Working towards a typology of indices of agreement for clustering evaluation, Margarida G. M. S. Cardoso

29 novembro 2018, 09:27 • Maria do Rosário De Oliveira Silva

Working towards a typology of indices of agreement for clustering evaluation

 

Margarida G. M. S. Cardoso (Instituto Universitário de Lisboa, ISCTE-IUL, Business Research Unit, BRU-IUL, Portugal)

 

December 13 (Thu.), 14:00 - Room P3.10 (Math. Building, IST, Av. Rovisco Pais, 1049-001 Lisboa)

 

Indices of agreement (IA) are commonly used to evaluate stability of a clustering solution or its agreement with ground truth – internal and external validation of the same solution, respectively.

IA provide different measures of the accordance between two partitions of the same data set, being based on contingency table data. Despite their frequent use in clustering evaluation, there are still open issues regarding the specific thresholds for each index to conclude about the degree of agreement between the partitions.

To acquire new insights on the indices behavior that may help improve clustering evaluation, 14 paired indices of indices are analyzed within diverse experimental scenarios - with balanced or unbalanced clusters and poorly, moderately or well separated ones. The paired indices’ observed values are all based on a cross-classification table of counts of pairs of observations both partitions agree to join and/or separate in the clusters. The IADJUST method is used to learn about the behavior of the indices under the hypothesis of agreement between partitions occurring by chance (H₀). It relies on the generation of contingency tables under H₀, being a simulation based procedure that enables to correct any index of agreement by deducting agreement by chance, overcoming previous limitations of analytical or approximate approaches – (Amorim and Cardoso, 2015).

The results suggest a preliminary typology of paired indices of agreement based on their distributional characteristics under H₀. Inter-scenarios symbolic data referring to location, dispersion and shape measures of IA distributions under H₀ are used to build this typology.

 

Reference: Amorim, M. J., & Cardoso, M. G. (2015). Comparing clustering solutions: The use of adjusted paired indices. Intelligent Data Analysis, 19(6), 1275-1296.

Joint work with Maria José Amorim (Department of Mathematics of ISEL, Lisbon, Portugal). 

https://math.tecnico.ulisboa.pt/seminars/pe/index?action=planned

LINK: Margarida G. M. S. Cardoso

https://ciencia.iscte-iul.pt/authors/margarida-g-m-s-cardoso/cv

SLIDES are available here

Feed-in Tariff Contract Schemes and Regulatory Uncertainty

26 novembro 2018, 13:55 • Maria do Rosário De Oliveira Silva

 hereCláudia Nunes (CEMAT & DM, IST, Portugal)

 

November 29 (Thu.), 14:00 - Room P3.10 (Math. Building, IST, Av. Rovisco Pais, 1049-001 Lisboa)

 

This paper presents a novel analysis of four finite feed-in tariff (FIT) schemes, namely fixed-price, fixed-premium, minimum price guarantee and sliding premium with a cap and a floor, under market and regulatory uncertainty. Using an analytical real options framework, we derive the project value, the optimal investment threshold and the value of the investment opportunity for the four FIT schemes. Regulatory uncertainty is modeled allowing the tariff to be reduced before the signature of the contract. While market uncertainty defers investment, a higher and more likely tariff reduction accelerates investment. We also present several findings that are aimed at policymaking decisions, regarding namely the choice, level and duration of the FIT. For instance, the investment threshold of the sliding premium with a cap and a floor is lower than the minimum price guarantee, which suggests that the first regime is a better policy than the latter because it accelerates the investment while avoiding overcompensation.

 

https://math.tecnico.ulisboa.pt/seminars/pe/index?action=planned

 

 

LINK: Claudia Nunes

http://cemat.ist.utl.pt/web/member.php?member_id=86

SLIDES are available here

Selecting differentially expressed genes in samples subgroups on microarray data, Carina Silva

11 novembro 2018, 22:37 • Maria do Rosário De Oliveira Silva

Selecting differentially expressed genes in samples subgroups on microarray data

 

Carina Silva (Escola Superior de Tecnologia da Saúde de Lisboa do Instituto Politécnico de Lisboa e CEAUL)

 

November 15 (Thr.), 14:00 - Room P3.10 (Math. Building, IST, Av. Rovisco Pais, 1049-001 Lisboa)

 

A common task in analysing microarray data is to determine which genes are differentially expressed under two (or more) kinds of tissue samples or samples submitted under different experimental conditions. It is well known that biological samples are heterogeneous due to factors such as molecular subtypes or genetic background, which are often unknown to the investigator. For instance, in experiments which involve molecular classification of tumours it is important to identify significant subtypes of cancer. Bimodal or multimodal distributions often reflect the presence of subsamples mixtures.

Consequently, truly differentially expressed genes on sample subgroups may be lost if usual statistical approaches are used. In this work it is proposed a graphical tool which identifies genes with up and down regulation, as well as genes with differential expression which revels hidden subclasses, that are usually missed if current statistical methods are used.

 

https://math.tecnico.ulisboa.pt/seminars/pe/index?action=planned

 

LINK: Carina Silva

http://www.ceaul.fc.ul.pt/mbr.html?membro=carina.silva@estesl.ipl.pt

SLIDES: available here

Optimal investment decision under switching regimes of subsidy support

29 outubro 2018, 14:52 • Maria do Rosário De Oliveira Silva

Optimal investment decision under switching regimes of subsidy support

Carlos Oliveira (GFMUL - Grupo de Física Matemática da Universidade de Lisboa)

 

November 08 (Thr.), 14:00 - Room P3.10 (Math. Building, IST, Av. Rovisco Pais, 1049-001 Lisboa)

 

We address the problem of making a managerial decision when the investment project is subsidized, which results in the resolution of an infinite-horizon optimal stopping problem of a switching diffusion driven by either a homogeneous or an inhomogeneous continuous-time Markov chain. We provide a characterization of the value function (and optimal strategy) of the optimal stopping problem. On the one hand, broadly, we can prove that the value function is the unique viscosity solution to a system of HJB equations. On the other hand, when the Markov chain is homogeneous and the switching diffusion is one-dimensional, we obtain stronger results: the value function is the difference between two convex functions.

Monitoring Non-Stationary Processes, Wolfgang Schmid

22 outubro 2018, 16:05 • Maria do Rosário De Oliveira Silva

Monitoring Non-Stationary Processes

 

Wolfgang Schmid (European University Viadrina, Department of Statistics, Germany)

 

October 29 (Mon.), 11:00 - Room P3.10 (Math. Building, IST, Av. Rovisco Pais, 1049-001 Lisboa)

 

In nearly all papers on statistical process control for time-dependent data it is assumed that the underlying process is stationary. However, in finance and economics we are often faced with situations where the process is close to non-stationarity or it is even non-stationary.

 

In this talk the target process is modeled by a multivariate state-space model which may be non-stationary. Our aim is to monitor its mean behavior. The likelihood ratio method, the sequential probability ratio test, and the Shiryaev-Roberts procedure are applied to derive control charts signaling a change from the supposed mean structure. These procedures depend on certain reference values which have to be chosen by the practitioner in advance. The corresponding generalized approaches are considered as well, and generalized control charts are determined for state-space processes. These schemes do not have further design parameters. In an extensive simulation study the behavior of the introduced schemes is compared with each other using various performance criteria as the average run length, the average delay, the probability of a successful detection, and the probability of a false detection.

  

Literature:

                Lazariv T. and Schmid W. (2018). Surveillance of non-stationary processes. AStA - Advances in Statistical Analysis, https://doi.org/10.1007/s10182-018-00330-4 .

                Lazariv T. and Schmid W. (2018). Challenges in monitoring non-stationary time series. In Frontiers in Statistical Process Control, Vol. 12, pp. 257-275. Berlin: Springer.

Joint work with Taras Lazariv (European University Viadrina, Department of Statistics, Germany)