Sci

View full text Add to dashboard Cite

show abstract

“…First, we consider the IVMA model in (1). Table 1 lists the REs of θ and σ for m = 250, n = 1000, 𝜎 = 2, and various values of 𝜃.…”

Section: Interval Time Seriesmentioning

confidence: 99%

“…Symbolic data analysis is a useful technique for characterizing interval‐valued observations. The history of interval data analysis is quite long and goes back to the work of Bock and Diday, 1 who first presented statistical methods for analyzing symbolic variables. Later, Billard and Diday 2 proposed the concept of symbolic data analysis, including basic descriptive statistics of interval‐valued variables.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive interval‐valued time series model with application to the S&P 500 index and PM2.5 level data analysis

Lin

Sung

Lee

2022

Appl Stoch Models Bus & Ind

View full text Add to dashboard Cite

In this study, we develop comprehensive symbolic interval-valued time-series models, including interval-valued moving average, auto-interval-regressive moving average, and heteroscedastic volatility models. These models can be flexibly combined to adapt more effectively to various situations. To make inferences regarding these models, likelihood functions were derived, and maximum likelihood estimators were obtained. To evaluate the performance of our methods empirically, Monte Carlo simulations and real data analyses were conducted using the S&P 500 index and PM2.5 levels of 15 stations in southern Taiwan. In the former case, it was found that the proposed model outperforms all other existing methods, whereas in the latter case, the residuals deduced from the proposed models provide more intuitively appealing results compared to the conventional vector autoregressive models. Overall, our findings strongly confirm the adequacy of the proposed model.

show abstract

“…To this end, in the present study, an unconventional cluster approach was considered. Currently, the concept of clustering has been extended to patterns described by unconventional data, often called symbolic data [3] or distribution‐valued data [28]. In this context, complex data can be described by intervals or distributions and structured as a set E of objects described by multi‐valued variables.…”

Section: Introductionmentioning

confidence: 99%

Share density‐based clustering of income data

Condino

2023

Statistical Analysis

View full text Add to dashboard Cite

The Lorenz curve is a fundamental tool for analyzing income and wealth distribution and inequality. Indeed, the Lorenz curve and its derivative, the so‐called share density, provide valuable information regarding inequality. There is a widely recognized connection between the Lorenz curve and elements from information theory field. Starting from this evidence, the aim of this work is to compare the income inequality of different subgroups, by using a proper dissimilarity measure, borrowed from information theory, between parametric share densities. This measure is then considered for clustering purposes. To this end, a dynamic clustering algorithm is considered to group unconventional data, such as density functions. Finally, an application, regarding data from Survey on Households Income and Wealth (SHIW) by Bank of Italy, is shown.

show abstract

Symbolic Factor Analysis

Cited by 10 publications

References 0 publications

Disparity in the wages of agricultural labourers in India: An interval-valued data analysis

Disparity in the wages of agricultural labourers in India: An interval-valued data analysis

Comprehensive interval‐valued time series model with application to the S&P 500 index and PM2.5 level data analysis

Share density‐based clustering of income data

Contact Info

Product

Resources

About