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End date: 2019 × Start date: 2010 × Subject: Structural change ×

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Now showing 1 - 3 of 3
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    Change-point detection in high-dimensional covariance structure
    (Ithaca, NY : Cornell University Library, 2018) Avanesov, Valeriy; Buzun, Nazar
    In this paper we introduce a novel approach for an important problem of break detection. Specifically, we are interested in detection of an abrupt change in the covariance structure of a high-dimensional random process – a problem, which has applications in many areas e.g., neuroimaging and finance. The developed approach is essentially a testing procedure involving a choice of a critical level. To that end a non-standard bootstrap scheme is proposed and theoretically justified under mild assumptions. Theoretical study features a result providing guaranties for break detection. All the theoretical results are established in a high-dimensional setting (dimensionality p≫n). Multiscale nature of the approach allows for a trade-off between sensitivity of break detection and localization. The approach can be naturally employed in an on-line setting. Simulation study demonstrates that the approach matches the nominal level of false alarm probability and exhibits high power, outperforming a recent approach.
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    Modeling of GPR Clutter Caused by Soil Heterogeneity
    (New York, NY : Hindawi, 2012) Takahashi, Kazunori; Igel, Jan; Preetz, Holger
    In small-scale measurements, ground-penetrating radar (GPR) often uses a higher frequency to detect a small object or structural changes in the ground. GPR becomes more sensitive to the natural heterogeneity of the soil when a higher frequency is used. Soil heterogeneity scatters electromagnetic waves, and the scattered waves are in part observed as unwanted reflections that are often referred to as clutter. Data containing a great amount of clutter are difficult to analyze and interpret because clutter disturbs reflections from objects of interest. Therefore, modeling GPR clutter is useful to assess the effectiveness of GPR measurements. In this paper, the development of such a technique is discussed. This modeling technique requires the permittivity distribution of soil (or its geostatistical properties) and gives a nominal value of clutter power. The paper demonstrates the technique with the comparison to the data from a GPR time-lapse measurement. The proposed technique is discussed in regard to its applicability and limitations based on the results.
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    Climate change, agriculture, and economic development in Ethiopia
    (Basel : MDPI AG, 2018) Yalew, A.W.; Hirte, G.; Lotze-Campen, H.; Tscharaktschiew, S.
    Quantifying the economic effects of climate change is a crucial step for planning adaptation in developing countries. This study assesses the economy-wide and regional effects of climate change-induced productivity and labor supply shocks in Ethiopian agriculture. We pursue a structural approach that blends biophysical and economic models. We consider different crop yield projections and add a regionalization to the country-wide CGE results. The study shows, in the worst case scenario, the effects on country-wide GDP may add up to -8%. The effects on regional value-added GDP are uneven and range from -10% to +2.5%. However, plausible cost-free exogenous structural change scenarios in labor skills and marketing margins may offset about 20-30% of these general equilibrium effects. As such, the ongoing structural transformation in the country may underpin the resilience of the economy to climate change. This can be regarded as a co-benefit of structural change in the country. Nevertheless, given the role of the sector in the current economic structure and the potency of the projected biophysical impacts, adaptation in agriculture is imperative. Otherwise, climate change may make rural livelihoods unpredictable and strain the country's economic progress.