Learn More
Composite indicators play an essential role for benchmarking higher education institutions. One of the main sources of uncertainty building composite indicators and, undoubtedly, the most debated problem in building composite indicators is the weighting schemes (assigning weights to the simple indicators or subindicators) together with the aggregation(More)
This paper presents two main results on partially observable (PO) stochastic systems. In the first one, we consider a general P O system on Bore1 spaces, with possibly unbounded cost-per-stage functions, and give conditions for the existence of a-discount optimal control policies (0 < a < 1). In the second result we specialize (*) to additive-noise systems(More)
Partial least squares regression (PLS) is a linear regression technique developed to relate many regressors to one or several response variables. Robust methods are introduced to reduce or remove the effect of outlying data points. In this paper we show that if the sample covariance matrix is properly robustified further robustification of the linear(More)
A finite horizon insurance model is studied where the risk/reserve process can be controlled by reinsurance and investment in the financial market. Our setting is innovative in the sense that we describe in a unified way the timing of the events, that is the arrivals of claims and the changes of the prices in the financial market, by means of a(More)
Recent studies have suggested that a causal link exists between the reputation of the institution and the subsequent demand indicators. However, it is unclear how these effects vary across institutional characteristics or whether these effects persist when considering other factors that affects demand outcomes. On the other hand, student demand studies have(More)
This paper deals with the optimal quadratic control problem for non-Gaussian discrete-time stochastic systems. Our main result gives explicit solutions for the optimal quadratic control problem for partially observable dynamic linear systems with asymmetric observation errors. For this purpose an asymmetric version of the Kalman filter based on asymmetric(More)
  • 1