Gzam, Maher; Mejdoub, Noureddine El; Jedoui, Younes
2016-02-01
The continental shelf of the Gulf of Gabes is outlined, during the MIS 5c and MIS 5a onshore highstands, by the genesis of forced regressive beach ridges situated respectively at -19 m b.s.l/100 ka and -8 m b.s.l/80 ka. This area, considered as a stable domain since at least the last 130 ka (Bouaziz et al. 2003), is a particular zone for the reconstruction of the late quaternary sea-level changes in the region. Shuttle Radar Topography Mission (SRTM) data and field observations are highlighted to deduce interaction between hydrodynamic factors and antecedent topography. Variations in geomorphology were attributed to geological inheritance. Petrography and sedimentary facies of the submerged coastal ridges reveal that the palaeocoastal morphology was more agitated than today and the fluvial discharges are consistent. Actual morphologic trend deduced from different environment coasts (sandy coasts, sea cliffs and tidal flat) is marked by accumulation of marine sands and progradation.
Dougherty, Amy J.
2014-09-01
Prograded barriers preserve palaeoenvironmental records within their varied morphologies and buried stratigraphy. In order to extract historical records of particular events, such as storms, the morphostratigraphy of these barriers must be detailed and the evolution deciphered. This study examines the progradation of Omaha barrier, New Zealand, using an integrated high-resolution geophysical and sedimentological approach. The barrier evolution appears complex, both spatially and temporally, with two different linear morphologies forming simultaneously alongshore, which both transition into a third type of ridge morphology across-shore. To determine what influenced the formation of these different morphologies, within the barrier and through time, various geological controls are investigated. The results are threefold: (1) a fall of sea level from a +2 m highstand drove barrier progradation, (2) differences in sediment supply driven by an exposure related longshore energy gradient dictated ridge morphology, and (3) storms punctuating barrier progradation formed the swales that define all morphologic ridges. High-energy events are recorded throughout the formation of Omaha barrier. Storm signatures are the most prominent features identified along the active beach and throughout the barrier morphostratigraphy. Observations of a high-energy event in 2007 document a unique depositional ridge emplaced landward of the characteristic erosional dune scarp and flattened beachface composed of course-grained/heavy mineral lag. A total of 25 paleo-beachfaces with the same post-storm geometry are identified within ground penetrating radar records of the barrier stratigraphy, including one associated with a known event in 1978 that has since been buried. Using limited ages available and the variable preservation of storm events in the morphostratigraphy, a speculative record of storm frequency and intensity is hypothesized. Future work aims to test this hypothesis by acquiring a
Surgical treatment of high-standing greater trochanter.
Takata, K; Maniwa, S; Ochi, M
1999-01-01
Eleven patients with high-standing greater trochanter (13 joints) aged 13-36 years underwent surgery. Distal transfer of the greater trochanter (group T) was performed in 4 patients (5 joints) and lateral displacement osteotomy (group L) in 7 (8 joints). The average follow-up duration was 13.4 years in group T and 5.9 years in group L. Clinical results were evaluated by the hip score according to Merle d'Aubigne. The mean hip score in group T was 13.4 points before operation and 15.4 points after operation, and in group L, 12.8 and 17.4 points, respectively. The postoperative clinical results of group L were significantly better than those of group T (P = 0.0494). In radiological evaluation, although the articulo-trochanteric distance (ATD) increased in both groups in group L it improved remarkably from 9.8 to 24.3, indicating a large descending distance of the greater trochanter. The lever arm ratio (LAR) did not change significantly in group T, but it decreased from 1.97 to 1.60 in group L (P = 0.004). This means that the lever arm of the abductors can certainly be extended by lateral displacement osteotomy. Lateral displacement osteotomy is the most effective procedure for high-standing greater trochanter.
Delta progradation in Greenland driven by increasing glacial mass loss
DEFF Research Database (Denmark)
Bendixen, Mette; Iversen, Lars Lonsmann; Bjork, Anders Anker
2017-01-01
Climate changes are pronounced in Arctic regions and increase the vulnerability of the Arctic coastal zone(1). For example, increases in melting of the Greenland Ice Sheet and reductions in sea ice and permafrost distribution are likely to alter coastal morphodynamics. The deltas of Greenland...... imagery. We find that delta progradation was driven by high freshwater runoff from the Greenland Ice Sheet coinciding with periods of open water. Progradation was controlled by the local initial environmental conditions (that is, accumulated air temperatures above 0 degrees C per year, freshwater runoff...... are largely unaffected by human activity, but increased freshwater runoff and sediment fluxes may increase the size of the deltas, whereas increased wave activity in ice-free periods could reduce their size, with the net impact being unclear until now. Here we show that southwestern Greenland deltas were...
The Oligo-Miocene of Eil (NE Somalia): a prograding coral- Lepidocyclina system
Bosellini, A.; Russo, A.; Arush, M. A.; Cabdulqadir, M. M.
The Oligo-Miocene succession of Eil is the product of a depositional regression and constitutes a 120-150 m thick depositional sequence that prograded seaward for at least 20-25 km. Its time-transgressive stratigraphy is documented physically by well exposed tangential clinoforms (previously considered as evidence of a tectonic coastal flexure) and biostratigraphically by the occurrence of calcareous nannoplankton, planktonic and benthonic foraminifera, and a rich coral fauna. The upper boundary of the sequence is indicated by a reefal toplap, which constitutes the flat surface of the Nogal Plateau. Age (Chattian to Burdigalian) and toplap relationships of the sequence indicate clearly that progradation took place after the Late Oligocene flooding which followed the strong fall of sea-level during the Chattian. Because of the horizontal geometry of the entire sedimentary system, it has been possible to make a clear environmental reconstruction and a facies model with original water depths. A worldwide Tertiary facies—the Lepidocyclina beds— was confined to the front of the reef, at depths ranging from 35-40 to 120-130 m.
Delta progradation in Greenland driven by increasing glacial mass loss
Bendixen, Mette; Lønsmann Iversen, Lars; Anker Bjørk, Anders; Elberling, Bo; Westergaard-Nielsen, Andreas; Overeem, Irina; Barnhart, Katy R.; Abbas Khan, Shfaqat; Box, Jason E.; Abermann, Jakob; Langley, Kirsty; Kroon, Aart
2017-10-01
Climate changes are pronounced in Arctic regions and increase the vulnerability of the Arctic coastal zone. For example, increases in melting of the Greenland Ice Sheet and reductions in sea ice and permafrost distribution are likely to alter coastal morphodynamics. The deltas of Greenland are largely unaffected by human activity, but increased freshwater runoff and sediment fluxes may increase the size of the deltas, whereas increased wave activity in ice-free periods could reduce their size, with the net impact being unclear until now. Here we show that southwestern Greenland deltas were largely stable from the 1940s to 1980s, but prograded (that is, sediment deposition extended the delta into the sea) in a warming Arctic from the 1980s to 2010s. Our results are based on the areal changes of 121 deltas since the 1940s, assessed using newly discovered aerial photographs and remotely sensed imagery. We find that delta progradation was driven by high freshwater runoff from the Greenland Ice Sheet coinciding with periods of open water. Progradation was controlled by the local initial environmental conditions (that is, accumulated air temperatures above 0 °C per year, freshwater runoff and sea ice in the 1980s) rather than by local changes in these conditions from the 1980s to 2010s at each delta. This is in contrast to a dominantly eroding trend of Arctic sedimentary coasts along the coastal plains of Alaska, Siberia and western Canada, and to the spatially variable patterns of erosion and accretion along the large deltas of the main rivers in the Arctic. Our results improve the understanding of Arctic coastal evolution in a changing climate, and reveal the impacts on coastal areas of increasing ice mass loss and the associated freshwater runoff and lengthening of open-water periods.
Retrograde versus Prograde Models of Accreting Black Holes
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David Garofalo
2013-01-01
Full Text Available There is a general consensus that magnetic fields, accretion disks, and rotating black holes are instrumental in the generation of the most powerful sources of energy in the known universe. Nonetheless, because magnetized accretion onto rotating black holes involves both the complications of nonlinear magnetohydrodynamics that currently cannot fully be treated numerically, and uncertainties about the origin of magnetic fields that at present are part of the input, the space of possible solutions remains less constrained. Consequently, the literature still bears witness to the proliferation of rather different black hole engine models. But the accumulated wealth of observational data is now sufficient to meaningfully distinguish between them. It is in this light that this critical paper compares the recent retrograde framework with standard “spin paradigm” prograde models.
Chlorine isotope behavior during prograde metamorphism of sedimentary rocks
Selverstone, Jane; Sharp, Zachary D.
2015-05-01
Chlorine stable isotope compositions of two sedimentary sequences and their metamorphic equivalents were measured in order to study fractionation effects during prograde metamorphism and devolatilization. Protoliths (n = 25) were collected from a 50 m section of Triassic fluvial and playa-lake strata and Jurassic (Liassic) marine black shales in a well-characterized quarry. Low greenschist to middle amphibolite facies equivalents (n > 80) were collected from the Glarus Alps, Urseren Zone, and Lucomagno region. Bulk δ37Cl values are constant within individual sedimentary layers, but vary from -2.0 to + 2.4 ‰ in Triassic rocks and from -3.0 to 0‰ in the black shales. Dolomitic and gypsiferous samples have positive δ37Cl values, but marls and shales are isotopically negative. Bulk Cl contents show only small declines during the earliest stages of metamorphism. Metamorphic equivalents of the Triassic and Liassic protoliths record the same overall ranges in δ37Cl as their protoliths. Samples with highly correlated bulk compositions but different metamorphic grade show no statistically significant difference in δ37Cl. These data lead to the following conclusions: (1) Terrestrial and marine sedimentary rocks display large primary heterogeneities in chlorine isotope composition. As a result, an unambiguous "sedimentary signature" does not exist in the chlorine stable isotope system. (2) No isotopic fractionation is discernable during metamorphic devolatilization, even at low temperatures. Alpine-style metamorphism thus has little to no effect on bulk chlorine isotopic compositions, despite significant devolatilization. (3) Cl is largely retained in the rocks during devolatilization, contrary to the normally assumed hydrophilic behavior of chlorine. Continuous release of mixed-volatile C-O-H fluids likely affected Cl partitioning between fluid and minerals and allowed chlorine to remain in the rocks. (4) There is no evidence for fluid communication across (meta
Cases Studies of Irrigated Soil Degradation and Progradation
Zeyliger, Anatoly; Kust, German; Rozov, Sergey; Stoma, Galina
2013-04-01
Waterlogging and salination, along with interaction with other degradation processes, have not only caused the collapse of irrigation-based societies in the past, but are indeed threatening the viability of irrigation at present. The problem is global in scope. Decimation of natural ecosystems, deterioration of soil productivity depletion and pollution of water resources, and conflicts over dwindling supplies have become international problems closely linked with extension of irrigation development to large scale and associated impact to soil fertility and surrounding environment. Practical experience and scientific research done in the frame of FP6 DESIRE project provided an affirmative answer to the question - can irrigated agriculture be sustained for long time. In present contribution two case studies will be discussed and analysed in scope to compare different irrigation practises used for about 35 years and their impact to soil fertility. Investigated areas of both case studies are situated in the same Saratov Region of Russia at the left bank of middle part of Volga River with distance between about 100 km. First case study was developed during 2009-2010 by field trials at irrigated and surrounded areas of agricultural farms situated at Privolghskaya Irrigation System (Marksovsky District). Second case study was developed during summer of 2011 by field trial at experimental farm of research institute called VolgNIIGiM (Enghelsky District). During fields trail soil maps of both case studies were developed and compared with soil maps of the same areas done at 1970th before irrigation projects at both areas were started. Results of soil map comparison are showing that in the territory of first case study considerable soil degradation is taken place, but in the territory of the second case study a substantial soil progradation is taken place. Thus is supported by the time series of ground water monitoring at both irrigated areas. Obtained results will be
A stalactite record of four relative sea-level highstands during the Middle Pleistocene Transition
Stocchi, Paolo; Antonioli, Fabrizio; Montagna, Paolo; Pepe, Fabrizio; Lo Presti, Valeria; Caruso, Antonio; Corradino, Marta; Dardanelli, Gino; Renda, Pietro; Frank, Norbert; Douville, Eric; Thil, François; de Boer, Bas; Ruggieri, Rosario; Sciortino, Rosanna; Pierre, Catherine
2017-10-01
Ice-sheet and sea-level fluctuations during the Early and Middle Pleistocene are as yet poorly understood. A stalactite from a karst cave in North West Sicily (Italy) provides the first evidence of four marine inundations that correspond to relative sea-level highstands at the time of the Middle Pleistocene Transition. The speleothem is located ∼97 m above mean sea level as result of Quaternary uplift. Its section reveals three marine hiatuses and a coral overgrowth that fixes the age of final marine ingression at 1.124 ± 0.2, thus making this speleothem the oldest stalactite with marine hiatuses ever studied to date. Scleractinian coral species witness light-limited conditions and water depth of 20-50 m. Integrating the coral-constrained depth with the geologically constrained uplift rate and an ensemble of RSL scenarios, we find that the age of the last marine ingression most likely coincides with Marine Isotope Stage 35 on the basis of a probabilistic assessment. Our findings are consistent with a significant Antarctic ice-sheet retreat.
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M. Reuter
2013-09-01
Full Text Available Climate change has an unknown impact on tropical cyclones and the Asian monsoon. Herein we present a sequence of fossil shell beds from the shallow-marine Maniyara Fort Formation (Kachcch Basin as a recorder of tropical cyclone activity along the NW Indian coast during the late Oligocene warming period (~ 27–24 Ma. Proxy data providing information about the atmospheric circulation dynamics over the Indian subcontinent at this time are important since it corresponds to a major climate reorganization in Asia that ends up with the establishment of the modern Asian monsoon system at the Oligocene–Miocene boundary. The vast shell concentrations are comprised of a mixture of parautochthonous and allochthonous assemblages indicating storm-generated sediment transport from deeper to shallow water during third-order sea level highstands. Three distinct skeletal assemblages were distinguished, each recording a relative storm wave base. (1 A shallow storm wave base is shown by nearshore molluscs, reef corals and Clypeaster echinoids; (2 an intermediate storm wave base depth is indicated by lepidocyclinid foraminifers, Eupatagus echinoids and corallinacean algae; and (3 a deep storm wave base is represented by an Amussiopecten bivalve-Schizaster echinoid assemblage. These wave base depth estimates were used for the reconstruction of long-term tropical storm intensity during the late Oligocene. The development and intensification of cyclones over the recent Arabian Sea is primarily limited by the atmospheric monsoon circulation and strength of the associated vertical wind shear. Therefore, since the topographic boundary conditions for the Indian monsoon already existed in the late Oligocene, the reconstructed long-term cyclone trends were interpreted to reflect monsoon variability during the initiation of the Asian monsoon system. Our results imply an active monsoon over the Eastern Tethys at ~ 26 Ma followed by a period of monsoon weakening during the
Reuter, M.; Piller, W. E.; Harzhauser, M.; Kroh, A.
2013-09-01
Climate change has an unknown impact on tropical cyclones and the Asian monsoon. Herein we present a sequence of fossil shell beds from the shallow-marine Maniyara Fort Formation (Kachcch Basin) as a recorder of tropical cyclone activity along the NW Indian coast during the late Oligocene warming period (~ 27-24 Ma). Proxy data providing information about the atmospheric circulation dynamics over the Indian subcontinent at this time are important since it corresponds to a major climate reorganization in Asia that ends up with the establishment of the modern Asian monsoon system at the Oligocene-Miocene boundary. The vast shell concentrations are comprised of a mixture of parautochthonous and allochthonous assemblages indicating storm-generated sediment transport from deeper to shallow water during third-order sea level highstands. Three distinct skeletal assemblages were distinguished, each recording a relative storm wave base. (1) A shallow storm wave base is shown by nearshore molluscs, reef corals and Clypeaster echinoids; (2) an intermediate storm wave base depth is indicated by lepidocyclinid foraminifers, Eupatagus echinoids and corallinacean algae; and (3) a deep storm wave base is represented by an Amussiopecten bivalve-Schizaster echinoid assemblage. These wave base depth estimates were used for the reconstruction of long-term tropical storm intensity during the late Oligocene. The development and intensification of cyclones over the recent Arabian Sea is primarily limited by the atmospheric monsoon circulation and strength of the associated vertical wind shear. Therefore, since the topographic boundary conditions for the Indian monsoon already existed in the late Oligocene, the reconstructed long-term cyclone trends were interpreted to reflect monsoon variability during the initiation of the Asian monsoon system. Our results imply an active monsoon over the Eastern Tethys at ~ 26 Ma followed by a period of monsoon weakening during the peak of the late
Fricke, A. T.; Nittrouer, C. A.; Ogston, A. S.; Vo-Luong, H. P.
2017-09-01
Mangrove forests are an important means of coastal protection along many shorelines in the tropics, and are often associated with large rivers there. Isolating the contribution of any one factor to the progradation or retreat of a coastal mangrove forest is often hindered by the physical separation between sites that are subject to vastly different combinations of marine and fluvial influence. The mangrove forest at the seaward end of Cù Lao Dung, an island in the Mekong Delta, includes areas with progradation rates of 10 s m y-1, and areas that have experienced little to no progradation in recent decades. The physical proximity (<12 km) of these two environments allows detailed hydrodynamic and sediment-dynamic measurements to be related directly to morphologic change and century-scale stratigraphy. Contrary to conventional understanding, the region of mangrove forest prograding most rapidly is subject to the greatest wave attack, while progradation is slowest in the most quiescent area. Limited progradation here is the product of a reduction in the supply of sediment to certain parts of the mangrove forest due to nearby estuarine dynamics operating on spring-neap timescales. Measurements of sediment flux show net transport into the rapidly prograding part of the forest, and transport out from the part of the forest with minimal progradation. Century-scale rates of sediment accumulation determined using 210Pb geochronology are consistent with in-situ dynamical measurements and geomorphic evolution of the mangrove forest. Where progradation is most rapid, sediment accumulation rates (3.0-5.1 cm y-1) exceed the rate of local sea-level rise (∼1.5 cm y-1). In contrast, sediment-accumulation rates in the area of minimal progradation (0.8-2.8 cm y-1) only somewhat exceed the rate of local sea-level rise, if at all. Physical stratification is well preserved in cores from areas of rapid progradation, consistent with energetic transport processes and an ample sediment
Piliouras, Anastasia; Kim, Wonsuck; Carlson, Brandee
2017-10-01
Vegetation is an important component of constructional landscapes, as plants enhance deposition and provide organic sediment that can increase aggradation rates to combat land loss. We conducted two sets of laboratory experiments using alfalfa (Medicago sativa) to determine the effects of plants on channel organization and large-scale delta dynamics. In the first set, we found that rapid vegetation colonization enhanced deposition but inhibited channelization via increased form drag that reduced the shear stress available for sediment entrainment and transport. A second set of experiments used discharge fluctuations between flood and base flow (or interflood). Interfloods were critical for reworking the topset via channel incision and lateral migration to create channel relief and prevent rapid plant colonization. These low-flow periods also greatly reduced the topset slope in the absence of vegetation by removing topset sediment and delivering it to the shoreline. Floods decreased relief by filling channels with sediment, resulting in periods of rapid progradation and enhanced aggradation over the topset surface, which was amplified by vegetation. The combination of discharge fluctuations and vegetation thus provided a balance of vertical aggradation and lateral progradation. We conclude that plants can inhibit channelization in depositional systems and that discharge fluctuations encourage channel network organization to naturally balance against aggradation. Thus, variations in discharge are an important aspect of understanding the ecomorphodynamics of aggrading surfaces and modeling vegetated deltaic systems, and the combined influences of plants and discharge variations can act to balance vertical and lateral delta growth.
Quantitative Prograde P-T Paths From Inclusion Assemblages in Eclogitic Garnets
Essene, E. J.; Page, F. Z.; Mukasa, S. B.
2003-12-01
Many workers have used garnet inclusions as qualitative indicators of the early history of tectonically exposed eclogites. Assemblages indicative of crustal facies combined with standard P-T conditions of those facies and thermobarometry on the matrix eclogite constrain the prograde path. However, now that conditions of some blueschist, amphibolite and granulite facies rocks have been extended to much higher pressures, in the range of 10-20 kbar, those assignments are in need of review. While undertaking studies on eclogites from the Blue Ridge of North Carolina and the Franciscan in northern California, the authors have identified key inclusion assemblages of sphene-rutile-epidote-quartz and phengite-omphacite together with garnet that constrain the prograde P-T path based on univariant assemblages corrected for observed solid solutions. Equilibria that have proved most useful are those bounding epidote stability and two key reactions, one involving sphene/rutile: (1) clinozoisite + sphene = grossular + rutile + H2O, and the second being the phengite barometer: garnet + Mg-celadonite = clinopyroxene + muscovite. Reactions (1) and (2) have negative slopes, intersecting with the Mg/Fe KD garnet-clinopyroxene thermometer and providing a reasonable estimate of pre- to syn-eclogite facies P-T. In the case of the Bakersville eclogite samples from North Carolina, the inclusion assemblage yields 10 +/- 2 kbar and 500 +/- 50° C with reaction (1) compared to the peak assemblage at P > 15 +/- 2 kbar and 700 +/- 50° C. These data combined with evidence for a granulite facies overprint indicate a clockwise P-T path for those eclogites. A similar study on the Healdsburg eclogite samples from California yields about 500° C and 12 +/- 1 kbar for the garnet cores, 14 +/- 2 kbar for mantles and 16 +/- 2 kbar for rim-matrix assemblages. The introduction of late glaucophane, epidote and chlorite partially replacing omphacite and garnet implies a retrograde return to the blueschist
Oliver, T. S. N.; Tamura, T.; Hudson, J. P.; Woodroffe, C. D.
2017-07-01
Prograded barriers are distinctive coastal landforms preserving the position of past shorelines as low relief, shore-parallel ridges composed of beach sediments and commonly adorned with variable amounts of dune sand. Prograded barriers have been valued as coastal archives which contain palaeoenvironmental information, however integrating the millennial timescale geological history of barriers with observed inter-decadal modern beach processes has proved difficult. Technologies such as airborne LiDAR, ground penetrating radar (GPR) and optically stimulated luminescence dating (OSL) were utilised at Boydtown and Wonboyn, in southeastern Australia, and combined with previously reported radiocarbon dates and offshore seismic and sedimentological data to reconstruct the morpho-sedimentary history of prograded barrier systems. These technologies enabled reconstruction of geological timescale processes integrated with an inter-decadal model of ridge formation explaining the GPR-imaged subsurface character of the barriers. Both the Boydtown and Wonboyn barriers began prograding 7500-8000 years ago when sea level attained at or near present height along this coastline and continued prograding until the present-day with an initially slower rate of shoreline advancement. Sources of sediment for progradation appear to be the inner shelf and shoreface with a large shelf sand body likely contributing to progradation at Wonboyn. The Towamba River seems to have delivered sediment to Twofold Bay during flood events after transitioning to a mature estuarine system sometime after 4000 cal. yr BP. Some of this material appears to have been reworked onto the Boydtown barrier, increasing the rate of progradation in the seaward 50% of the barrier deposited over the past 1500 years. The GPR imaged beachfaces are shown to have similar geometry to beach profiles following recent storm events and a model of ridge formation involving cut and fill of the beachface, and dune building in the
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ZSOLT RÓBERT NAGY
2014-07-01
Full Text Available A shallowing-upward carbonate sequence was studied from the outcrop at Gyulakeszi, Tapolca Basin (western Hungary, and it is interpreted as a Middle Triassic (Curionii or younger platform progradation. Two lithostratigraphic units are distinguished. Microfacies analysis and micropaleontological investigation conducted on the red nodular, cherty limestone (Vászoly and Buchenstein formations suggest that the lower unit was deposited during the Reitzi and the Secedensis ammonoid zones. The overlying white platform limestone (upper unit is typical of a prograding platform and includes gravity-driven deposits at the base followed by periplatform facies deposited in shallow marine warm waters around the fair-weather wave base. The section at Gyulakeszi was unaffected by fabric-destructive dolomitization, which is uncharacteristic of similar platform facies in the Balaton Highland. Isopachous and radiaxial fibrous calcite cement found in the grainstone and boundstone facies are indicative of early lithification and diagenesis in the marine phreatic zone. “Evinospongiae”-type cement is described for the first time from the Balaton Highland and it is similar to the outer platform cements published previously from the Alps (Italy and Austria. The progradation could have advanced over the pelagic limestones as early as the Curionii zone, which is an undocumented event in the Veszprém Plateau. Similar event, however, is well known from the Western Dolomites, where aggradation was followed by intense progradation during the Gredleri and Archelaus ammonoid zones. The length of this progradation event at Gyulakeszi, however, is ambiguous since proven Ladinian (Longobardian rocks are not exposed in the study area and were not penetrated by boreholes in the Tapolca Basin.
Li, Daohai; Christou, Apostolos A.
2017-09-01
In extending the analysis of the four secular resonances between close orbits in Li and Christou (Celest Mech Dyn Astron 125:133-160, 2016) (Paper I), we generalise the semianalytical model so that it applies to both prograde and retrograde orbits with a one-to-one map between the resonances in the two regimes. We propose the general form of the critical angle to be a linear combination of apsidal and nodal differences between the two orbits b_1 Δ π + b_2 Δ Ω, forming a collection of secular resonances in which the ones studied in Paper I are among the strongest. Test of the model in the orbital vicinity of massive satellites with physical and orbital parameters similar to those of the irregular satellites Himalia at Jupiter and Phoebe at Saturn shows that {>}20 and {>}40% of phase space is affected by these resonances, respectively. The survivability of the resonances is confirmed using numerical integration of the full Newtonian equations of motion. We observe that the lowest order resonances with b_1+|b_2|≤ 3 persist, while even higher-order resonances, up to b_1+|b_2|≥ 7, survive. Depending on the mass, between 10 and 60% of the integrated test particles are captured in these secular resonances, in agreement with the phase space analysis in the semianalytical model.
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Visser, J.N.J. (Orange Free State University, Bloemfontein (South Africa). Dept. of Geology)
1992-12-01
The black, laminated, carbonaceous shales of the Whitehill Formation were deposited in a very young, underfilled foreland basin under anoxic bottom conditions. A sea-level highstand, basin tectonics, and climate were the controlling factors - interplay of which resulted in bounding conditions for organic-rich mud deposition during a specific time slot in the history of the basin. Coal-forming environments along the steep palaeo-eastern basin margin were the source of mud and organic matter transported as fresh-water plumes in an offshore direction during episodic flooding and erosion of the organic-rich deposits. Air-borne volcanic ash deposited together with the muds as well as in discrete layers was derived from a tectonic arc in the palaeo-west. The high concentration of organic matter in the water body and the restricted oceanic circulation in the morphologically complex basin created anoxia in the water column. Preservation of organic matter in the absence of benthonic fauna was high. Less anoxic conditions prevailed in the shallow marginal regions where deposition of siltstone and carbonate rocks interbedded with the black shales took place. Continuous inflow of fresh-water plumes in the restricted basin progressively caused brackish conditions suitable for the proliferation of aquatic fauna. 67 refs., 10 figs., 1 tab.
Oliver, Thomas; Tamura, Toru; Short, Andrew; Woodroffe, Colin
2017-04-01
Prograded coastal barriers are accumulations of marine and aeolian sands configured into shore-parallel ridges. A variety of ridge morphologies described around the world reflect differences in origin as a consequence of differing prevailing coastal morphodynamics. The 'morphodynamic approach' described by Wright and Thom (1977) expounds the coastal environmental conditions, hydrodynamic and morphodynamic processes and inheritance of evolutionary sequences over varying temporal scales which interdependently operate to produce an assemblage of coastal landforms adjusted, or adjusting to, a dynamic equilibrium. At Pedro Beach on the southeastern coast of Australia a large sandy deposit of foredune ridges provides an opportunity to explore the morphodynamic paradigm as it applies to coastal barrier systems using optically stimulated luminescence (OSL) dating, ground penetrating radar (GPR) and airborne LiDAR topography. The prograded barrier at Pedro Beach has formed following the stabilisation of the sea level at its present height on the southeast Australian coastline. A series of dune-capped ridges, increasing in height seawards, formed from 6000 years ago to 4000 years ago. During this time the shoreline straightened as bedrock accommodation space for Holocene sediments diminished. Calculation of Holocene sediment volumes utilising airborne LiDAR topography shows a decline in sediment volume over this time period coupled with a decrease in shoreline progradation rate from 0.75 m/yr to 0.49 m/yr. The average ridge 'lifetime' during this period increases resulting in higher ridges as dune-forming processes have longer to operate. Greater exposure to wave and wind energy also appears to have resulted in higher ridges as the sheltering effect of marginal headlands has diminished. A high outer foredune has formed through vertical accretion in the past 700 years, evidenced by GPR subsurface structures and upward younging of OSL ages, with a sample from 1 m deep within
Micallef, Aaron; Ribó, Marta; Canals, Miquel; Puig, Pere; Lastras, Galderic; Tubau, Xavier
2013-04-01
40% of submarine canyons worldwide are located in passive margins, where they constitute preferential conduits of sediment and biodiversity hotspots. Recent studies have presented evidence that submarine canyons incising passive, progradational margins can co-evolve with the adjacent continental slope during long-term margin construction. The stages of submarine canyon initiation and their development into a mature canyon-channel system are still poorly constrained, however, which is problematic when attempting to reconstruct the development of passive continental margins. In this study we analyse multibeam echosounder and seismic reflection data from the southern Ebro margin (western Mediterranean Sea) to document the stages through which a first-order gully develops into a mature, shelf-breaching canyon and, finally, into a canyon-channel system. This morphological evolution allows the application of a space-for-time substitution approach. Initial gully growth on the continental slope takes place via incision and downslope elongation, with limited upslope head retreat. Gravity flows are the main driver of canyon evolution, whereas slope failures are the main agent of erosion; they control the extent of valley widening, promote tributary development, and their influence becomes more significant with time. Breaching of the continental shelf by a canyon results in higher water/sediment loads that enhance canyon development, particularly in the upper reaches. Connection of the canyon head with a paleo-river changes evolution dynamics significantly, promoting development of a channel and formation of depositional landforms. Morphometric analyses demonstrate that canyons develop into geometrically self-similar systems that approach steady-state and higher drainage efficiency. Canyon activity in the southern Ebro margin is pulsating and enhanced during sea level lowstands. Rapid sedimentation by extension of the palaeo-Millars River into the outermost shelf and upper
Kahane, Leo H
2007-01-01
Using a friendly, nontechnical approach, the Second Edition of Regression Basics introduces readers to the fundamentals of regression. Accessible to anyone with an introductory statistics background, this book builds from a simple two-variable model to a model of greater complexity. Author Leo H. Kahane weaves four engaging examples throughout the text to illustrate not only the techniques of regression but also how this empirical tool can be applied in creative ways to consider a broad array of topics. New to the Second Edition Offers greater coverage of simple panel-data estimation:
Olive, David J
2017-01-01
This text covers both multiple linear regression and some experimental design models. The text uses the response plot to visualize the model and to detect outliers, does not assume that the error distribution has a known parametric distribution, develops prediction intervals that work when the error distribution is unknown, suggests bootstrap hypothesis tests that may be useful for inference after variable selection, and develops prediction regions and large sample theory for the multivariate linear regression model that has m response variables. A relationship between multivariate prediction regions and confidence regions provides a simple way to bootstrap confidence regions. These confidence regions often provide a practical method for testing hypotheses. There is also a chapter on generalized linear models and generalized additive models. There are many R functions to produce response and residual plots, to simulate prediction intervals and hypothesis tests, to detect outliers, and to choose response trans...
Directory of Open Access Journals (Sweden)
Igor K. Kochanenko
2013-01-01
Full Text Available Procedures of construction of curve regress by criterion of the least fractals, i.e. the greatest probability of the sums of degrees of the least deviations measured intensity from their modelling values are proved. The exponent is defined as fractal dimension of a time number. The difference of results of a well-founded method and a method of the least squares is quantitatively estimated.
Hearty, Paul; Raymo, Maureen; Sandstrom, Michael; Rovere, Alessio; O'Leary, Michael
2016-04-01
surprising result from a west coast marine terrace at Donkergat was the identification of an early Pleistocene highstand (MIS 31 or 37?) at 16.5 ± 0.5 m asl with a mean Sr age of 1.26 ± 0.15 Ma. These shorelines suggest that a considerable volume of polar ice sheets could have been susceptible to melting when atmospheric CO2 and global temperatures were only modestly higher than the present but significant uncertainty in elevations due to glacial isostatic adjustment and dynamic topography (e.g. Rovere et al., 2014) precludes a robust assessment of eustatic sea level in the Pliocene and early Pleistocene. Fedorov, A.V., Brierley, C.M., Lawrence, K.T., Liu, Z., Dekens, P.S., Ravelo, A.C., 2013. Patterns and mechanisms of early Pliocene warmth. Nature 496, 43-49. Rovere, A., Raymo, M.E., Mitrovica, J.X., Hearty, P.J., O'Leary, M.J., Inglis, J.D., 2013. The Mid-Pliocene sea-level conundrum: Glacial isostasy, eustasy and dynamic topography. Earth and Planetary Science Letters 387 (2014) 27-33, doi.org/10.1016/j.epsl.2013.10.030.
Okamoto, Atsushi; Shimizu, Hiroyuki; Fukuda, Jun-ichi; Muto, Jun; Okudaira, Takamoto
2017-09-01
Devolatilization reactions during prograde metamorphism are a key control on the fluid distribution within subduction zones. Garnets in Mn-rich quartz schist within the Sanbagawa metamorphic belt of Japan are characterized by skeletal structures containing abundant quartz inclusions. Each quartz inclusion was angular-shaped, and showed random crystallographic orientations, suggesting that these quartz inclusions were trapped via grain boundary cracking during garnet growth. Such skeletal garnet within the quartz schist formed related to decarbonation reactions with a positive total volume change (Δ V t > 0), whereas the euhedral garnet within the pelitic schists formed as a result of dehydration reaction with negative Δ V t values. Coupled hydrological-chemical-mechanical processes during metamorphic devolatilization reactions were investigated by a distinct element method (DEM) numerical simulation on a foliated rock that contained reactive minerals and non-reactive matrix minerals. Negative Δ V t reactions cause a decrease in fluid pressure and do not produce fractures within the matrix. In contrast, a fluid pressure increase by positive Δ V t reactions results in hydrofracturing of the matrix. This fracturing preferentially occurs along grain boundaries and causes episodic fluid pulses associated with the development of the fracture network. The precipitation of garnet within grain boundary fractures could explain the formation of the skeletal garnet. Our DEM model also suggests a strong influence of reaction-induced fracturing on anisotropic fluid flow, meaning that dominant fluid flow directions could easily change in response to changes in stress configuration and the magnitude of differential stress during prograde metamorphism within a subduction zone.
Rebay, G.; Messiga, B.
2007-10-01
In the coronitic metagabbroic rocks of the Corio and Monastero metagabbro bodies in the continental Sesia-Lanzo zone of the western Italian Alps, a variety of mineral reactions that testify to prograde conditions from greenschist to eclogite-facies can be recognised. A microstructural and microchemical study of a series of samples characterized by coronitic textures and pseudomorphic replacement of the original igneous minerals has allowed the prograde reactions undergone by the rocks to be established. In completely eclogitized coronitic samples, paragonite, blue amphibole, garnet, epidote, fine grained jadeite and chloritoid occur in plagioclase microdomains (former igneous plagioclase). The mafic mineral microdomains consist of glaucophane and garnet. Complexly-zoned amphiboles constrain changing metamorphic conditions: cores of pre-Alpine brown hornblende and/or tremolite are preserved inside rims of a sodic-calcic amphibole that are in turn surrounded by a sodic amphibole. The main high-pressure mineral assemblage, as seen in mylonites, involves glaucophane, chloritoid, epidote, garnet ± phengite, ± paragonite. Some layers within the gabbro contain garnet, omphacite, ± glaucophane, and acid dykes crosscutting the gabbro body contain jadeite, quartz, garnet, epidote and paragonite. The presence of chloritoid-bearing high-pressure assemblages reflects hydration of the gabbros during their pre-Alpine exhumation prior to subduction, as well as the composition of the microdomains operating during subduction. The pressure and temperature conditions of gabbro transformation during subduction are inferred to be 450-550 °C at up to 2 GPa on the basis of the chloritoid-bearing assemblages. The factors controlling the reaction pathway to form chloritoid-bearing high-pressure assemblages in mafic rocks are inferred from these observations.
Pratolongo, Paula; Piovan, María Julia; Cuadrado, Diana G.; Gómez, Eduardo A.
2017-08-01
Sedimentary descriptions and radiocarbon ages from two cores obtained from coastal plains along the western margin of the Bahía Blanca Estuary (Argentina) were integrated with previous information on landscape patterns and plant associations to infer landscape evolution during the mid-to-late Holocene. The study area comprises at least two marine terraces of different elevations. The old marine plain (OMP), at an average elevation of 5 m above mean tidal level (MTL), is a nearly continuous flat surface. The Recent marine plain (RMP), 2 to 3 m above MTL, is a mosaic of topographic highs and elongated depressions that may correspond to former tidal channels. Mollusks at the base of the OMP core (site elevation 5.09 m above MTL), with ages between 5,660 ± 30 and 5,470 ± 30 years BP, indicate a subtidal setting near the inland limits of the marine ingression. The sandy bottom of the core is interpreted as the last stage of the transgressive phase, followed by a tight sequence of dark laminated muds topped by a thick layer of massive gray muds. The RMP core (site elevation 1.80 m above MTL) has a similar sedimentary sequence, but unconformities appear at lower elevations and the massive mud deposits are less developed. The thickness of the grayish mud layer is a major difference between the OMP and RMP cores, but deeper layers have similar ages, suggesting a common origin at the end of the transgressive phase. The overlying massive muds would correspond to rapid sedimentation during a high sea-level stillstand or slow regression. It is proposed that, after a rapid sea-level drop to about 3 m above MTL, a flat and continuous surface corresponding to the OMP emerged, and more recent coastal dynamics shaped the dissected landscape of the RMP. For the Bahía Blanca Estuary, smooth regressive trends have been proposed after the mid-Holocene highstand, but also stepped curves. A stillstand or slowly dropping sea level was described around 3,850 ± 100 years BP, as well as
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Baur, N.; Liew, T.C.; Todt, W.; Hofmann, A.W. (Max-Planck-Institut fuer Chemie, Mainz (West Germany)); Kroener, A. (Univ. Mainz (West Germany)); Williams, I.S. (Australian National Univ., Canberra (Australia))
1991-07-01
The authors present U-Pb zircon isotopic data from locally restricted prograde (arrested in situ charnockitization) and retrograde metamorphic transition zones, which are well exposed in Proterozoic orthogneisses tectonically interbanded with granulite facies supracrustal rocks of the Highland Group in Sri Lanka. These granitoid rocks yield apparent ages of 1942 {plus minus} 22 Ma, {approximately} 770 Ma, {approximately} 660 Ma, and {approximately} 560 Ma. All samples show severe Pb-loss some 550-560 Ma ago. The main phase of granulite-formation could not be dated unambiguously but is bracketed between {approximately} 660 Ma and {approximately} 550 Ma. The pervasive Pb-loss event around 550-560 Ma reflects the end of this period of high-grade metamorphism and was associated with widespread igneous activity and retrogression. This is constrained by the 550 {plus minus} 3 Ma intrusion age for a post-tectonic granite. They relate this late phase of thermal activity to crustal uplift of the Sri Lankan granulites. This data unambiguously prove the high-grade history of the Sri Lanka gneisses to be a late Precambrian event that may be related to the Pan-African evolution along the eastern part of Africa.
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Fryberger, S.G.; Al-Sari, A.M.; Clisham, T.J.
1983-02-01
An offshore prograding sand sea exists along portions of the Arabian Gulf coastline near Dhahran, Saudi Arabia. In this region, sediments of eolian dune, interdune, sand sheet, and siliciclastic sabkha intercalate with marine deposits. This depositional setting is characterized by strong offshore winds which supply abundant sand to the coastline, and cause at present time the outbuilding of the dune system. This quartz-detrital dominant setting contrasts markedly with the carbonate dominant setting resulting from onshore winds in the Trucial Coast area to the south. The broad intercalation of eolian and marine deposits which results creates ideal potential for subregional stratigraphic petroleum traps, due to pinch-out of porous and permeable dune sands into impermeable marine mudstones. Within the eolian system itself are potential reservoir rocks, sources, (organic-rich sabkha and interdune deposits), and seals (zones of early cementation in all deposits). Early cementation is very common in all facies of the eolian sand sea. The early cementation occurs owing to (1) soil formation, (2) deposition of pore-filling gypsiferous cements from saturated solutions near water table, and (3) addition of sand-size windblown evaporitic material to sands downwind of sabkhas.
Energy Technology Data Exchange (ETDEWEB)
Fryberger, S.G.; Al-Sari, A.M.; Clisham, T.J.
1983-02-01
An offshore prograding sand sea exists along portions of the Arabian Gulf coastline near Dhahran, Saudi Arabia. In this region, sediments of eolian dune, interdune, sand sheet, and siliciclastic sabkha intercalate with marine deposits. This depositional setting is characterized by strong offshore winds which supply abundant sand to the coastline, and cause at present time the outbuilding of the dune system. This quartz-detrital dominant setting contrasts markedly with the carbonate dominant setting resulting from onshore winds in the Trucial Coast area to the south. The broad intercalation of eolian and marine deposits which results creates ideal potential for subregional stratigraphic petroleum traps, due to pinch-out of porous and permeable dune sands into impermeable marine mudstones. Within the eolian system itself are potential reservoir rocks (dunes), sources (organic-rich sabkha and interdune deposits), and seals (zones of early cementation in all deposits). Early cementation is very common in all facies of the eolian sand sea. The early cementation occurs owing to (1) soil formation, (2) deposition of pore-filling gypsiferous cements from saturated solutions near water table, and (3) addition of sand-size windblown evaporitic material to sands downwind of sabkhas.
Meltzner, A. J.; Rockwell, T. K.; Verdugo, D. M.
2003-12-01
The Imperial fault (IF) is the only fault in southern California to have ruptured in two major earthquakes in the 20th century. In 1940, it ruptured end-to-end (both north and south of the international border) in an M 6.9 earthquake, and in 1979, the northern segment of the fault (north of the border) ruptured again in an M 6.4 event. Slip in 1940 was highest (5-6 m) along the central portion of the fault and lowest (<1 m) along the northern portion, with a high slip gradient between these two segments just north of the border. The 1979 earthquake involved surface rupture along only the northern 30 km of the fault, with dextral offsets being <1 m and being nearly identical to 1940 offsets along the northern 20 km of the rupture. The similarities and differences of the two events led Sieh (1996) to propose a "slip-patch model" for the Imperial fault, whereby the fault ruptures with frequent moderate earthquakes along its northern end, like in 1979, and with less frequent larger events like 1940 along its entire length. According to the model, the central patch, which experienced high slip in 1940 and did not rupture in 1979, would rupture with relatively infrequent events (roughly every 260 years) with typically 5-6 m of slip per event; meanwhile, the northern patch, which corresponds to the 1979 rupture, would rupture more frequently (roughly every 40 years) with up to 1 m of slip per event. This model is consistent with the slip distribution observed in 1940 and in 1979. Paleoseismic investigations along the central patch also support this model, as the penultimate event there occurred shortly after the last Lake Cahuilla (LC) highstand at around A.D. 1680 (Thomas and Rockwell, 1996). Prior to the present investigation, however, there were no data on events prior to 1940 on the northern patch, which could serve to either support or refute the slip-patch model. We have opened a trench across the IF south of Harris Road, adjacent to Mesquite Basin, where the fault
Cyclic transgressive and regressive sequences, Paleocene Suite, Sirte basin, Libya
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Abushagur, S.A.
1986-05-01
The Farrud lithofacies represent the main reservoir rock of the Ghani oil field and Western Concession Eleven of the Sirte basin, Libya. Eight microfacies are recognized in the Farrud lithofacies in the Ghani field area: (1) bryozoan-bioclastic (shallow, warm, normal marine shelf deposits); (2) micrite (suggesting quiet, low-energy conditions such as may have existed in a well-protected lagoon); (3) dasycladacean (very shallow, normal marine environment); (4) bioclastic (very shallow, normal marine environment with moderate to vigorous energy); (5) mgal (very shallow, normal marine environment in a shelf lagoon); (6) pelletal-skeletal (deposition within slightly agitated waters of a sheltered lagoon with restricted circulation); (7) dolomicrite (fenestrate structures indicating a high intertidal environment of deposition); and (8) anhydrite (supratidal environment). The Paleocene suite of the Farrud lithofacies generally shows a prograding, regressive sequence of three facies: (1) supratidal facies, characterized by nonfossiliferous anhydrite, dolomite, and dolomitic pelletal carbonate mudstone; (2) intertidal to very shallow subtidal facies, characterized by fossiliferous, pelletal, carbonate mudstone and skeletal calcarenite; and (3) subtidal facies, characterized by a skeletal, pelletal, carbonate mudstone. Source rocks were primarily organic-rich shales overlying the Farrud reservoir rock. Porosity and permeability were developed in part by such processes as dolomitization, leaching, and fracturing in the two progradational, regressive carbonate facies. Hydrocarbons were trapped by a supratidal, anhydrite cap rock.
Olson, Storrs L.; Hearty, Paul J.
2009-02-01
A small, protected karstic feature exposed in a limestone quarry in Bermuda preserved abundant sedimentary and biogenic materials documenting a transgressive phase, still-stand, and regressive phase of a sea-level in excess of 21.3 m above present during Marine Isotope Stage (MIS) 11 (400 ka) as determined by U/Th dating and amino acid racemization. Cobbles and marine sediments deposited during the high-energy transgressive phase exhibit rim cements indicating a subsequent phreatic environment. This was succeeded stratigraphically by a still-stand deposition of fine calcareous lagoonal sediments containing bioclasts of red algae and benthic and planktonic foraminifera that was intensely burrowed by marine invertebrates, probably upogebiid shrimp, that could not be produced under any condition other than sustained marine submergence. Overlying this were pure carbonate beach sands of a low-energy regressive phase containing abundant remains of terrestrial and marine vertebrates and invertebrates. The considerable diversity of this fauna along with taphonomic evidence from seabird remains indicates deposition by high run-up waves over a minimum duration of months, if not years. The maximum duration has yet to be determined but probably did not exceed one or two thousand years. The most abundant snails in this fauna are two species indicative of brackish water and high-tide line showing that a Ghyben-Herzberg lens must have existed at > + 20 m. The nature of these sediments and fossil accumulation is incompatible with tsunami deposition and, given the absence of evidence for tectonic uplift of the Bermuda pedestal or platform, provide proof that sea-level during MIS 11 exceeded +20 m, a fact that has widespread ramifications for geologists, biogeographers, and human demographics along the world's coastlines.
Wojtulek, Piotr; Puziewicz, Jacek; Ntaflos, Theodoros
2016-04-01
The Central-Sudetic Ophiolite (CSO) consists of Ślęża (SM), Braszowice-Brzeźnica (BBM), Szklary (SZM) and Nowa Ruda massifs. Ultramafic rocks occurring in ŚM, BBM and SM have MgO/SiO2 (0.82-1.20) and Al2O3/SiO2 (~0.01) ratios typical for serpentinized mantle peridotites. They are enriched in Cs, Pb and Sb and depleted in Rb, Ba, Nb, La, Ce, Sr, Zr, Er and Y relative to primitive mantle. The serpentinites are antigorite ones, pseudomorphic chrysotile varieties occur sparsely. Serpentinites from each massif contain specific non-serpentine phases. Ślęża serpentinites contain primary olivine-chromite aggregates, olivine and clinopyroxene aggregates interpreted as basaltic melt percolation phases, secondary olivine with magnetite inclusions (locally with cleavage) and secondary microcrystalline olivine-clinopyroxene-magnetite aggregates ("brownish aggregates") with bastite and mesh textures. The BBM serpentinites contain primary olivine-chromite aggregates, primary diopside grains, secondary magnetite-bearing olivine and tremolite. The SZM serpentinites contain olivine, tremolite and enstatite grains. Enstatite (Mg# = 92.8-93.0) contains >0.2 wt.% Cr2O3 and >0.7 Al2O3. All secondary non-serpentine phases are intergrown by antigorite. Very low overall trace element contents, Cs and high Pb-Sb anomalies of the CSO serpentinites are similar to subduction zone related serpentinites not affected by later fluid refertilization. Mineral assemblages shows prograde alteration of the rocks: (1) low-T serpentinization I forming pseudomorphic lizardite-chrysotile serpentinites; (2) antigorite recrystallization; (3) deserpentinization forming secondary olivine with magnetite inclusions, "brownish structures", tremolite and/or enstatite; (4) high-T serpentinization II forming antigorite intergrowths. Alteration degree is different in each massif: rocks from the SM are the most altered, they contain antigorite-olivine-enstatite-tremolite assemblage typical for temperatures
Directory of Open Access Journals (Sweden)
Tim E. Johnson
2015-05-01
Full Text Available Data from a migmatised metapelite raft enclosed within charnockite provide quantitative constraints on the pressure–temperature–time (P–T–t evolution of the Nagercoil Block at the southernmost tip of peninsular India. An inferred peak metamorphic assemblage of garnet, K-feldspar, sillimanite, plagioclase, magnetite, ilmenite, spinel and melt is consistent with peak metamorphic pressures of 6–8 kbar and temperatures in excess of 900 °C. Subsequent growth of cordierite and biotite record high-temperature retrograde decompression to around 5 kbar and 800 °C. SHRIMP U–Pb dating of magmatic zircon cores suggests that the sedimentary protoliths were in part derived from felsic igneous rocks with Palaeoproterozoic crystallisation ages. New growth of metamorphic zircon on the rims of detrital grains constrains the onset of melt crystallisation, and the minimum age of the metamorphic peak, to around 560 Ma. The data suggest two stages of monazite growth. The first generation of REE-enriched monazite grew during partial melting along the prograde path at around 570 Ma via the incongruent breakdown of apatite. Relatively REE-depleted rims, which have a pronounced negative europium anomaly, grew during melt crystallisation along the retrograde path at around 535 Ma. Our data show the rocks remained at suprasolidus temperatures for at least 35 million years and probably much longer, supporting a long-lived high-grade metamorphic history. The metamorphic conditions, timing and duration of the implied clockwise P–T–t path are similar to that previously established for other regions in peninsular India during the Ediacaran to Cambrian assembly of that part of the Gondwanan supercontinent.
Long, Hao; Fuchs, Markus; Yang, Linhai; Cheng, Hongyi
2016-05-13
Over the Tibetan Plateau and adjacent regions, numerous (14)C-based lake records revealed a ubiquitous wet climatic period during 40-25 ka (late MIS 3), which is in contradiction with the global pattern of generally cold and dry climates. This paper focuses on OSL dating results of a large set of sand dunes and alluvial sediments (50 OSL ages) from the Qinwangchuan (QWC) Basin at the northeast edge of the Tibetan Plateau, with the aim to test the validity of the anomalous wet condition for the late MIS 3 interval, evidenced by numerous lake highstands. The abrupt sand dune accumulation as indication of increased aridity in the study area was OSL dated to ~40-13 ka. This dry climatic inference of the sand dune system from QWC apparently shows no wet MIS 3a event. Thus, the anomalous wet conditions revealed by high lake levels for the late MIS 3 phase may not be a universal phenomena across entire western China.
Differentiating regressed melanoma from regressed lichenoid keratosis.
Chan, Aegean H; Shulman, Kenneth J; Lee, Bonnie A
2017-04-01
Distinguishing regressed lichen planus-like keratosis (LPLK) from regressed melanoma can be difficult on histopathologic examination, potentially resulting in mismanagement of patients. We aimed to identify histopathologic features by which regressed melanoma can be differentiated from regressed LPLK. Twenty actively inflamed LPLK, 12 LPLK with regression and 15 melanomas with regression were compared and evaluated by hematoxylin and eosin staining as well as Melan-A, microphthalmia transcription factor (MiTF) and cytokeratin (AE1/AE3) immunostaining. (1) A total of 40% of regressed melanomas showed complete or near complete loss of melanocytes within the epidermis with Melan-A and MiTF immunostaining, while 8% of regressed LPLK exhibited this finding. (2) Necrotic keratinocytes were seen in the epidermis in 33% regressed melanomas as opposed to all of the regressed LPLK. (3) A dense infiltrate of melanophages in the papillary dermis was seen in 40% of regressed melanomas, a feature not seen in regressed LPLK. In summary, our findings suggest that a complete or near complete loss of melanocytes within the epidermis strongly favors a regressed melanoma over a regressed LPLK. In addition, necrotic epidermal keratinocytes and the presence of a dense band-like distribution of dermal melanophages can be helpful in differentiating these lesions. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
Directory of Open Access Journals (Sweden)
Surlyk, Finn
2003-10-01
Full Text Available A Sinemurian mudstone-dominated succession was exposed until recently in the Gantofta quarry in Skåne, southern Sweden. The deposits are placed in the Döshult and Pankarp Members of the Sinemurian–Aalenian Rya Formation. Similar facies of the same age are widespread in the Danish Basin where they constitute the F-Ib unit (F-I member of the Fjerritslev Formation. The Gantofta succession thus represents the easternmost extension of the environment characteristic of the Fjerritslev Formation and is essentially the only locality where it has been possible tostudy the facies of this formation in outcrop. Sedimentation seems to have taken place under relatively quiet tectonic conditions except for the possible fault-control of the basin margin. Thelower part of the Gantofta section is of Early and early Late Sinemurian age. It represents the upper part of the Döshult Member and consists of muddy, lower shoreface sandstones, abruptlyoverlain by dark, bioturbated, fossiliferous mudstones with thin storm siltstones and sandstones. They are overlain by the Upper Sinemurian Pankarp Member which comprises red-brown, restricted marine calcareous mudstones with an upwards increasing number of storm siltstones and sandstones reflecting general shallowing and shoreline progradation.The succession spans the greater part of two simple sequences with a distal sequence boundary located at the boundary between the Döshult Member and the Pankarp Member. The exposed part of the lower sequence includes a thick transgressive systems tract and a very thin highstand systems tract. The upper sequence is represented by an undifferentiated transgressive and highstand systems tract. An Early Sinemurian sea-level rise, a late Early Sinemurian highstand, an early Late Sinemurian fall and a Late Sinemurian minor rise and a major fall are recognised. Nearby boreholes show evidence for an end-Sinemurian – Early Pliensbachian major rise. This evolution corresponds well with
Reinecke, T.
1998-03-01
Pelagic metasediments and MORB-type metabasalts of the former Tethyan oceanic crust at Cignana, Valtournanche, Italy, experienced UHP metamorphism and subsequent exhumation during the Early to Late Tertiary. Maximum PT conditions attained during UHP metamorphism were 600-630 °C, 2.7-2.9 GPa, which resulted in the formation of coesite-glaucophane-eclogites in the basaltic layer and of garnet-dolomite-aragonite-lawsonite-coesite-phengite-bearing calc-schists and garnet-phengite-coesite-schists with variable amounts of epidote, talc, dolomite, Na-pyroxene and Na-amphibole in the overlying metasediments. During subduction the rocks followed a prograde HP/UHP path which in correspondance with the Jurassic age of the Tethyan crust reflects the thermal influence of relatively old and cold lithosphere and of low to moderate shear heating. Inflections on the prograde metamorphic path may correspond to thermal effects that arise from a decrease in shear heating due to brittle-plastic transition in the quartz-aragonite-dominated rocks, induced convection in the asthenospheric mantle wedge and/or heat consumption by endothermic reactions over a restricted PT segment during subduction. After detachment from the downgoing slab some 50-70 Ma before present, the Cignana crustal slice was first exhumed to ca. 60 km and concomitantly cooled to ca. 550 °C, tracing back the UHP/HP prograde path displaced by 50-80 °C to higher temperatures. Exhumation at this stage is likely to have occurred in the Benioff zone, while the subduction of cool lithosphere was going on. Subsequently, the rocks were near-isothermally exhumed to ca. 30 km, followed by concomitant decompression and cooling to surface conditions (at glaucophane eclogites, the HP/UHP assemblages of the metasediments have been largely obliterated during exhumation. Relics from which the metamorphic evolution of the rocks during prograde HP metamorphism and the UHP stage can be retrieved are restricted to rigid low
Regression analysis by example
National Research Council Canada - National Science Library
Chatterjee, Samprit; Hadi, Ali S
2012-01-01
.... The emphasis continues to be on exploratory data analysis rather than statistical theory. The coverage offers in-depth treatment of regression diagnostics, transformation, multicollinearity, logistic regression, and robust regression...
DEFF Research Database (Denmark)
Johansen, Søren
2008-01-01
The reduced rank regression model is a multivariate regression model with a coefficient matrix with reduced rank. The reduced rank regression algorithm is an estimation procedure, which estimates the reduced rank regression model. It is related to canonical correlations and involves calculating e...... eigenvalues and eigenvectors. We give a number of different applications to regression and time series analysis, and show how the reduced rank regression estimator can be derived as a Gaussian maximum likelihood estimator. We briefly mention asymptotic results......The reduced rank regression model is a multivariate regression model with a coefficient matrix with reduced rank. The reduced rank regression algorithm is an estimation procedure, which estimates the reduced rank regression model. It is related to canonical correlations and involves calculating...
Regression analysis by example
Chatterjee, Samprit
2012-01-01
Praise for the Fourth Edition: ""This book is . . . an excellent source of examples for regression analysis. It has been and still is readily readable and understandable."" -Journal of the American Statistical Association Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgment. Regression Analysis by Example, Fifth Edition has been expanded
Flexible survival regression modelling
DEFF Research Database (Denmark)
Cortese, Giuliana; Scheike, Thomas H; Martinussen, Torben
2009-01-01
Regression analysis of survival data, and more generally event history data, is typically based on Cox's regression model. We here review some recent methodology, focusing on the limitations of Cox's regression model. The key limitation is that the model is not well suited to represent time-varyi...
Visualisation of Regression Trees
Brunsdon, Chris
2007-01-01
he regression tree [1] has been used as a tool for exploring multivariate data sets for some time. As in multiple linear regression, the technique is applied to a data set consisting of a contin- uous response variable y and a set of predictor variables { x 1 ,x 2 ,...,x k } which may be continuous or categorical. However, instead of modelling y as a linear function of the predictors, regression trees model y as a series of ...
Dabrowska, Dorota M.
1997-01-01
Nonparametric regression was shown by Beran and McKeague and Utikal to provide a flexible method for analysis of censored failure times and more general counting processes models in the presence of covariates. We discuss application of kernel smoothing towards estimation in a generalized Cox regression model with baseline intensity dependent on a covariate. Under regularity conditions we show that estimates of the regression parameters are asymptotically normal at rate root-n, and we also dis...
Introduction to regression graphics
Cook, R Dennis
2009-01-01
Covers the use of dynamic and interactive computer graphics in linear regression analysis, focusing on analytical graphics. Features new techniques like plot rotation. The authors have composed their own regression code, using Xlisp-Stat language called R-code, which is a nearly complete system for linear regression analysis and can be utilized as the main computer program in a linear regression course. The accompanying disks, for both Macintosh and Windows computers, contain the R-code and Xlisp-Stat. An Instructor's Manual presenting detailed solutions to all the problems in the book is ava
Alternative Methods of Regression
Birkes, David
2011-01-01
Of related interest. Nonlinear Regression Analysis and its Applications Douglas M. Bates and Donald G. Watts ".an extraordinary presentation of concepts and methods concerning the use and analysis of nonlinear regression models.highly recommend[ed].for anyone needing to use and/or understand issues concerning the analysis of nonlinear regression models." --Technometrics This book provides a balance between theory and practice supported by extensive displays of instructive geometrical constructs. Numerous in-depth case studies illustrate the use of nonlinear regression analysis--with all data s
Energy Technology Data Exchange (ETDEWEB)
Gerber, Samuel [Univ. of Utah, Salt Lake City, UT (United States); Rubel, Oliver [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Bremer, Peer -Timo [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Pascucci, Valerio [Univ. of Utah, Salt Lake City, UT (United States); Whitaker, Ross T. [Univ. of Utah, Salt Lake City, UT (United States)
2012-01-19
This paper introduces a novel partition-based regression approach that incorporates topological information. Partition-based regression typically introduces a quality-of-fit-driven decomposition of the domain. The emphasis in this work is on a topologically meaningful segmentation. Thus, the proposed regression approach is based on a segmentation induced by a discrete approximation of the Morse–Smale complex. This yields a segmentation with partitions corresponding to regions of the function with a single minimum and maximum that are often well approximated by a linear model. This approach yields regression models that are amenable to interpretation and have good predictive capacity. Typically, regression estimates are quantified by their geometrical accuracy. For the proposed regression, an important aspect is the quality of the segmentation itself. Thus, this article introduces a new criterion that measures the topological accuracy of the estimate. The topological accuracy provides a complementary measure to the classical geometrical error measures and is very sensitive to overfitting. The Morse–Smale regression is compared to state-of-the-art approaches in terms of geometry and topology and yields comparable or improved fits in many cases. Finally, a detailed study on climate-simulation data demonstrates the application of the Morse–Smale regression. Supplementary Materials are available online and contain an implementation of the proposed approach in the R package msr, an analysis and simulations on the stability of the Morse–Smale complex approximation, and additional tables for the climate-simulation study.
DEFF Research Database (Denmark)
Fitzenberger, Bernd; Wilke, Ralf Andreas
2015-01-01
Quantile regression is emerging as a popular statistical approach, which complements the estimation of conditional mean models. While the latter only focuses on one aspect of the conditional distribution of the dependent variable, the mean, quantile regression provides more detailed insights...
DEFF Research Database (Denmark)
Bordacconi, Mats Joe; Larsen, Martin Vinæs
2014-01-01
Humans are fundamentally primed for making causal attributions based on correlations. This implies that researchers must be careful to present their results in a manner that inhibits unwarranted causal attribution. In this paper, we present the results of an experiment that suggests regression...... models – one of the primary vehicles for analyzing statistical results in political science – encourage causal interpretation. Specifically, we demonstrate that presenting observational results in a regression model, rather than as a simple comparison of means, makes causal interpretation of the results...... of equivalent results presented as either regression models or as a test of two sample means. Our experiment shows that the subjects who were presented with results as estimates from a regression model were more inclined to interpret these results causally. Our experiment implies that scholars using regression...
Sequence Stratigraphic Appraisal: Coastal Swamp Depobelt In The ...
African Journals Online (AJOL)
The massive sand formation of the basin floor fan, the sand-rich prograding wedge and the highstand sands as well as the transgressive sands constitute good reservoirs. The distal shale toes of the prograding wedge and transgressive shales as well as highstand shales form seals for the stratigraphic traps formed in the ...
Weisberg, Sanford
2013-01-01
Praise for the Third Edition ""...this is an excellent book which could easily be used as a course text...""-International Statistical Institute The Fourth Edition of Applied Linear Regression provides a thorough update of the basic theory and methodology of linear regression modeling. Demonstrating the practical applications of linear regression analysis techniques, the Fourth Edition uses interesting, real-world exercises and examples. Stressing central concepts such as model building, understanding parameters, assessing fit and reliability, and drawing conclusions, the new edition illus
Hosmer, David W; Sturdivant, Rodney X
2013-01-01
A new edition of the definitive guide to logistic regression modeling for health science and other applications This thoroughly expanded Third Edition provides an easily accessible introduction to the logistic regression (LR) model and highlights the power of this model by examining the relationship between a dichotomous outcome and a set of covariables. Applied Logistic Regression, Third Edition emphasizes applications in the health sciences and handpicks topics that best suit the use of modern statistical software. The book provides readers with state-of-
Semiparametric Regression Pursuit.
Huang, Jian; Wei, Fengrong; Ma, Shuangge
2012-10-01
The semiparametric partially linear model allows flexible modeling of covariate effects on the response variable in regression. It combines the flexibility of nonparametric regression and parsimony of linear regression. The most important assumption in the existing methods for the estimation in this model is to assume a priori that it is known which covariates have a linear effect and which do not. However, in applied work, this is rarely known in advance. We consider the problem of estimation in the partially linear models without assuming a priori which covariates have linear effects. We propose a semiparametric regression pursuit method for identifying the covariates with a linear effect. Our proposed method is a penalized regression approach using a group minimax concave penalty. Under suitable conditions we show that the proposed approach is model-pursuit consistent, meaning that it can correctly determine which covariates have a linear effect and which do not with high probability. The performance of the proposed method is evaluated using simulation studies, which support our theoretical results. A real data example is used to illustrated the application of the proposed method.
[Understanding logistic regression].
El Sanharawi, M; Naudet, F
2013-10-01
Logistic regression is one of the most common multivariate analysis models utilized in epidemiology. It allows the measurement of the association between the occurrence of an event (qualitative dependent variable) and factors susceptible to influence it (explicative variables). The choice of explicative variables that should be included in the logistic regression model is based on prior knowledge of the disease physiopathology and the statistical association between the variable and the event, as measured by the odds ratio. The main steps for the procedure, the conditions of application, and the essential tools for its interpretation are discussed concisely. We also discuss the importance of the choice of variables that must be included and retained in the regression model in order to avoid the omission of important confounding factors. Finally, by way of illustration, we provide an example from the literature, which should help the reader test his or her knowledge. Copyright © 2013 Elsevier Masson SAS. All rights reserved.
Simultaneous Inference in Regression
Liu, Wei
2010-01-01
The use of simultaneous confidence bands in linear regression is a vibrant area of research. This book presents an overview of the methodology and applications, including necessary background material on linear models. A special chapter on logistic regression gives readers a glimpse into how these methods can be used for generalized linear models. The appendices provide computational tools for simulating confidence bands. The author also includes MATLAB[registered] programs for all examples on the web. With many numerical examples and software implementation, this text serves the needs of rese
Ritz, Christian; Parmigiani, Giovanni
2009-01-01
R is a rapidly evolving lingua franca of graphical display and statistical analysis of experiments from the applied sciences. This book provides a coherent treatment of nonlinear regression with R by means of examples from a diversity of applied sciences such as biology, chemistry, engineering, medicine and toxicology.
African Journals Online (AJOL)
zlukovi
modelled as a quadratic regression, nested within parity. The previous lactation length was ... This proportion was mainly covered by linear and quadratic coefficients. Results suggest that RRM could .... The multiple trait models in scalar notation are presented by equations (1, 2), while equation. (3) represents the random ...
Modern Regression Discontinuity Analysis
Bloom, Howard S.
2012-01-01
This article provides a detailed discussion of the theory and practice of modern regression discontinuity (RD) analysis for estimating the effects of interventions or treatments. Part 1 briefly chronicles the history of RD analysis and summarizes its past applications. Part 2 explains how in theory an RD analysis can identify an average effect of…
Multiple linear regression analysis
Edwards, T. R.
1980-01-01
Program rapidly selects best-suited set of coefficients. User supplies only vectors of independent and dependent data and specifies confidence level required. Program uses stepwise statistical procedure for relating minimal set of variables to set of observations; final regression contains only most statistically significant coefficients. Program is written in FORTRAN IV for batch execution and has been implemented on NOVA 1200.
Seber, George A F
2012-01-01
Concise, mathematically clear, and comprehensive treatment of the subject.* Expanded coverage of diagnostics and methods of model fitting.* Requires no specialized knowledge beyond a good grasp of matrix algebra and some acquaintance with straight-line regression and simple analysis of variance models.* More than 200 problems throughout the book plus outline solutions for the exercises.* This revision has been extensively class-tested.
Bayesian ARTMAP for regression.
Sasu, L M; Andonie, R
2013-10-01
Bayesian ARTMAP (BA) is a recently introduced neural architecture which uses a combination of Fuzzy ARTMAP competitive learning and Bayesian learning. Training is generally performed online, in a single-epoch. During training, BA creates input data clusters as Gaussian categories, and also infers the conditional probabilities between input patterns and categories, and between categories and classes. During prediction, BA uses Bayesian posterior probability estimation. So far, BA was used only for classification. The goal of this paper is to analyze the efficiency of BA for regression problems. Our contributions are: (i) we generalize the BA algorithm using the clustering functionality of both ART modules, and name it BA for Regression (BAR); (ii) we prove that BAR is a universal approximator with the best approximation property. In other words, BAR approximates arbitrarily well any continuous function (universal approximation) and, for every given continuous function, there is one in the set of BAR approximators situated at minimum distance (best approximation); (iii) we experimentally compare the online trained BAR with several neural models, on the following standard regression benchmarks: CPU Computer Hardware, Boston Housing, Wisconsin Breast Cancer, and Communities and Crime. Our results show that BAR is an appropriate tool for regression tasks, both for theoretical and practical reasons. Copyright © 2013 Elsevier Ltd. All rights reserved.
Bounded Gaussian process regression
DEFF Research Database (Denmark)
Jensen, Bjørn Sand; Nielsen, Jens Brehm; Larsen, Jan
2013-01-01
We extend the Gaussian process (GP) framework for bounded regression by introducing two bounded likelihood functions that model the noise on the dependent variable explicitly. This is fundamentally different from the implicit noise assumption in the previously suggested warped GP framework. We...
Mechanisms of neuroblastoma regression
Brodeur, Garrett M.; Bagatell, Rochelle
2014-01-01
Recent genomic and biological studies of neuroblastoma have shed light on the dramatic heterogeneity in the clinical behaviour of this disease, which spans from spontaneous regression or differentiation in some patients, to relentless disease progression in others, despite intensive multimodality therapy. This evidence also suggests several possible mechanisms to explain the phenomena of spontaneous regression in neuroblastomas, including neurotrophin deprivation, humoral or cellular immunity, loss of telomerase activity and alterations in epigenetic regulation. A better understanding of the mechanisms of spontaneous regression might help to identify optimal therapeutic approaches for patients with these tumours. Currently, the most druggable mechanism is the delayed activation of developmentally programmed cell death regulated by the tropomyosin receptor kinase A pathway. Indeed, targeted therapy aimed at inhibiting neurotrophin receptors might be used in lieu of conventional chemotherapy or radiation in infants with biologically favourable tumours that require treatment. Alternative approaches consist of breaking immune tolerance to tumour antigens or activating neurotrophin receptor pathways to induce neuronal differentiation. These approaches are likely to be most effective against biologically favourable tumours, but they might also provide insights into treatment of biologically unfavourable tumours. We describe the different mechanisms of spontaneous neuroblastoma regression and the consequent therapeutic approaches. PMID:25331179
Subset selection in regression
Miller, Alan
2002-01-01
Originally published in 1990, the first edition of Subset Selection in Regression filled a significant gap in the literature, and its critical and popular success has continued for more than a decade. Thoroughly revised to reflect progress in theory, methods, and computing power, the second edition promises to continue that tradition. The author has thoroughly updated each chapter, incorporated new material on recent developments, and included more examples and references. New in the Second Edition:A separate chapter on Bayesian methodsComplete revision of the chapter on estimationA major example from the field of near infrared spectroscopyMore emphasis on cross-validationGreater focus on bootstrappingStochastic algorithms for finding good subsets from large numbers of predictors when an exhaustive search is not feasible Software available on the Internet for implementing many of the algorithms presentedMore examplesSubset Selection in Regression, Second Edition remains dedicated to the techniques for fitting...
Classification and regression trees
Breiman, Leo; Olshen, Richard A; Stone, Charles J
1984-01-01
The methodology used to construct tree structured rules is the focus of this monograph. Unlike many other statistical procedures, which moved from pencil and paper to calculators, this text's use of trees was unthinkable before computers. Both the practical and theoretical sides have been developed in the authors' study of tree methods. Classification and Regression Trees reflects these two sides, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties.
Better Autologistic Regression
Directory of Open Access Journals (Sweden)
Mark A. Wolters
2017-11-01
Full Text Available Autologistic regression is an important probability model for dichotomous random variables observed along with covariate information. It has been used in various fields for analyzing binary data possessing spatial or network structure. The model can be viewed as an extension of the autologistic model (also known as the Ising model, quadratic exponential binary distribution, or Boltzmann machine to include covariates. It can also be viewed as an extension of logistic regression to handle responses that are not independent. Not all authors use exactly the same form of the autologistic regression model. Variations of the model differ in two respects. First, the variable coding—the two numbers used to represent the two possible states of the variables—might differ. Common coding choices are (zero, one and (minus one, plus one. Second, the model might appear in either of two algebraic forms: a standard form, or a recently proposed centered form. Little attention has been paid to the effect of these differences, and the literature shows ambiguity about their importance. It is shown here that changes to either coding or centering in fact produce distinct, non-nested probability models. Theoretical results, numerical studies, and analysis of an ecological data set all show that the differences among the models can be large and practically significant. Understanding the nature of the differences and making appropriate modeling choices can lead to significantly improved autologistic regression analyses. The results strongly suggest that the standard model with plus/minus coding, which we call the symmetric autologistic model, is the most natural choice among the autologistic variants.
DEFF Research Database (Denmark)
Hansen, Henrik; Tarp, Finn
2001-01-01
. There are, however, decreasing returns to aid, and the estimated effectiveness of aid is highly sensitive to the choice of estimator and the set of control variables. When investment and human capital are controlled for, no positive effect of aid is found. Yet, aid continues to impact on growth via...... investment. We conclude by stressing the need for more theoretical work before this kind of cross-country regressions are used for policy purposes....
Hilbe, Joseph M
2009-01-01
This book really does cover everything you ever wanted to know about logistic regression … with updates available on the author's website. Hilbe, a former national athletics champion, philosopher, and expert in astronomy, is a master at explaining statistical concepts and methods. Readers familiar with his other expository work will know what to expect-great clarity.The book provides considerable detail about all facets of logistic regression. No step of an argument is omitted so that the book will meet the needs of the reader who likes to see everything spelt out, while a person familiar with some of the topics has the option to skip "obvious" sections. The material has been thoroughly road-tested through classroom and web-based teaching. … The focus is on helping the reader to learn and understand logistic regression. The audience is not just students meeting the topic for the first time, but also experienced users. I believe the book really does meet the author's goal … .-Annette J. Dobson, Biometric...
Adaptive metric kernel regression
DEFF Research Database (Denmark)
Goutte, Cyril; Larsen, Jan
2000-01-01
Kernel smoothing is a widely used non-parametric pattern recognition technique. By nature, it suffers from the curse of dimensionality and is usually difficult to apply to high input dimensions. In this contribution, we propose an algorithm that adapts the input metric used in multivariate...... regression by minimising a cross-validation estimate of the generalisation error. This allows to automatically adjust the importance of different dimensions. The improvement in terms of modelling performance is illustrated on a variable selection task where the adaptive metric kernel clearly outperforms...
Adaptive Metric Kernel Regression
DEFF Research Database (Denmark)
Goutte, Cyril; Larsen, Jan
1998-01-01
Kernel smoothing is a widely used nonparametric pattern recognition technique. By nature, it suffers from the curse of dimensionality and is usually difficult to apply to high input dimensions. In this paper, we propose an algorithm that adapts the input metric used in multivariate regression...... by minimising a cross-validation estimate of the generalisation error. This allows one to automatically adjust the importance of different dimensions. The improvement in terms of modelling performance is illustrated on a variable selection task where the adaptive metric kernel clearly outperforms the standard...
Anczkiewicz, Robert; Chakraborty, Sumit; Dasgupta, Somnath; Mukhopadhyay, Dilip; Kołtonik, Katarzyna
2014-12-01
We investigated Lu-Hf isotopic systematics in garnets from gradually higher grade metamorphic rocks from the first appearance of garnet at c. 500 °C to biotite dehydration melting at c. 800 °C in the Sikkim Himalaya, India. Exceptionally precise Lu-Hf ages obtained for the Barrovian metasedimentary sequence in the Lesser Himalaya (LH) correspond to the time of early garnet formation on a prograde path and show remarkable correlation with increasing metamorphic grade and decreasing structural depth. The youngest age is reported for the garnet zone (10.6±0.2 Ma) and then the ages become progressively older in the staurolite (12.8±0.3 Ma), kyanite (13.7±0.2 Ma) and sillimanite (14.6±0.1 Ma) zones. The oldest age of 16.8±0.1 Ma was recorded at the top of the sequence in the zone marking the onset of muscovite dehydration melting, directly below the Main Central Thrust (MCT). These ages provide a tight constraint on the timing and duration of the Barrovian sequence formation which lasted about 6 Ma. The age pattern is clearly inverted with respect to structural depth but shows "normal" correlation with the metamorphic grade, i.e. earlier garnet growth in higher grade rocks. The timing of the peak metamorphic conditions estimated for the garnet and kyanite zones suggests that thermal climax occurred in all zones within a relatively short time span about 10-13 Ma ago. The metamorphic inversion in the Sikkim Himalaya must therefore have occurred after the metamorphic peak was attained in all grades. Lu-Hf garnet ages in the overthrust Higher Himalaya (HH) continue to be older but do not show a clear progression. There is about 6 Ma jump to 22.6±0.1 Ma within the MCT zone and even older ages of 26-28 Ma were obtained for migmatites from the lower part of the HH. The Lu-Hf system in garnets from the HH records the time of melting at or near the thermal peak rather than dates initial garnet growth as in the LH. The age pattern obtained in this study provides precise
An Introduction to Logistic Regression.
Cizek, Gregory J.; Fitzgerald, Shawn M.
1999-01-01
Where linearity cannot be assumed, logistic regression may be appropriate. This article describes conditions and tests for using logistic regression; introduces the logistic-regression model, the use of logistic-regression software, and some applications in published literature. Univariate and multiple independent-variable conditions and…
Reciprocal Causation in Regression Analysis.
Wolfle, Lee M.
1979-01-01
With even the simplest bivariate regression, least-squares solutions are inappropriate unless one assumes a priori that reciprocal effects are absent, or at least implausible. While this discussion is limited to bivariate regression, the issues apply equally to multivariate regression, including stepwise regression. (Author/CTM)
Modified Regression Correlation Coefficient for Poisson Regression Model
Kaengthong, Nattacha; Domthong, Uthumporn
2017-09-01
This study gives attention to indicators in predictive power of the Generalized Linear Model (GLM) which are widely used; however, often having some restrictions. We are interested in regression correlation coefficient for a Poisson regression model. This is a measure of predictive power, and defined by the relationship between the dependent variable (Y) and the expected value of the dependent variable given the independent variables [E(Y|X)] for the Poisson regression model. The dependent variable is distributed as Poisson. The purpose of this research was modifying regression correlation coefficient for Poisson regression model. We also compare the proposed modified regression correlation coefficient with the traditional regression correlation coefficient in the case of two or more independent variables, and having multicollinearity in independent variables. The result shows that the proposed regression correlation coefficient is better than the traditional regression correlation coefficient based on Bias and the Root Mean Square Error (RMSE).
Combining Alphas via Bounded Regression
Directory of Open Access Journals (Sweden)
Zura Kakushadze
2015-11-01
Full Text Available We give an explicit algorithm and source code for combining alpha streams via bounded regression. In practical applications, typically, there is insufficient history to compute a sample covariance matrix (SCM for a large number of alphas. To compute alpha allocation weights, one then resorts to (weighted regression over SCM principal components. Regression often produces alpha weights with insufficient diversification and/or skewed distribution against, e.g., turnover. This can be rectified by imposing bounds on alpha weights within the regression procedure. Bounded regression can also be applied to stock and other asset portfolio construction. We discuss illustrative examples.
Time-adaptive quantile regression
DEFF Research Database (Denmark)
Møller, Jan Kloppenborg; Nielsen, Henrik Aalborg; Madsen, Henrik
2008-01-01
An algorithm for time-adaptive quantile regression is presented. The algorithm is based on the simplex algorithm, and the linear optimization formulation of the quantile regression problem is given. The observations have been split to allow a direct use of the simplex algorithm. The simplex method...... and an updating procedure are combined into a new algorithm for time-adaptive quantile regression, which generates new solutions on the basis of the old solution, leading to savings in computation time. The suggested algorithm is tested against a static quantile regression model on a data set with wind power...... production, where the models combine splines and quantile regression. The comparison indicates superior performance for the time-adaptive quantile regression in all the performance parameters considered....
Energy Technology Data Exchange (ETDEWEB)
Bartek, L.R.; Wellner, R. (Univ. of Alabama, Tuscaloosa, AL (United States))
1996-01-01
Geopulse seismic reflection (2,825 km) data collected during a survey of the East China Sea (ECS) in September of 1993 have been used to reconstruct the shallow stratigraphic architecture of the ECS continental margin. This area is characterized by a broad continental shelf and has extremely high sediment supply relative to other margins. On the inner to middle portions of the ECS margin we identified extensive areas outside of several incised valleys that contain channelized seismic facies that are interpreted as fluvial sequences deposited as sea level fell prior to the last low-stands. These deposits lie above highstand silts and clays and beneath a transgressive surface, above which sediments appear to have been extensively reworked. Historical records suggest that the tremendous sediment load of the Yellow River caused the river to avulse over an area of hundreds of kilometers during the Holocene and deposition of thick sheet of fluvial sands in [open quotes]interfluvial[close quotes] areas. We suggest that as sea level fall in this area, the equilibrium point and bayline synchronously migrated seaward, and subareal accommodation was created during the latter stages of highstands, in a manner similar to that proposed in published models. The high sediment supply of the area and increasing subareal accommodation space provided an opportunity for deposition of the laterally extensive fluvial facies we observe on the seismic data. The upper portions of these [open quotes]interfluvial[close quotes] fluvial deposits were reworked during the ensuing transgression and downlapped upon by muddy highstand deposits, but the lower fluvial sheet-sand facies, are preserved in place. This situation creates a laterally extensive, braided fluvial sand type reservoir with a potential for a stratigraphic seal that is within close proximity to hydrocarbon source rocks.
Energy Technology Data Exchange (ETDEWEB)
Bartek, L.R.; Wellner, R. [Univ. of Alabama, Tuscaloosa, AL (United States)
1996-12-31
Geopulse seismic reflection (2,825 km) data collected during a survey of the East China Sea (ECS) in September of 1993 have been used to reconstruct the shallow stratigraphic architecture of the ECS continental margin. This area is characterized by a broad continental shelf and has extremely high sediment supply relative to other margins. On the inner to middle portions of the ECS margin we identified extensive areas outside of several incised valleys that contain channelized seismic facies that are interpreted as fluvial sequences deposited as sea level fell prior to the last low-stands. These deposits lie above highstand silts and clays and beneath a transgressive surface, above which sediments appear to have been extensively reworked. Historical records suggest that the tremendous sediment load of the Yellow River caused the river to avulse over an area of hundreds of kilometers during the Holocene and deposition of thick sheet of fluvial sands in {open_quotes}interfluvial{close_quotes} areas. We suggest that as sea level fall in this area, the equilibrium point and bayline synchronously migrated seaward, and subareal accommodation was created during the latter stages of highstands, in a manner similar to that proposed in published models. The high sediment supply of the area and increasing subareal accommodation space provided an opportunity for deposition of the laterally extensive fluvial facies we observe on the seismic data. The upper portions of these {open_quotes}interfluvial{close_quotes} fluvial deposits were reworked during the ensuing transgression and downlapped upon by muddy highstand deposits, but the lower fluvial sheet-sand facies, are preserved in place. This situation creates a laterally extensive, braided fluvial sand type reservoir with a potential for a stratigraphic seal that is within close proximity to hydrocarbon source rocks.
Evaluating Differential Effects Using Regression Interactions and Regression Mixture Models
Van Horn, M. Lee; Jaki, Thomas; Masyn, Katherine; Howe, George; Feaster, Daniel J.; Lamont, Andrea E.; George, Melissa R. W.; Kim, Minjung
2015-01-01
Research increasingly emphasizes understanding differential effects. This article focuses on understanding regression mixture models, which are relatively new statistical methods for assessing differential effects by comparing results to using an interactive term in linear regression. The research questions which each model answers, their…
Bias-corrected quantile regression estimation of censored regression models
Cizek, Pavel; Sadikoglu, Serhan
2018-01-01
In this paper, an extension of the indirect inference methodology to semiparametric estimation is explored in the context of censored regression. Motivated by weak small-sample performance of the censored regression quantile estimator proposed by Powell (J Econom 32:143–155, 1986a), two- and
Quantum assisted Gaussian process regression
Zhao, Zhikuan; Fitzsimons, Jack K.; Fitzsimons, Joseph F.
2015-01-01
Gaussian processes (GP) are a widely used model for regression problems in supervised machine learning. Implementation of GP regression typically requires $O(n^3)$ logic gates. We show that the quantum linear systems algorithm [Harrow et al., Phys. Rev. Lett. 103, 150502 (2009)] can be applied to Gaussian process regression (GPR), leading to an exponential reduction in computation time in some instances. We show that even in some cases not ideally suited to the quantum linear systems algorith...
Energy Technology Data Exchange (ETDEWEB)
Arato, Hiroyuki. [Teikoku Oil Corp, Tokyo (Japan). Technical Research Center
1999-01-01
Dialectic prediction of reservoir distribution using conceptual facies models is one of the most direct techniques in petroleum exploration for reducing uncertainties about the factors in a petroleum system. If reservoir distribution could be predicted logically on the basis of generics of strata and sedimentary processes with inproved accuracy, then previously discovered petroleum systems might be reconsidered as future objectives of subtle entrapments. In this study a fourth-order daces model was constructed for Quaternary of the Niigata Basin within a back-arc setting, based on the concept of sequence stratigraphy. A progradational type fourth-order depositional sequence is subdivided into the following four systems tracts, which are defined by their morphologies and benthic foraminiferal faunas: (1) regressive progradational wedge systems tract (RPW), consisting of a forced-regressive strandplain system and associated depositional systems, (2) lowstand stable progradational wedge systems trac (LPW), consisting mainly of an incised-valley fill braidplain dalta system and associated systems, (3) transgressive aggradational sheet systems tract (TAS), consisting of a filled-estuary system and associated depositional systems, and (4) highstand stable progradational wedge systems tract (HPW), consisting of a fluvial-dominated dalta system and associated system. The results of this study demonstrate the effectiveness of sequence stratigraphic approaches and provide useful examples of occurrence, development, and sedimentary facies distribution for prograding coasts in ative margin basins. The development of such facies models improves our ability to the presence of sudetected standstone reservoirs, which is one of the most important factors of petroleum system. (autor)
Quantile regression theory and applications
Davino, Cristina; Vistocco, Domenico
2013-01-01
A guide to the implementation and interpretation of Quantile Regression models This book explores the theory and numerous applications of quantile regression, offering empirical data analysis as well as the software tools to implement the methods. The main focus of this book is to provide the reader with a comprehensivedescription of the main issues concerning quantile regression; these include basic modeling, geometrical interpretation, estimation and inference for quantile regression, as well as issues on validity of the model, diagnostic tools. Each methodological aspect is explored and
Testing discontinuities in nonparametric regression
Dai, Wenlin
2017-01-19
In nonparametric regression, it is often needed to detect whether there are jump discontinuities in the mean function. In this paper, we revisit the difference-based method in [13 H.-G. Müller and U. Stadtmüller, Discontinuous versus smooth regression, Ann. Stat. 27 (1999), pp. 299–337. doi: 10.1214/aos/1018031100
Logistic Regression: Concept and Application
Cokluk, Omay
2010-01-01
The main focus of logistic regression analysis is classification of individuals in different groups. The aim of the present study is to explain basic concepts and processes of binary logistic regression analysis intended to determine the combination of independent variables which best explain the membership in certain groups called dichotomous…
Panel Smooth Transition Regression Models
DEFF Research Database (Denmark)
González, Andrés; Terasvirta, Timo; Dijk, Dick van
We introduce the panel smooth transition regression model. This new model is intended for characterizing heterogeneous panels, allowing the regression coefficients to vary both across individuals and over time. Specifically, heterogeneity is allowed for by assuming that these coefficients are bou...
Regression analysis with categorized regression calibrated exposure: some interesting findings
Directory of Open Access Journals (Sweden)
Hjartåker Anette
2006-07-01
Full Text Available Abstract Background Regression calibration as a method for handling measurement error is becoming increasingly well-known and used in epidemiologic research. However, the standard version of the method is not appropriate for exposure analyzed on a categorical (e.g. quintile scale, an approach commonly used in epidemiologic studies. A tempting solution could then be to use the predicted continuous exposure obtained through the regression calibration method and treat it as an approximation to the true exposure, that is, include the categorized calibrated exposure in the main regression analysis. Methods We use semi-analytical calculations and simulations to evaluate the performance of the proposed approach compared to the naive approach of not correcting for measurement error, in situations where analyses are performed on quintile scale and when incorporating the original scale into the categorical variables, respectively. We also present analyses of real data, containing measures of folate intake and depression, from the Norwegian Women and Cancer study (NOWAC. Results In cases where extra information is available through replicated measurements and not validation data, regression calibration does not maintain important qualities of the true exposure distribution, thus estimates of variance and percentiles can be severely biased. We show that the outlined approach maintains much, in some cases all, of the misclassification found in the observed exposure. For that reason, regression analysis with the corrected variable included on a categorical scale is still biased. In some cases the corrected estimates are analytically equal to those obtained by the naive approach. Regression calibration is however vastly superior to the naive method when applying the medians of each category in the analysis. Conclusion Regression calibration in its most well-known form is not appropriate for measurement error correction when the exposure is analyzed on a
Advanced statistics: linear regression, part II: multiple linear regression.
Marill, Keith A
2004-01-01
The applications of simple linear regression in medical research are limited, because in most situations, there are multiple relevant predictor variables. Univariate statistical techniques such as simple linear regression use a single predictor variable, and they often may be mathematically correct but clinically misleading. Multiple linear regression is a mathematical technique used to model the relationship between multiple independent predictor variables and a single dependent outcome variable. It is used in medical research to model observational data, as well as in diagnostic and therapeutic studies in which the outcome is dependent on more than one factor. Although the technique generally is limited to data that can be expressed with a linear function, it benefits from a well-developed mathematical framework that yields unique solutions and exact confidence intervals for regression coefficients. Building on Part I of this series, this article acquaints the reader with some of the important concepts in multiple regression analysis. These include multicollinearity, interaction effects, and an expansion of the discussion of inference testing, leverage, and variable transformations to multivariate models. Examples from the first article in this series are expanded on using a primarily graphic, rather than mathematical, approach. The importance of the relationships among the predictor variables and the dependence of the multivariate model coefficients on the choice of these variables are stressed. Finally, concepts in regression model building are discussed.
Logic regression and its extensions.
Schwender, Holger; Ruczinski, Ingo
2010-01-01
Logic regression is an adaptive classification and regression procedure, initially developed to reveal interacting single nucleotide polymorphisms (SNPs) in genetic association studies. In general, this approach can be used in any setting with binary predictors, when the interaction of these covariates is of primary interest. Logic regression searches for Boolean (logic) combinations of binary variables that best explain the variability in the outcome variable, and thus, reveals variables and interactions that are associated with the response and/or have predictive capabilities. The logic expressions are embedded in a generalized linear regression framework, and thus, logic regression can handle a variety of outcome types, such as binary responses in case-control studies, numeric responses, and time-to-event data. In this chapter, we provide an introduction to the logic regression methodology, list some applications in public health and medicine, and summarize some of the direct extensions and modifications of logic regression that have been proposed in the literature. Copyright © 2010 Elsevier Inc. All rights reserved.
Schwab, Valérie; Spangenberg, Jorge. E.; Grimalt, Joan O.
2005-04-01
Hydrocarbon distributions and stable isotope ratios of carbonates (δ 13C car, δ 18O car), kerogen (δ 13C ker), extractable organic matter (δ 13C EOM) and individual hydrocarbons of Liassic black shale samples from a prograde metamorphic sequence in the Swiss Alps were used to identify the major organic reactions with increasing metamorphic grade. The studied samples range from the diagenetic zone (17n-alkanes suggests the occurrence of cracking reactions of high molecular weight compounds. The isotopically heavier (up to 5.6 ‰) C 13n-alkanes with metamorphism suggests progressive thermal release of kerogen-linked fatty acid precursors and degradation of n-alkanes. Changes of the steroid and terpenoid distributions are clearly related to increasing metamorphic temperatures. The absence of 18α(H)-22,29,30-trisnorneohopane (Ts), the occurrence of 17β(H)-trisnorhopane, 17β(H), 21α(H)-hopanes in the C 29 to C 31 range and 5α(H),14α(H),17α(H)-20R C 27, C 29 steranes in the low diagenetic samples (bitumens. The higher thermal stress within the upper diagenetic zone (150°C) is marked by the presence of Ts, the disappearance of 17β(H)-trisnorhopane and thermodynamic equilibrium of the 22S/(22S + 22R) homohopane ratios. The increase of the ααα-sterane 20S/(20S + 20R) and 20R ββ/(ββ + αα) ratios (from 0.0 to 0.55 and from 0.0 to 0.40, respectively) in the upper diagenetic zone indicates the occurrence of isomerization reactions already at carbon isotopic compositions of n-alkanes related to metamorphism suggest that the organic geochemistry may help to evaluate the lowest grades of prograde metamorphism.
Weller, O. M.; St-Onge, M. R.; Rayner, N.; Waters, D. J.; Searle, M. P.; Palin, R. M.
2016-10-01
The Sumdo complex is a Permian-Triassic eclogitic metamorphic belt in south-east Tibet, which marks the location of a suture zone that separates the northern and southern Lhasa terranes. An integrated geochronological and petrological study of a mafic eclogite from the complex has constrained its tectonometamorphic history and provides a case study of zircon growth in eclogite as a product of prograde dissolution-precipitation. In situ U-Pb geochronology indicates that the eclogite contains a single population of zircon with a crystallisation age of 273.6 ± 2.8 Ma. The morphology and chemistry of the zircon grains are consistent with growth by dissolution-precipitation of protolith magmatic zircon. The presence of zircon grains as inclusions in the cores of peak phases indicates that zircon dissolution-precipitation occurred during prograde metamorphism, and calculated pressure and temperature conditions over which mineral inclusions in zircon are stable suggest that the zircon most likely precipitated at 15.5-16.5 kbar and 500-560 °C. Subsequent peak metamorphism is calculated to have reached pressure-temperature conditions of 27 ± 1 kbar and 670 ± 50 °C. Previous studies, which have documented a range of peak metamorphic conditions from high- to ultrahigh-pressure at c. 266-230 Ma, indicate that the Sumdo complex is a composite belt that experienced protracted eclogite exhumation. The results of this study are consistent with this interpretation, and extend the age range of high-pressure metamorphism in the complex to over 40 Myr. Analysis of published pressure-temperature-time data indicates two systematic behaviours within this spread. First, peak metamorphic temperatures declined over time. Second, eclogite exhumation occurred in two discrete intervals: soon after formation, and during the demise of the subduction zone. The latter behaviour serves as a reminder that eclogite exhumation is the exception rather than the rule.
Practical Session: Simple Linear Regression
Clausel, M.; Grégoire, G.
2014-12-01
Two exercises are proposed to illustrate the simple linear regression. The first one is based on the famous Galton's data set on heredity. We use the lm R command and get coefficients estimates, standard error of the error, R2, residuals …In the second example, devoted to data related to the vapor tension of mercury, we fit a simple linear regression, predict values, and anticipate on multiple linear regression. This pratical session is an excerpt from practical exercises proposed by A. Dalalyan at EPNC (see Exercises 1 and 2 of http://certis.enpc.fr/~dalalyan/Download/TP_ENPC_4.pdf).
Multiple Regression and Its Discontents
Snell, Joel C.; Marsh, Mitchell
2012-01-01
Multiple regression is part of a larger statistical strategy originated by Gauss. The authors raise questions about the theory and suggest some changes that would make room for Mandelbrot and Serendipity.
Regression methods for medical research
Tai, Bee Choo
2013-01-01
Regression Methods for Medical Research provides medical researchers with the skills they need to critically read and interpret research using more advanced statistical methods. The statistical requirements of interpreting and publishing in medical journals, together with rapid changes in science and technology, increasingly demands an understanding of more complex and sophisticated analytic procedures.The text explains the application of statistical models to a wide variety of practical medical investigative studies and clinical trials. Regression methods are used to appropriately answer the
Forecasting with Dynamic Regression Models
Pankratz, Alan
2012-01-01
One of the most widely used tools in statistical forecasting, single equation regression models is examined here. A companion to the author's earlier work, Forecasting with Univariate Box-Jenkins Models: Concepts and Cases, the present text pulls together recent time series ideas and gives special attention to possible intertemporal patterns, distributed lag responses of output to input series and the auto correlation patterns of regression disturbance. It also includes six case studies.
Inferential Models for Linear Regression
Directory of Open Access Journals (Sweden)
Zuoyi Zhang
2011-09-01
Full Text Available Linear regression is arguably one of the most widely used statistical methods in applications. However, important problems, especially variable selection, remain a challenge for classical modes of inference. This paper develops a recently proposed framework of inferential models (IMs in the linear regression context. In general, an IM is able to produce meaningful probabilistic summaries of the statistical evidence for and against assertions about the unknown parameter of interest and, moreover, these summaries are shown to be properly calibrated in a frequentist sense. Here we demonstrate, using simple examples, that the IM framework is promising for linear regression analysis --- including model checking, variable selection, and prediction --- and for uncertain inference in general.
A Matlab program for stepwise regression
Directory of Open Access Journals (Sweden)
Yanhong Qi
2016-03-01
Full Text Available The stepwise linear regression is a multi-variable regression for identifying statistically significant variables in the linear regression equation. In present study, we presented the Matlab program of stepwise regression.
Logistic regression for circular data
Al-Daffaie, Kadhem; Khan, Shahjahan
2017-05-01
This paper considers the relationship between a binary response and a circular predictor. It develops the logistic regression model by employing the linear-circular regression approach. The maximum likelihood method is used to estimate the parameters. The Newton-Raphson numerical method is used to find the estimated values of the parameters. A data set from weather records of Toowoomba city is analysed by the proposed methods. Moreover, a simulation study is considered. The R software is used for all computations and simulations.
Quasi-least squares regression
Shults, Justine
2014-01-01
Drawing on the authors' substantial expertise in modeling longitudinal and clustered data, Quasi-Least Squares Regression provides a thorough treatment of quasi-least squares (QLS) regression-a computational approach for the estimation of correlation parameters within the framework of generalized estimating equations (GEEs). The authors present a detailed evaluation of QLS methodology, demonstrating the advantages of QLS in comparison with alternative methods. They describe how QLS can be used to extend the application of the traditional GEE approach to the analysis of unequally spaced longitu
Biplots in Reduced-Rank Regression
Braak, ter C.J.F.; Looman, C.W.N.
1994-01-01
Regression problems with a number of related response variables are typically analyzed by separate multiple regressions. This paper shows how these regressions can be visualized jointly in a biplot based on reduced-rank regression. Reduced-rank regression combines multiple regression and principal
Growth Regression and Economic Theory
Elbers, Chris; Gunning, Jan Willem
2002-01-01
In this note we show that the standard, loglinear growth regression specificationis consistent with one and only one model in the class of stochastic Ramsey models. Thismodel is highly restrictive: it requires a Cobb-Douglas technology and a 100% depreciationrate and it implies that risk does not
Regression of lumbar disk herniation
Directory of Open Access Journals (Sweden)
G. Yu Evzikov
2015-01-01
Full Text Available Compression of the spinal nerve root, giving rise to pain and sensory and motor disorders in the area of its innervation is the most vivid manifestation of herniated intervertebral disk. Different treatment modalities, including neurosurgery, for evolving these conditions are discussed. There has been recent evidence that spontaneous regression of disk herniation can regress. The paper describes a female patient with large lateralized disc extrusion that has caused compression of the nerve root S1, leading to obvious myotonic and radicular syndrome. Magnetic resonance imaging has shown that the clinical manifestations of discogenic radiculopathy, as well myotonic syndrome and morphological changes completely regressed 8 months later. The likely mechanism is inflammation-induced resorption of a large herniated disk fragment, which agrees with the data available in the literature. A decision to perform neurosurgery for which the patient had indications was made during her first consultation. After regression of discogenic radiculopathy, there was only moderate pain caused by musculoskeletal diseases (facet syndrome, piriformis syndrome that were successfully eliminated by minimally invasive techniques.
Claim reserving with fuzzy regression
Bahrami, Tahereh; BAHRAMI, Masuod
2015-01-01
Abstract. Claims reserving plays a key role for the insurance. Therefore, various statistical methods are used to provide for an adequate amount of claim reserves. Since claim reserves are always variable, fuzzy set theory is used to handle this variability. In this paper, non-symmetric fuzzy regression is integrated in the Taylor’s method to develop a new method for claim reserving.
Multimodality in GARCH regression models
Ooms, M.; Doornik, J.A.
2008-01-01
It is shown empirically that mixed autoregressive moving average regression models with generalized autoregressive conditional heteroskedasticity (Reg-ARMA-GARCH models) can have multimodality in the likelihood that is caused by a dummy variable in the conditional mean. Maximum likelihood estimates
Fungible Weights in Multiple Regression
Waller, Niels G.
2008-01-01
Every set of alternate weights (i.e., nonleast squares weights) in a multiple regression analysis with three or more predictors is associated with an infinite class of weights. All members of a given class can be deemed "fungible" because they yield identical "SSE" (sum of squared errors) and R[superscript 2] values. Equations for generating…
On Weighted Support Vector Regression
DEFF Research Database (Denmark)
Han, Xixuan; Clemmensen, Line Katrine Harder
2014-01-01
We propose a new type of weighted support vector regression (SVR), motivated by modeling local dependencies in time and space in prediction of house prices. The classic weights of the weighted SVR are added to the slack variables in the objective function (OF‐weights). This procedure directly...
PROBIT REGRESSION IN PREDICTION ANALYSIS
African Journals Online (AJOL)
Admin
2008-12-12
Dec 12, 2008 ... GLOBAL JOURNAL OF MATHEMATICAL SCIENCES VOL. ... INTRODUCTION. For some dichotomous variables, the response y is actually a proxy for a variable that is continuous (Newsom, 2005). A regression ... M. E. Nja, Dept. of Mathematics / Statistics Cross River University of Technology, Calabar ...
Ridge Regression for Interactive Models.
Tate, Richard L.
1988-01-01
An exploratory study of the value of ridge regression for interactive models is reported. Assuming that the linear terms in a simple interactive model are centered to eliminate non-essential multicollinearity, a variety of common models, representing both ordinal and disordinal interactions, are shown to have "orientations" that are…
Logistic regression: a brief primer.
Stoltzfus, Jill C
2011-10-01
Regression techniques are versatile in their application to medical research because they can measure associations, predict outcomes, and control for confounding variable effects. As one such technique, logistic regression is an efficient and powerful way to analyze the effect of a group of independent variables on a binary outcome by quantifying each independent variable's unique contribution. Using components of linear regression reflected in the logit scale, logistic regression iteratively identifies the strongest linear combination of variables with the greatest probability of detecting the observed outcome. Important considerations when conducting logistic regression include selecting independent variables, ensuring that relevant assumptions are met, and choosing an appropriate model building strategy. For independent variable selection, one should be guided by such factors as accepted theory, previous empirical investigations, clinical considerations, and univariate statistical analyses, with acknowledgement of potential confounding variables that should be accounted for. Basic assumptions that must be met for logistic regression include independence of errors, linearity in the logit for continuous variables, absence of multicollinearity, and lack of strongly influential outliers. Additionally, there should be an adequate number of events per independent variable to avoid an overfit model, with commonly recommended minimum "rules of thumb" ranging from 10 to 20 events per covariate. Regarding model building strategies, the three general types are direct/standard, sequential/hierarchical, and stepwise/statistical, with each having a different emphasis and purpose. Before reaching definitive conclusions from the results of any of these methods, one should formally quantify the model's internal validity (i.e., replicability within the same data set) and external validity (i.e., generalizability beyond the current sample). The resulting logistic regression model
Hierarchical Adaptive Regression Kernels for Regression with Functional Predictors.
Woodard, Dawn B; Crainiceanu, Ciprian; Ruppert, David
2013-01-01
We propose a new method for regression using a parsimonious and scientifically interpretable representation of functional predictors. Our approach is designed for data that exhibit features such as spikes, dips, and plateaus whose frequency, location, size, and shape varies stochastically across subjects. We propose Bayesian inference of the joint functional and exposure models, and give a method for efficient computation. We contrast our approach with existing state-of-the-art methods for regression with functional predictors, and show that our method is more effective and efficient for data that include features occurring at varying locations. We apply our methodology to a large and complex dataset from the Sleep Heart Health Study, to quantify the association between sleep characteristics and health outcomes. Software and technical appendices are provided in online supplemental materials.
Hierarchical Adaptive Regression Kernels for Regression with Functional Predictors
Woodard, Dawn B.; Crainiceanu, Ciprian; Ruppert, David
2013-01-01
We propose a new method for regression using a parsimonious and scientifically interpretable representation of functional predictors. Our approach is designed for data that exhibit features such as spikes, dips, and plateaus whose frequency, location, size, and shape varies stochastically across subjects. We propose Bayesian inference of the joint functional and exposure models, and give a method for efficient computation. We contrast our approach with existing state-of-the-art methods for re...
Active set support vector regression.
Musicant, David R; Feinberg, Alexander
2004-03-01
This paper presents active set support vector regression (ASVR), a new active set strategy to solve a straightforward reformulation of the standard support vector regression problem. This new algorithm is based on the successful ASVM algorithm for classification problems, and consists of solving a finite number of linear equations with a typically large dimensionality equal to the number of points to be approximated. However, by making use of the Sherman-Morrison-Woodbury formula, a much smaller matrix of the order of the original input space is inverted at each step. The algorithm requires no specialized quadratic or linear programming code, but merely a linear equation solver which is publicly available. ASVR is extremely fast, produces comparable generalization error to other popular algorithms, and is available on the web for download.
AUTISTIC EPILEPTIFORM REGRESSION (A REVIEW
Directory of Open Access Journals (Sweden)
L. Yu. Glukhova
2012-01-01
Full Text Available The author represents the review of current scientific literature devoted to autistic epileptiform regression — the special form of autistic disorder, characterized by development of severe communicative disorders in children as a result of continuous prolonged epileptiform activity on EEG. This condition has been described by R.F. Tuchman and I. Rapin in 1997. The author describes the aspects of pathogenesis, clinical pictures and diagnostics of this disorder, including the peculiar anomalies on EEG (benign epileptiform patterns of childhood, with a high index of epileptiform activity, especially in the sleep. The especial attention is given to approaches to the treatment of autistic epileptiform regression. Efficacy of valproates, corticosteroid hormones and antiepileptic drugs of other groups is considered.
Binary data regression: Weibull distribution
Caron, Renault; Polpo, Adriano
2009-12-01
The problem of estimation in binary response data has receivied a great number of alternative statistical solutions. Generalized linear models allow for a wide range of statistical models for regression data. The most used model is the logistic regression, see Hosmer et al. [6]. However, as Chen et al. [5] mentions, when the probability of a given binary response approaches 0 at a different rate than it approaches 1, symmetric linkages are inappropriate. A class of models based on Weibull distribution indexed by three parameters is introduced here. Maximum likelihood methods are employed to estimate the parameters. The objective of the present paper is to show a solution for the estimation problem under the Weibull model. An example showing the quality of the model is illustrated by comparing it with the alternative probit and logit models.
Spontaneous regression of colon cancer.
Kihara, Kyoichi; Fujita, Shin; Ohshiro, Taihei; Yamamoto, Seiichiro; Sekine, Shigeki
2015-01-01
A case of spontaneous regression of transverse colon cancer is reported. A 64-year-old man was diagnosed as having cancer of the transverse colon at a local hospital. Initial and second colonoscopy examinations revealed a typical cancer of the transverse colon, which was diagnosed as moderately differentiated adenocarcinoma. The patient underwent right hemicolectomy 6 weeks after the initial colonoscopy. The resected specimen showed only a scar at the tumor site, and no cancerous tissue was proven histologically. The patient is alive with no evidence of recurrence 1 year after surgery. Although an antitumor immune response is the most likely explanation, the exact nature of the phenomenon was unclear. We describe this rare case and review the literature pertaining to spontaneous regression of colorectal cancer. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
Polynomial Regressions and Nonsense Inference
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Daniel Ventosa-Santaulària
2013-11-01
Full Text Available Polynomial specifications are widely used, not only in applied economics, but also in epidemiology, physics, political analysis and psychology, just to mention a few examples. In many cases, the data employed to estimate such specifications are time series that may exhibit stochastic nonstationary behavior. We extend Phillips’ results (Phillips, P. Understanding spurious regressions in econometrics. J. Econom. 1986, 33, 311–340. by proving that an inference drawn from polynomial specifications, under stochastic nonstationarity, is misleading unless the variables cointegrate. We use a generalized polynomial specification as a vehicle to study its asymptotic and finite-sample properties. Our results, therefore, lead to a call to be cautious whenever practitioners estimate polynomial regressions.
Quantile Regression With Measurement Error
Wei, Ying
2009-08-27
Regression quantiles can be substantially biased when the covariates are measured with error. In this paper we propose a new method that produces consistent linear quantile estimation in the presence of covariate measurement error. The method corrects the measurement error induced bias by constructing joint estimating equations that simultaneously hold for all the quantile levels. An iterative EM-type estimation algorithm to obtain the solutions to such joint estimation equations is provided. The finite sample performance of the proposed method is investigated in a simulation study, and compared to the standard regression calibration approach. Finally, we apply our methodology to part of the National Collaborative Perinatal Project growth data, a longitudinal study with an unusual measurement error structure. © 2009 American Statistical Association.
Directional quantile regression in R
Czech Academy of Sciences Publication Activity Database
Boček, Pavel; Šiman, Miroslav
2017-01-01
Roč. 53, č. 3 (2017), s. 480-492 ISSN 0023-5954 R&D Projects: GA ČR GA14-07234S Institutional support: RVO:67985556 Keywords : multivariate quantile * regression quantile * halfspace depth * depth contour Subject RIV: BD - Theory of Information Impact factor: 0.379, year: 2016 http:// library .utia.cas.cz/separaty/2017/SI/bocek-0476587.pdf
QUANTILE CALCULUS AND CENSORED REGRESSION.
Huang, Yijian
2010-06-01
Quantile regression has been advocated in survival analysis to assess evolving covariate effects. However, challenges arise when the censoring time is not always observed and may be covariate-dependent, particularly in the presence of continuously-distributed covariates. In spite of several recent advances, existing methods either involve algorithmic complications or impose a probability grid. The former leads to difficulties in the implementation and asymptotics, whereas the latter introduces undesirable grid dependence. To resolve these issues, we develop fundamental and general quantile calculus on cumulative probability scale in this article, upon recognizing that probability and time scales do not always have a one-to-one mapping given a survival distribution. These results give rise to a novel estimation procedure for censored quantile regression, based on estimating integral equations. A numerically reliable and efficient Progressive Localized Minimization (PLMIN) algorithm is proposed for the computation. This procedure reduces exactly to the Kaplan-Meier method in the k-sample problem, and to standard uncensored quantile regression in the absence of censoring. Under regularity conditions, the proposed quantile coefficient estimator is uniformly consistent and converges weakly to a Gaussian process. Simulations show good statistical and algorithmic performance. The proposal is illustrated in the application to a clinical study.
Gaussian Process Regression Model in Spatial Logistic Regression
Sofro, A.; Oktaviarina, A.
2018-01-01
Spatial analysis has developed very quickly in the last decade. One of the favorite approaches is based on the neighbourhood of the region. Unfortunately, there are some limitations such as difficulty in prediction. Therefore, we offer Gaussian process regression (GPR) to accommodate the issue. In this paper, we will focus on spatial modeling with GPR for binomial data with logit link function. The performance of the model will be investigated. We will discuss the inference of how to estimate the parameters and hyper-parameters and to predict as well. Furthermore, simulation studies will be explained in the last section.
Producing The New Regressive Left
DEFF Research Database (Denmark)
Crone, Christine
to be a committed artist, and how that translates into supporting al-Assad’s rule in Syria; the Ramadan programme Harrir Aqlak’s attempt to relaunch an intellectual renaissance and to promote religious pluralism; and finally, al-Mayadeen’s cooperation with the pan-Latin American TV station TeleSur and its ambitions...... becomes clear from the analytical chapters is the emergence of the new cross-ideological alliance of The New Regressive Left. This emerging coalition between Shia Muslims, religious minorities, parts of the Arab Left, secular cultural producers, and the remnants of the political,strategic resistance...
An Evaluation of Ridge Regression.
1981-12-01
of the parameter estimates, is a decreasing function of k. The idea of ridge regression, as suggested by Hoerl and Kennard (Ref 12:58-63), is to pick...CROSS? 0 CR0553 f.812 CR0554 0 CR0555 4.39? CROSS6 0 ALSO 4.922 KSO 0 NVARSO 4. A5059 .622 CONTFNTS OF CASE NUlIPER 209 SEQHUI 209. SUOILE PEGANAL CASWGT...KSQ .000 NVARSO 9. RSOSO .846 CONTENTS OF CASE NUMBER 55 SEONUN 55. SUfTFILE PEGANAL CASWGI 2.0000 459 .970 RI 76600 K .025 NVA? 3. MSE .177 NS[IS
Energy Technology Data Exchange (ETDEWEB)
Ferry, S.; Dromart, G. (Univ. of Lyon (France))
1991-03-01
From the several tens of depositional sequences that can be platform-to-basin traced in the Mesozoic of the Vocontian Trough and nearby platforms, the following rules may be set: (1) there are two basic systems of gravity deposits - a regressive one and a transgressive one - but unequally developed depending on sequences; (2) thick bundles of bioclastic turbidites, tied to parasequence channels and representing 'shingled turbidites,' are emplaced mainly at the basis of lowstand systems tracts, but may last the whole low stand; the complex organization of siliciclastic fans is not found; (3) debris-flow deposits, as a result of catastrophic margin collapses, are almost always within transgressive systems tracts; (4) slumps deposits are scattered throughout when frequent; when scarce, they are mainly within transgressive systems tracts, and replace debris flow deposits; (5) Upper Jurassic to Berriasian 'resedimentation breccias,' a peculiar type of gravity deposits, are emplaced at both rises and falls in relative sea level, and cannot be used as reliable markers of sequence boundaries; and (6) both transgressive and regressive gravity systems are more developed during second order lowstands in sea level marked by strong carbonate platform progradation. As a whole, third order transgressive gravity systems are often more developed than regressive ones. Comparisons with siliciclastic depositional systems suggest that sandstone turbidites could be transgressive systems, as a result of stronger parasequential ( glacio-eustatic) high-frequency oscillations during third order rises in relative sea level.
Varying-coefficient functional linear regression
Wu, Yichao; Fan, Jianqing; Müller, Hans-Georg
2010-01-01
Functional linear regression analysis aims to model regression relations which include a functional predictor. The analog of the regression parameter vector or matrix in conventional multivariate or multiple-response linear regression models is a regression parameter function in one or two arguments. If, in addition, one has scalar predictors, as is often the case in applications to longitudinal studies, the question arises how to incorporate these into a functional regression model. We study...
Nonparametric Regression with Common Shocks
Directory of Open Access Journals (Sweden)
Eduardo A. Souza-Rodrigues
2016-09-01
Full Text Available This paper considers a nonparametric regression model for cross-sectional data in the presence of common shocks. Common shocks are allowed to be very general in nature; they do not need to be finite dimensional with a known (small number of factors. I investigate the properties of the Nadaraya-Watson kernel estimator and determine how general the common shocks can be while still obtaining meaningful kernel estimates. Restrictions on the common shocks are necessary because kernel estimators typically manipulate conditional densities, and conditional densities do not necessarily exist in the present case. By appealing to disintegration theory, I provide sufficient conditions for the existence of such conditional densities and show that the estimator converges in probability to the Kolmogorov conditional expectation given the sigma-field generated by the common shocks. I also establish the rate of convergence and the asymptotic distribution of the kernel estimator.
Practical Session: Multiple Linear Regression
Clausel, M.; Grégoire, G.
2014-12-01
Three exercises are proposed to illustrate the simple linear regression. In the first one investigates the influence of several factors on atmospheric pollution. It has been proposed by D. Chessel and A.B. Dufour in Lyon 1 (see Sect. 6 of http://pbil.univ-lyon1.fr/R/pdf/tdr33.pdf) and is based on data coming from 20 cities of U.S. Exercise 2 is an introduction to model selection whereas Exercise 3 provides a first example of analysis of variance. Exercises 2 and 3 have been proposed by A. Dalalyan at ENPC (see Exercises 2 and 3 of http://certis.enpc.fr/~dalalyan/Download/TP_ENPC_5.pdf).
Kernel Multitask Regression for Toxicogenetics.
Bernard, Elsa; Jiao, Yunlong; Scornet, Erwan; Stoven, Veronique; Walter, Thomas; Vert, Jean-Philippe
2017-10-01
The development of high-throughput in vitro assays to study quantitatively the toxicity of chemical compounds on genetically characterized human-derived cell lines paves the way to predictive toxicogenetics, where one would be able to predict the toxicity of any particular compound on any particular individual. In this paper we present a machine learning-based approach for that purpose, kernel multitask regression (KMR), which combines chemical characterizations of molecular compounds with genetic and transcriptomic characterizations of cell lines to predict the toxicity of a given compound on a given cell line. We demonstrate the relevance of the method on the recent DREAM8 Toxicogenetics challenge, where it ranked among the best state-of-the-art models, and discuss the importance of choosing good descriptors for cell lines and chemicals. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.
Lumbar herniated disc: spontaneous regression.
Altun, Idiris; Yüksel, Kasım Zafer
2017-01-01
Low back pain is a frequent condition that results in substantial disability and causes admission of patients to neurosurgery clinics. To evaluate and present the therapeutic outcomes in lumbar disc hernia (LDH) patients treated by means of a conservative approach, consisting of bed rest and medical therapy. This retrospective cohort was carried out in the neurosurgery departments of hospitals in Kahramanmaraş city and 23 patients diagnosed with LDH at the levels of L3-L4, L4-L5 or L5-S1 were enrolled. The average age was 38.4 ± 8.0 and the chief complaint was low back pain and sciatica radiating to one or both lower extremities. Conservative treatment was administered. Neurological examination findings, durations of treatment and intervals until symptomatic recovery were recorded. Laségue tests and neurosensory examination revealed that mild neurological deficits existed in 16 of our patients. Previously, 5 patients had received physiotherapy and 7 patients had been on medical treatment. The number of patients with LDH at the level of L3-L4, L4-L5, and L5-S1 were 1, 13, and 9, respectively. All patients reported that they had benefit from medical treatment and bed rest, and radiologic improvement was observed simultaneously on MRI scans. The average duration until symptomatic recovery and/or regression of LDH symptoms was 13.6 ± 5.4 months (range: 5-22). It should be kept in mind that lumbar disc hernias could regress with medical treatment and rest without surgery, and there should be an awareness that these patients could recover radiologically. This condition must be taken into account during decision making for surgical intervention in LDH patients devoid of indications for emergent surgery.
Energy Technology Data Exchange (ETDEWEB)
Whitehouse, M.J. (Univ. of Oxford (England))
1989-03-01
Two end-member types of U-Pb fractionation behavior are recognized: type I in which Pb-isotopic homogenization occurs, and type II, in which Pb-isotopic homogenization is incomplete or absent. Type II can be further subdivided into three categories depending upon the relationship of the post-fractionation {mu}{sub 3} ({sup 238}U/{sup 204}Pb) value to prefractionation {mu}{sub 2}. In type IIa, {mu}{sub 3} is unrelated to {mu}{sub 2}; in type IIb, {mu}{sub 3} = k {mu}{sub 2}, where k is a constant; in type IIc, {mu}{sub 3} values are uniform. Each of these types of behavior results in a predictable effect upon observed uranogenic Pb-isotope systematics which may lead to an incorrect geochronological interpretation. An example of the relationship between these specific types of U-Pb behavior and metamorphic grade is provided from the late-Archaean Lewisian complex of N.W. Scotland. This complex was metamorphosed during the {approximately}2,660 Ma Badcallian event. A consideration of the thorogenic Pb-isotopic data reveals a systematic variation during prograde metamorphism. An attempt to test the model of 2,660 Ma initial ratio inhomogeneity by analysis of K-feldspars from the amphibolite facies gneiss suite reveals {approximately}1,700 Ma Laxfordian resetting of their Pb-isotope systematics. This study has implications for Pb/Pb geochronological data particularly in lower grade metamorphic terrains. It suggests that reliable isochrons may only be generated by type I behavior, or by the imposition of high {mu}{sub 3} values during type IIa behavior at higher grades of metamorphism.
Inconsistency Between Univariate and Multiple Logistic Regressions
WANG, HONGYUE; Peng, Jing; Wang, Bokai; Lu, Xiang; ZHENG, Julia Z.; Wang, Kejia; Tu, Xin M.; Feng, Changyong
2017-01-01
Summary Logistic regression is a popular statistical method in studying the effects of covariates on binary outcomes. It has been widely used in both clinical trials and observational studies. However, the results from the univariate regression and from the multiple logistic regression tend to be conflicting. A covariate may show very strong effect on the outcome in the multiple regression but not in the univariate regression, and vice versa. These facts have not been well appreciated in biom...
Insulin resistance: regression and clustering.
Directory of Open Access Journals (Sweden)
Sangho Yoon
Full Text Available In this paper we try to define insulin resistance (IR precisely for a group of Chinese women. Our definition deliberately does not depend upon body mass index (BMI or age, although in other studies, with particular random effects models quite different from models used here, BMI accounts for a large part of the variability in IR. We accomplish our goal through application of Gauss mixture vector quantization (GMVQ, a technique for clustering that was developed for application to lossy data compression. Defining data come from measurements that play major roles in medical practice. A precise statement of what the data are is in Section 1. Their family structures are described in detail. They concern levels of lipids and the results of an oral glucose tolerance test (OGTT. We apply GMVQ to residuals obtained from regressions of outcomes of an OGTT and lipids on functions of age and BMI that are inferred from the data. A bootstrap procedure developed for our family data supplemented by insights from other approaches leads us to believe that two clusters are appropriate for defining IR precisely. One cluster consists of women who are IR, and the other of women who seem not to be. Genes and other features are used to predict cluster membership. We argue that prediction with "main effects" is not satisfactory, but prediction that includes interactions may be.
Knowledge and Awareness: Linear Regression
Directory of Open Access Journals (Sweden)
Monika Raghuvanshi
2016-12-01
Full Text Available Knowledge and awareness are factors guiding development of an individual. These may seem simple and practicable, but in reality a proper combination of these is a complex task. Economically driven state of development in younger generations is an impediment to the correct manner of development. As youths are at the learning phase, they can be molded to follow a correct lifestyle. Awareness and knowledge are important components of any formal or informal environmental education. The purpose of this study is to evaluate the relationship of these components among students of secondary/ senior secondary schools who have undergone a formal study of environment in their curricula. A suitable instrument is developed in order to measure the elements of Awareness and Knowledge among the participants of the study. Data was collected from various secondary and senior secondary school students in the age group 14 to 20 years using cluster sampling technique from the city of Bikaner, India. Linear regression analysis was performed using IBM SPSS 23 statistical tool. There exists a weak relation between knowledge and awareness about environmental issues, caused due to routine practices mishandling; hence one component can be complemented by other for improvement in both. Knowledge and awareness are crucial factors and can provide huge opportunities in any field. Resource utilization for economic solutions may pave the way for eco-friendly products and practices. If green practices are inculcated at the learning phase, they may become normal routine. This will also help in repletion of the environment.
Estimating equivalence with quantile regression
Cade, B.S.
2011-01-01
Equivalence testing and corresponding confidence interval estimates are used to provide more enlightened statistical statements about parameter estimates by relating them to intervals of effect sizes deemed to be of scientific or practical importance rather than just to an effect size of zero. Equivalence tests and confidence interval estimates are based on a null hypothesis that a parameter estimate is either outside (inequivalence hypothesis) or inside (equivalence hypothesis) an equivalence region, depending on the question of interest and assignment of risk. The former approach, often referred to as bioequivalence testing, is often used in regulatory settings because it reverses the burden of proof compared to a standard test of significance, following a precautionary principle for environmental protection. Unfortunately, many applications of equivalence testing focus on establishing average equivalence by estimating differences in means of distributions that do not have homogeneous variances. I discuss how to compare equivalence across quantiles of distributions using confidence intervals on quantile regression estimates that detect differences in heterogeneous distributions missed by focusing on means. I used one-tailed confidence intervals based on inequivalence hypotheses in a two-group treatment-control design for estimating bioequivalence of arsenic concentrations in soils at an old ammunition testing site and bioequivalence of vegetation biomass at a reclaimed mining site. Two-tailed confidence intervals based both on inequivalence and equivalence hypotheses were used to examine quantile equivalence for negligible trends over time for a continuous exponential model of amphibian abundance. ?? 2011 by the Ecological Society of America.
Principal component regression analysis with SPSS.
Liu, R X; Kuang, J; Gong, Q; Hou, X L
2003-06-01
The paper introduces all indices of multicollinearity diagnoses, the basic principle of principal component regression and determination of 'best' equation method. The paper uses an example to describe how to do principal component regression analysis with SPSS 10.0: including all calculating processes of the principal component regression and all operations of linear regression, factor analysis, descriptives, compute variable and bivariate correlations procedures in SPSS 10.0. The principal component regression analysis can be used to overcome disturbance of the multicollinearity. The simplified, speeded up and accurate statistical effect is reached through the principal component regression analysis with SPSS.
A Spreadsheet Model for Teaching Regression Analysis.
Wood, William C.; O'Hare, Sharon L.
1992-01-01
Presents a spreadsheet model that is useful in introducing students to regression analysis and the computation of regression coefficients. Includes spreadsheet layouts and formulas so that the spreadsheet can be implemented. (Author)
Complete regression of primary malignant melanoma.
Emanuel, Patrick O; Mannion, Meghan; Phelps, Robert G
2008-04-01
Over the years, histopathologic studies to determine the nature and significance of regression in malignant melanoma have yielded different results. At least in part, this most likely reflects differences in the definition of what constitutes regression. Although partial regression is relatively common, complete regression is rare. It has been said that complete regression of a primary lesion is associated with metastatic disease, but the evidence for this is largely anecdotal-the literature contains only case reports and small series. We found 2 cases of complete regression in our dermatopathology database. Metastatic disease was identified in both cases; in 1 case, the suspicion of melanoma was raised on the initial biopsy and subsequent workup revealed lymph node metastasis. These cases illustrate the histologic features of a completely regressed primary melanoma and add credence to the theory that completely regressed melanoma is associated with a poor outcome.
Unbalanced Regressions and the Predictive Equation
DEFF Research Database (Denmark)
Osterrieder, Daniela; Ventosa-Santaulària, Daniel; Vera-Valdés, J. Eduardo
Predictive return regressions with persistent regressors are typically plagued by (asymptotically) biased/inconsistent estimates of the slope, non-standard or potentially even spurious statistical inference, and regression unbalancedness. We alleviate the problem of unbalancedness in the theoreti...
Semiparametric regression during 2003–2007
Ruppert, David
2009-01-01
Semiparametric regression is a fusion between parametric regression and nonparametric regression that integrates low-rank penalized splines, mixed model and hierarchical Bayesian methodology – thus allowing more streamlined handling of longitudinal and spatial correlation. We review progress in the field over the five-year period between 2003 and 2007. We find semiparametric regression to be a vibrant field with substantial involvement and activity, continual enhancement and widespread application.
Regression with Sparse Approximations of Data
DEFF Research Database (Denmark)
Noorzad, Pardis; Sturm, Bob L.
2012-01-01
We propose sparse approximation weighted regression (SPARROW), a method for local estimation of the regression function that uses sparse approximation with a dictionary of measurements. SPARROW estimates the regression function at a point with a linear combination of a few regressands selected by...... on the sparse approximation process. Our experimental results show the locally constant form of SPARROW performs competitively....
Regression Analysis by Example. 5th Edition
Chatterjee, Samprit; Hadi, Ali S.
2012-01-01
Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgment. "Regression Analysis by Example, Fifth Edition" has been expanded and thoroughly…
Standards for Standardized Logistic Regression Coefficients
Menard, Scott
2011-01-01
Standardized coefficients in logistic regression analysis have the same utility as standardized coefficients in linear regression analysis. Although there has been no consensus on the best way to construct standardized logistic regression coefficients, there is now sufficient evidence to suggest a single best approach to the construction of a…
Fully Regressive Melanoma: A Case Without Metastasis.
Ehrsam, Eric; Kallini, Joseph R; Lebas, Damien; Khachemoune, Amor; Modiano, Philippe; Cotten, Hervé
2016-08-01
Fully regressive melanoma is a phenomenon in which the primary cutaneous melanoma becomes completely replaced by fibrotic components as a result of host immune response. Although 10 to 35 percent of cases of cutaneous melanomas may partially regress, fully regressive melanoma is very rare; only 47 cases have been reported in the literature to date. AH of the cases of fully regressive melanoma reported in the literature were diagnosed in conjunction with metastasis on a patient. The authors describe a case of fully regressive melanoma without any metastases at the time of its diagnosis. Characteristic findings on dermoscopy, as well as the absence of melanoma on final biopsy, confirmed the diagnosis.
Spontaneous Regression of Lumbar Herniated Disc
Directory of Open Access Journals (Sweden)
Chun-Wei Chang
2009-12-01
Full Text Available Intervertebral disc herniation of the lumbar spine is a common disease presenting with low back pain and involving nerve root radiculopathy. Some neurological symptoms in the majority of patients frequently improve after a period of conservative treatment. This has been regarded as the result of a decrease of pressure exerted from the herniated disc on neighboring neurostructures and a gradual regression of inflammation. Recently, with advances in magnetic resonance imaging, many reports have demonstrated that the herniated disc has the potential for spontaneous regression. Regression coincided with the improvement of associated symptoms. However, the exact regression mechanism remains unclear. Here, we present 2 cases of lumbar intervertebral disc herniation with spontaneous regression. We review the literature and discuss the possible mechanisms, the precipitating factors of spontaneous disc regression and the proper timing of surgical intervention.
Applied regression analysis a research tool
Pantula, Sastry; Dickey, David
1998-01-01
Least squares estimation, when used appropriately, is a powerful research tool. A deeper understanding of the regression concepts is essential for achieving optimal benefits from a least squares analysis. This book builds on the fundamentals of statistical methods and provides appropriate concepts that will allow a scientist to use least squares as an effective research tool. Applied Regression Analysis is aimed at the scientist who wishes to gain a working knowledge of regression analysis. The basic purpose of this book is to develop an understanding of least squares and related statistical methods without becoming excessively mathematical. It is the outgrowth of more than 30 years of consulting experience with scientists and many years of teaching an applied regression course to graduate students. Applied Regression Analysis serves as an excellent text for a service course on regression for non-statisticians and as a reference for researchers. It also provides a bridge between a two-semester introduction to...
Three contributions to robust regression diagnostics
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Kalina J.
2015-12-01
Full Text Available Robust regression methods have been developed not only as a diagnostic tool for standard least squares estimation in statistical and econometric applications, but can be also used as self-standing regression estimation procedures. Therefore, they need to be equipped by their own diagnostic tools. This paper is devoted to robust regression and presents three contributions to its diagnostic tools or estimating regression parameters under non-standard conditions. Firstly, we derive the Durbin-Watson test of independence of random regression errors for the regression median. The approach is based on the approximation to the exact null distribution of the test statistic. Secondly, we accompany the least trimmed squares estimator by a subjective criterion for selecting a suitable value of the trimming constant. Thirdly, we propose a robust version of the instrumental variables estimator. The new methods are illustrated on examples with real data and their advantages and limitations are discussed.
Regression techniques for Portfolio Optimisation using MOSEK
Schmelzer, Thomas; Hauser, Raphael; Andersen, Erling; Dahl, Joachim
2013-01-01
Regression is widely used by practioners across many disciplines. We reformulate the underlying optimisation problem as a second-order conic program providing the flexibility often needed in applications. Using examples from portfolio management and quantitative trading we solve regression problems with and without constraints. Several Python code fragments are given. The code and data are available online at http://www.github.com/tschm/MosekRegression.
Bulcock, J. W.
The problem of model estimation when the data are collinear was examined. Though the ridge regression (RR) outperforms ordinary least squares (OLS) regression in the presence of acute multicollinearity, it is not a problem free technique for reducing the variance of the estimates. It is a stochastic procedure when it should be nonstochastic and it…
New ridge parameters for ridge regression
Directory of Open Access Journals (Sweden)
A.V. Dorugade
2014-04-01
Full Text Available Hoerl and Kennard (1970a introduced the ridge regression estimator as an alternative to the ordinary least squares (OLS estimator in the presence of multicollinearity. In ridge regression, ridge parameter plays an important role in parameter estimation. In this article, a new method for estimating ridge parameters in both situations of ordinary ridge regression (ORR and generalized ridge regression (GRR is proposed. The simulation study evaluates the performance of the proposed estimator based on the mean squared error (MSE criterion and indicates that under certain conditions the proposed estimators perform well compared to OLS and other well-known estimators reviewed in this article.
The regress problem : Metatheory, development, and criticism
Peijnenburg, Jeanne; Aikin, Scott
This introduction presents selected proceedings of a two-day meeting on the regress problem, sponsored by the Netherlands Organization for Scientific Research (NWO) and hosted by Vanderbilt University in October 2013, along with other submitted essays. Three forms of research on the regress problem
A Simulation Investigation of Principal Component Regression.
Allen, David E.
Regression analysis is one of the more common analytic tools used by researchers. However, multicollinearity between the predictor variables can cause problems in using the results of regression analyses. Problems associated with multicollinearity include entanglement of relative influences of variables due to reduced precision of estimation,…
Regression Analysis and the Sociological Imagination
De Maio, Fernando
2014-01-01
Regression analysis is an important aspect of most introductory statistics courses in sociology but is often presented in contexts divorced from the central concerns that bring students into the discipline. Consequently, we present five lesson ideas that emerge from a regression analysis of income inequality and mortality in the USA and Canada.
Regression Analysis: Legal Applications in Institutional Research
Frizell, Julie A.; Shippen, Benjamin S., Jr.; Luna, Andrew L.
2008-01-01
This article reviews multiple regression analysis, describes how its results should be interpreted, and instructs institutional researchers on how to conduct such analyses using an example focused on faculty pay equity between men and women. The use of multiple regression analysis will be presented as a method with which to compare salaries of…
Variable importance in latent variable regression models
Kvalheim, O.M.; Arneberg, R.; Bleie, O.; Rajalahti, T.; Smilde, A.K.; Westerhuis, J.A.
2014-01-01
The quality and practical usefulness of a regression model are a function of both interpretability and prediction performance. This work presents some new graphical tools for improved interpretation of latent variable regression models that can also assist in improved algorithms for variable
An identity for kernel ridge regression
Zhdanov, Fedor; Kalnishkan, Yuri
2013-01-01
This paper derives an identity connecting the square loss of ridge regression in on-line mode with the loss of the retrospectively best regressor. Some corollaries about the properties of the cumulative loss of on-line ridge regression are also obtained.
ON REGRESSION REPRESENTATIONS OF STOCHASTIC-PROCESSES
RUSCHENDORF, L; DEVALK, [No Value
We construct a.s. nonlinear regression representations of general stochastic processes (X(n))n is-an-element-of N. As a consequence we obtain in particular special regression representations of Markov chains and of certain m-dependent sequences. For m-dependent sequences we obtain a constructive
Pathological assessment of liver fibrosis regression
Directory of Open Access Journals (Sweden)
WANG Bingqiong
2017-03-01
Full Text Available Hepatic fibrosis is the common pathological outcome of chronic hepatic diseases. An accurate assessment of fibrosis degree provides an important reference for a definite diagnosis of diseases, treatment decision-making, treatment outcome monitoring, and prognostic evaluation. At present, many clinical studies have proven that regression of hepatic fibrosis and early-stage liver cirrhosis can be achieved by effective treatment, and a correct evaluation of fibrosis regression has become a hot topic in clinical research. Liver biopsy has long been regarded as the gold standard for the assessment of hepatic fibrosis, and thus it plays an important role in the evaluation of fibrosis regression. This article reviews the clinical application of current pathological staging systems in the evaluation of fibrosis regression from the perspectives of semi-quantitative scoring system, quantitative approach, and qualitative approach, in order to propose a better pathological evaluation system for the assessment of fibrosis regression.
Regression Estimator Using Double Ranked Set Sampling
Directory of Open Access Journals (Sweden)
Hani M. Samawi
2002-06-01
Full Text Available The performance of a regression estimator based on the double ranked set sample (DRSS scheme, introduced by Al-Saleh and Al-Kadiri (2000, is investigated when the mean of the auxiliary variable X is unknown. Our primary analysis and simulation indicates that using the DRSS regression estimator for estimating the population mean substantially increases relative efficiency compared to using regression estimator based on simple random sampling (SRS or ranked set sampling (RSS (Yu and Lam, 1997 regression estimator. Moreover, the regression estimator using DRSS is also more efficient than the naïve estimators of the population mean using SRS, RSS (when the correlation coefficient is at least 0.4 and DRSS for high correlation coefficient (at least 0.91. The theory is illustrated using a real data set of trees.
Investigating bias in squared regression structure coefficients.
Nimon, Kim F; Zientek, Linda R; Thompson, Bruce
2015-01-01
The importance of structure coefficients and analogs of regression weights for analysis within the general linear model (GLM) has been well-documented. The purpose of this study was to investigate bias in squared structure coefficients in the context of multiple regression and to determine if a formula that had been shown to correct for bias in squared Pearson correlation coefficients and coefficients of determination could be used to correct for bias in squared regression structure coefficients. Using data from a Monte Carlo simulation, this study found that squared regression structure coefficients corrected with Pratt's formula produced less biased estimates and might be more accurate and stable estimates of population squared regression structure coefficients than estimates with no such corrections. While our findings are in line with prior literature that identified multicollinearity as a predictor of bias in squared regression structure coefficients but not coefficients of determination, the findings from this study are unique in that the level of predictive power, number of predictors, and sample size were also observed to contribute bias in squared regression structure coefficients.
Regression of altitude-produced cardiac hypertrophy.
Sizemore, D. A.; Mcintyre, T. W.; Van Liere, E. J.; Wilson , M. F.
1973-01-01
The rate of regression of cardiac hypertrophy with time has been determined in adult male albino rats. The hypertrophy was induced by intermittent exposure to simulated high altitude. The percentage hypertrophy was much greater (46%) in the right ventricle than in the left (16%). The regression could be adequately fitted to a single exponential function with a half-time of 6.73 plus or minus 0.71 days (90% CI). There was no significant difference in the rates of regression for the two ventricles.
Competing Risks Quantile Regression at Work
DEFF Research Database (Denmark)
Dlugosz, Stephan; Lo, Simon M. S.; Wilke, Ralf
2017-01-01
Despite its emergence as a frequently used method for the empirical analysis of multivariate data, quantile regression is yet to become a mainstream tool for the analysis of duration data. We present a pioneering empirical study on the grounds of a competing risks quantile regression model. We us...... into the distribution of transitions out of maternity leave. It is found that cumulative incidences implied by the quantile regression model differ from those implied by a proportional hazards model. To foster the use of the model, we make an R-package (cmprskQR) available....
Particle Swarm Optimization and Regression Analysis II
Mohanty, Soumya D.
2012-10-01
In the first part of this article, Particle Swarm Optimization (PSO) was applied to the problem of optimizing knot placement in the regression spline method. Although promising for broadband signals having smooth, but otherwise unknown, waveforms, this simple approach fails in the case of narrowband signals when the carrier frequency as well as the amplitude and phase modulations are unknown. A method is presented that addresses this challenge by using PSO based regression splines for the in-phase and quadrature amplitudes separately. It is thereby seen that PSO is an effective tool for regression analysis of a broad class of signals.
Applied Regression Modeling A Business Approach
Pardoe, Iain
2012-01-01
An applied and concise treatment of statistical regression techniques for business students and professionals who have little or no background in calculusRegression analysis is an invaluable statistical methodology in business settings and is vital to model the relationship between a response variable and one or more predictor variables, as well as the prediction of a response value given values of the predictors. In view of the inherent uncertainty of business processes, such as the volatility of consumer spending and the presence of market uncertainty, business professionals use regression a
Model checking for ROC regression analysis.
Cai, Tianxi; Zheng, Yingye
2007-03-01
The receiver operating characteristic (ROC) curve is a prominent tool for characterizing the accuracy of a continuous diagnostic test. To account for factors that might influence the test accuracy, various ROC regression methods have been proposed. However, as in any regression analysis, when the assumed models do not fit the data well, these methods may render invalid and misleading results. To date, practical model-checking techniques suitable for validating existing ROC regression models are not yet available. In this article, we develop cumulative residual-based procedures to graphically and numerically assess the goodness of fit for some commonly used ROC regression models, and show how specific components of these models can be examined within this framework. We derive asymptotic null distributions for the residual processes and discuss resampling procedures to approximate these distributions in practice. We illustrate our methods with a dataset from the cystic fibrosis registry.
Weighted regression analysis and interval estimators
Donald W. Seegrist
1974-01-01
A method for deriving the weighted least squares estimators for the parameters of a multiple regression model. Confidence intervals for expected values, and prediction intervals for the means of future samples are given.
Multiple Instance Regression with Structured Data
Wagstaff, Kiri L.; Lane, Terran; Roper, Alex
2008-01-01
This slide presentation reviews the use of multiple instance regression with structured data from multiple and related data sets. It applies the concept to a practical problem, that of estimating crop yield using remote sensed country wide weekly observations.
Patterns of Regression in Rett Syndrome
Directory of Open Access Journals (Sweden)
J Gordon Millichap
2002-10-01
Full Text Available Patterns and features of regression in a case series of 53 girls and women with Rett syndrome were studied at the Institute of Child Health and Great Ormond Street Children’s Hospital, London, UK.
Dynamic travel time estimation using regression trees.
2008-10-01
This report presents a methodology for travel time estimation by using regression trees. The dissemination of travel time information has become crucial for effective traffic management, especially under congested road conditions. In the absence of c...
STREAMFLOW AND WATER QUALITY REGRESSION MODELING ...
African Journals Online (AJOL)
STREAMFLOW AND WATER QUALITY REGRESSION MODELING OF IMO RIVER SYSTEM: A CASE STUDY. ... Journal of Modeling, Design and Management of Engineering Systems ... Possible sources of contamination of Imo-river system within Nekede and Obigbo hydrological stations watershed were traced.
Leffondré, Karen; Jager, Kitty J.; Boucquemont, Julie; Stel, Vianda S.; Heinze, Georg
2014-01-01
Regression models are being used to quantify the effect of an exposure on an outcome, while adjusting for potential confounders. While the type of regression model to be used is determined by the nature of the outcome variable, e.g. linear regression has to be applied for continuous outcome
DART: Dropouts meet Multiple Additive Regression Trees
Rashmi, K. V.; Gilad-Bachrach, Ran
2015-01-01
Multiple Additive Regression Trees (MART), an ensemble model of boosted regression trees, is known to deliver high prediction accuracy for diverse tasks, and it is widely used in practice. However, it suffers an issue which we call over-specialization, wherein trees added at later iterations tend to impact the prediction of only a few instances, and make negligible contribution towards the remaining instances. This negatively affects the performance of the model on unseen data, and also makes...
Multinomial probit Bayesian additive regression trees.
Kindo, Bereket P; Wang, Hao; Peña, Edsel A
This article proposes multinomial probit Bayesian additive regression trees (MPBART) as a multinomial probit extension of BART - Bayesian additive regression trees. MPBART is flexible to allow inclusion of predictors that describe the observed units as well as the available choice alternatives. Through two simulation studies and four real data examples, we show that MPBART exhibits very good predictive performance in comparison to other discrete choice and multiclass classification methods. To implement MPBART, the R package mpbart is freely available from CRAN repositories.
Spontaneous regression of herniated lumbar discs.
Kim, Eric S; Oladunjoye, Azeem O; Li, Jay A; Kim, Kee D
2014-06-01
The spontaneous regression of a lumbar herniated disc is a common occurrence. Studies using imaging techniques as well as immunohistologic analyses have attempted to explain the mechanism for regression. However, the exact mechanism remains elusive. Understanding the process by which herniated discs disappear in the absence of surgery may better guide treatment. Recent case reports, radiographic and immunohistologic studies show that the extent of extrusion of the nucleus pulposus is related to a higher likelihood of regression. To our knowledge, Patient 3 is the first report of spontaneous regression occurring within 2 months. This occurrence was discovered intraoperatively. We present three illustrative patients. Patient 1, a 53-year-old man, presented with a large L2-L3 disc herniation. His 2 year follow-up MRI revealed a complete regression of the extruded fragment. Patient 2, a 58-year-old man, presented with an L3-L4 disc herniation with cephalad migration of a free fragment. MRI 9 months later showed no free fragment but progression of a disc bulge. Intraoperative exploration during the L3-L4 microdiscectomy confirmed the absence of the free fragment. Patient 3, a 58-year-old woman, presented with a large L2-L3 disc extrusion with cephalad migration. An imaging study performed 2 months after the initial study revealed an absence of the free fragment. Our case reports demonstrate the temporal variance in disc regression. While the time course and extent of regression vary widely, the rapid time in which regression can occur should caution surgeons contemplating discectomy based on an MRI performed a significant period prior to surgery. Copyright © 2013 Elsevier Ltd. All rights reserved.
Spontaneous regression of metastatic Merkel cell carcinoma.
LENUS (Irish Health Repository)
Hassan, S J
2010-01-01
Merkel cell carcinoma is a rare aggressive neuroendocrine carcinoma of the skin predominantly affecting elderly Caucasians. It has a high rate of local recurrence and regional lymph node metastases. It is associated with a poor prognosis. Complete spontaneous regression of Merkel cell carcinoma has been reported but is a poorly understood phenomenon. Here we present a case of complete spontaneous regression of metastatic Merkel cell carcinoma demonstrating a markedly different pattern of events from those previously published.
Online Active Linear Regression via Thresholding
Riquelme, Carlos; Johari, Ramesh; Zhang, Baosen
2016-01-01
We consider the problem of online active learning to collect data for regression modeling. Specifically, we consider a decision maker with a limited experimentation budget who must efficiently learn an underlying linear population model. Our main contribution is a novel threshold-based algorithm for selection of most informative observations; we characterize its performance and fundamental lower bounds. We extend the algorithm and its guarantees to sparse linear regression in high-dimensional...
Fuzzy multiple linear regression: A computational approach
Juang, C. H.; Huang, X. H.; Fleming, J. W.
1992-01-01
This paper presents a new computational approach for performing fuzzy regression. In contrast to Bardossy's approach, the new approach, while dealing with fuzzy variables, closely follows the conventional regression technique. In this approach, treatment of fuzzy input is more 'computational' than 'symbolic.' The following sections first outline the formulation of the new approach, then deal with the implementation and computational scheme, and this is followed by examples to illustrate the new procedure.
Marginal longitudinal semiparametric regression via penalized splines
Al Kadiri, M.
2010-08-01
We study the marginal longitudinal nonparametric regression problem and some of its semiparametric extensions. We point out that, while several elaborate proposals for efficient estimation have been proposed, a relative simple and straightforward one, based on penalized splines, has not. After describing our approach, we then explain how Gibbs sampling and the BUGS software can be used to achieve quick and effective implementation. Illustrations are provided for nonparametric regression and additive models.
The Geometry of Enhancement in Multiple Regression
Waller, Niels G.
2011-01-01
In linear multiple regression, "enhancement" is said to occur when R[superscript 2] = b[prime]r greater than r[prime]r, where b is a p x 1 vector of standardized regression coefficients and r is a p x 1 vector of correlations between a criterion y and a set of standardized regressors, x. When p = 1 then b [is congruent to] r and…
Two Paradoxes in Linear Regression Analysis.
Feng, Ge; Peng, Jing; Tu, Dongke; Zheng, Julia Z; Feng, Changyong
2016-12-25
Regression is one of the favorite tools in applied statistics. However, misuse and misinterpretation of results from regression analysis are common in biomedical research. In this paper we use statistical theory and simulation studies to clarify some paradoxes around this popular statistical method. In particular, we show that a widely used model selection procedure employed in many publications in top medical journals is wrong. Formal procedures based on solid statistical theory should be used in model selection.
Variable and subset selection in PLS regression
DEFF Research Database (Denmark)
Høskuldsson, Agnar
2001-01-01
The purpose of this paper is to present some useful methods for introductory analysis of variables and subsets in relation to PLS regression. We present here methods that are efficient in finding the appropriate variables or subset to use in the PLS regression. The general conclusion is that vari...... obtained by different methods. We also present an approach to orthogonal scatter correction. The procedures and comparisons are applied to industrial data. (C) 2001 Elsevier Science B.V. All rights reserved....
Post-processing through linear regression
Directory of Open Access Journals (Sweden)
B. Van Schaeybroeck
2011-03-01
Full Text Available Various post-processing techniques are compared for both deterministic and ensemble forecasts, all based on linear regression between forecast data and observations. In order to evaluate the quality of the regression methods, three criteria are proposed, related to the effective correction of forecast error, the optimal variability of the corrected forecast and multicollinearity. The regression schemes under consideration include the ordinary least-square (OLS method, a new time-dependent Tikhonov regularization (TDTR method, the total least-square method, a new geometric-mean regression (GM, a recently introduced error-in-variables (EVMOS method and, finally, a "best member" OLS method. The advantages and drawbacks of each method are clarified.
These techniques are applied in the context of the 63 Lorenz system, whose model version is affected by both initial condition and model errors. For short forecast lead times, the number and choice of predictors plays an important role. Contrarily to the other techniques, GM degrades when the number of predictors increases. At intermediate lead times, linear regression is unable to provide corrections to the forecast and can sometimes degrade the performance (GM and the best member OLS with noise. At long lead times the regression schemes (EVMOS, TDTR which yield the correct variability and the largest correlation between ensemble error and spread, should be preferred.
Multiple-Instance Regression with Structured Data
Wagstaff, Kiri L.; Lane, Terran; Roper, Alex
2008-01-01
We present a multiple-instance regression algorithm that models internal bag structure to identify the items most relevant to the bag labels. Multiple-instance regression (MIR) operates on a set of bags with real-valued labels, each containing a set of unlabeled items, in which the relevance of each item to its bag label is unknown. The goal is to predict the labels of new bags from their contents. Unlike previous MIR methods, MI-ClusterRegress can operate on bags that are structured in that they contain items drawn from a number of distinct (but unknown) distributions. MI-ClusterRegress simultaneously learns a model of the bag's internal structure, the relevance of each item, and a regression model that accurately predicts labels for new bags. We evaluated this approach on the challenging MIR problem of crop yield prediction from remote sensing data. MI-ClusterRegress provided predictions that were more accurate than those obtained with non-multiple-instance approaches or MIR methods that do not model the bag structure.
Habib, D.; Miller, J.A.
1989-01-01
inertinite and vascular tissue facies. The vascular tissue facies occurs in the proximal prodelta and nearshore shallow shelf lithofacies of early Maestrichtian age. Baed in the sequence of organuic facies, dinoflagellate species abundance, and lithofacies in the investigated wells, the nonmarine and coastal inertinite facies was first deposited and was followed during the late Campanian by a marine trangression when the nearshore amorphous debris facies was deposited. This was followed in the early Maestrichtian by the influx of terrigenous organic matter (vascular tissue facies) in response to deltaic progradation on the shallow shelf. A marine regression occured towards the close of the early Maestrichtian, emplacing the coastal inertinite facies. The major marine transgression occured near the end of the early Maestrichtian, developing a farther offshore amorphous debris facies on an expanded continental shelf which persisted through the late Maestrichtian. The inertinite facies returned during marine regression in the approximate position of the Maestrichtian/Danian boudnary. ?? 1989.
Symbolic Regression of Conditional Target Expressions
Korns, Michael F.
This chapter examines techniques for improving symbolic regression systems in cases where the target expression contains conditionals. In three previous papers we experimentedwith combining high performance techniques fromthe literature to produce a large scale, industrial strength, symbolic regression-classification system. Performance metrics across multiple problems show deterioration in accuracy for problems where the target expression contains conditionals. The techniques described herein are shown to improve accuracy on such conditional problems. Nine base test cases, from the literature, are used to test the improvement in accuracy. A previously published regression system combining standard genetic programming with abstract expression grammars, particle swarm optimization, differential evolution, context aware crossover and age-layered populations is tested on the nine base test cases. The regression system is enhanced with these additional techniques: pessimal vertical slicing, splicing of uncorrelated champions via abstract conditional expressions, and abstract mutation and crossover. The enhanced symbolic regression system is applied to the nine base test cases and an improvement in accuracy is observed.
Regression Test Selection for C# Programs
Directory of Open Access Journals (Sweden)
Nashat Mansour
2009-01-01
Full Text Available We present a regression test selection technique for C# programs. C# is fairly new and is often used within the Microsoft .Net framework to give programmers a solid base to develop a variety of applications. Regression testing is done after modifying a program. Regression test selection refers to selecting a suitable subset of test cases from the original test suite in order to be rerun. It aims to provide confidence that the modifications are correct and did not affect other unmodified parts of the program. The regression test selection technique presented in this paper accounts for C#.Net specific features. Our technique is based on three phases; the first phase builds an Affected Class Diagram consisting of classes that are affected by the change in the source code. The second phase builds a C# Interclass Graph (CIG from the affected class diagram based on C# specific features. In this phase, we reduce the number of selected test cases. The third phase involves further reduction and a new metric for assigning weights to test cases for prioritizing the selected test cases. We have empirically validated the proposed technique by using case studies. The empirical results show the usefulness of the proposed regression testing technique for C#.Net programs.
Principal component regression for crop yield estimation
Suryanarayana, T M V
2016-01-01
This book highlights the estimation of crop yield in Central Gujarat, especially with regard to the development of Multiple Regression Models and Principal Component Regression (PCR) models using climatological parameters as independent variables and crop yield as a dependent variable. It subsequently compares the multiple linear regression (MLR) and PCR results, and discusses the significance of PCR for crop yield estimation. In this context, the book also covers Principal Component Analysis (PCA), a statistical procedure used to reduce a number of correlated variables into a smaller number of uncorrelated variables called principal components (PC). This book will be helpful to the students and researchers, starting their works on climate and agriculture, mainly focussing on estimation models. The flow of chapters takes the readers in a smooth path, in understanding climate and weather and impact of climate change, and gradually proceeds towards downscaling techniques and then finally towards development of ...
On Solving Lq-Penalized Regressions
Directory of Open Access Journals (Sweden)
Tracy Zhou Wu
2007-01-01
Full Text Available Lq-penalized regression arises in multidimensional statistical modelling where all or part of the regression coefficients are penalized to achieve both accuracy and parsimony of statistical models. There is often substantial computational difficulty except for the quadratic penalty case. The difficulty is partly due to the nonsmoothness of the objective function inherited from the use of the absolute value. We propose a new solution method for the general Lq-penalized regression problem based on space transformation and thus efficient optimization algorithms. The new method has immediate applications in statistics, notably in penalized spline smoothing problems. In particular, the LASSO problem is shown to be polynomial time solvable. Numerical studies show promise of our approach.
LINEAR REGRESSION WITH R AND HADOOP
Directory of Open Access Journals (Sweden)
Bogdan OANCEA
2015-07-01
Full Text Available In this paper we present a way to solve the linear regression model with R and Hadoop using the Rhadoop library. We show how the linear regression model can be solved even for very large models that require special technologies. For storing the data we used Hadoop and for computation we used R. The interface between R and Hadoop is the open source library RHadoop. We present the main features of the Hadoop and R software systems and the way of interconnecting them. We then show how the least squares solution for the linear regression problem could be expressed in terms of map-reduce programming paradigm and how could be implemented using the Rhadoop library.
Computing aspects of power for multiple regression.
Dunlap, William P; Xin, Xue; Myers, Leann
2004-11-01
Rules of thumb for power in multiple regression research abound. Most such rules dictate the necessary sample size, but they are based only upon the number of predictor variables, usually ignoring other critical factors necessary to compute power accurately. Other guides to power in multiple regression typically use approximate rather than precise equations for the underlying distribution; entail complex preparatory computations; require interpolation with tabular presentation formats; run only under software such as Mathmatica or SAS that may not be immediately available to the user; or are sold to the user as parts of power computation packages. In contrast, the program we offer herein is immediately downloadable at no charge, runs under Windows, is interactive, self-explanatory, flexible to fit the user's own regression problems, and is as accurate as single precision computation ordinarily permits.
Regression Models for Market-Shares
DEFF Research Database (Denmark)
Birch, Kristina; Olsen, Jørgen Kai; Tjur, Tue
2005-01-01
On the background of a data set of weekly sales and prices for three brands of coffee, this paper discusses various regression models and their relation to the multiplicative competitive-interaction model (the MCI model, see Cooper 1988, 1993) for market-shares. Emphasis is put on the interpretat......On the background of a data set of weekly sales and prices for three brands of coffee, this paper discusses various regression models and their relation to the multiplicative competitive-interaction model (the MCI model, see Cooper 1988, 1993) for market-shares. Emphasis is put...... on the interpretation of the parameters in relation to models for the total sales based on discrete choice models.Key words and phrases. MCI model, discrete choice model, market-shares, price elasitcity, regression model....
Influence diagnostics in meta-regression model.
Shi, Lei; Zuo, ShanShan; Yu, Dalei; Zhou, Xiaohua
2017-09-01
This paper studies the influence diagnostics in meta-regression model including case deletion diagnostic and local influence analysis. We derive the subset deletion formulae for the estimation of regression coefficient and heterogeneity variance and obtain the corresponding influence measures. The DerSimonian and Laird estimation and maximum likelihood estimation methods in meta-regression are considered, respectively, to derive the results. Internal and external residual and leverage measure are defined. The local influence analysis based on case-weights perturbation scheme, responses perturbation scheme, covariate perturbation scheme, and within-variance perturbation scheme are explored. We introduce a method by simultaneous perturbing responses, covariate, and within-variance to obtain the local influence measure, which has an advantage of capable to compare the influence magnitude of influential studies from different perturbations. An example is used to illustrate the proposed methodology. Copyright © 2017 John Wiley & Sons, Ltd.
Groupwise Retargeted Least-Squares Regression.
Wang, Lingfeng; Pan, Chunhong
2017-01-25
In this brief, we propose a new groupwise retargeted least squares regression (GReLSR) model for multicategory classification. The main motivation behind GReLSR is to utilize an additional regularization to restrict the translation values of ReLSR, so that they should be similar within same class. By analyzing the regression targets of ReLSR, we propose a new formulation of ReLSR, where the translation values are expressed explicitly. On the basis of the new formulation, discriminative least-squares regression can be regarded as a special case of ReLSR with zero translation values. Moreover, a groupwise constraint is added to ReLSR to form the new GReLSR model. Extensive experiments on various machine leaning data sets illustrate that our method outperforms the current state-of-the-art approaches.
Liu, Zhan-yu; Huang, Jing-feng; Shi, Jing-jing; Tao, Rong-xiang; Zhou, Wan; Zhang, Li-Li
2007-10-01
Detecting plant health conditions plays a key role in farm pest management and crop protection. In this study, measurement of hyperspectral leaf reflectance in rice crop (Oryzasativa L.) was conducted on groups of healthy and infected leaves by the fungus Bipolaris oryzae (Helminthosporium oryzae Breda. de Hann) through the wavelength range from 350 to 2,500 nm. The percentage of leaf surface lesions was estimated and defined as the disease severity. Statistical methods like multiple stepwise regression, principal component analysis and partial least-square regression were utilized to calculate and estimate the disease severity of rice brown spot at the leaf level. Our results revealed that multiple stepwise linear regressions could efficiently estimate disease severity with three wavebands in seven steps. The root mean square errors (RMSEs) for training (n=210) and testing (n=53) dataset were 6.5% and 5.8%, respectively. Principal component analysis showed that the first principal component could explain approximately 80% of the variance of the original hyperspectral reflectance. The regression model with the first two principal components predicted a disease severity with RMSEs of 16.3% and 13.9% for the training and testing dataset, respectively. Partial least-square regression with seven extracted factors could most effectively predict disease severity compared with other statistical methods with RMSEs of 4.1% and 2.0% for the training and testing dataset, respectively. Our research demonstrates that it is feasible to estimate the disease severity of rice brown spot using hyperspectral reflectance data at the leaf level.
Multicollinearity in cross-sectional regressions
Lauridsen, Jørgen; Mur, Jesùs
2006-10-01
The paper examines robustness of results from cross-sectional regression paying attention to the impact of multicollinearity. It is well known that the reliability of estimators (least-squares or maximum-likelihood) gets worse as the linear relationships between the regressors become more acute. We resolve the discussion in a spatial context, looking closely into the behaviour shown, under several unfavourable conditions, by the most outstanding misspecification tests when collinear variables are added to the regression. A Monte Carlo simulation is performed. The conclusions point to the fact that these statistics react in different ways to the problems posed.
Multiple regression modeling of nonlinear data sets
Kravtsov, S.; Kondrashov, D.; Ghil, M.
2003-04-01
Application of multiple polynomial regression modeling to observational and model generated data sets is discussed. Here the form of classical multiple linear regression is generalized to a model that is still linear in its parameters, but includes general multivariate polynomials of predictor variables as the basis functions. The system's low-frequency evolution is assumed to be the result of deterministic, possibly nonlinear, dynamics excited by a temporally white, but geographically coherent and normally distributed white noise. In determining the appropriate structure of the latter, the multi-level generalization of multiple polynomial regression, where the residual stochastic forcing at a given level is subsequently modeled as a function of variables at this, and all preceding levels, has turned out to be useful. The number of levels is determined so that lag-0 covariance of the residual forcing converges to a constant matrix, while its lag-1 covariance vanishes. The method has been applied to the output from a three-layer quasi-geostrophic model, to the analysis of the Northern Hemisphere wintertime geopotential height anomalies, and to global sea-surface temperature (SST) data. In the former two cases, the nonlinear multi-regime structure of probability density function (PDF) constructed in the phase subspace of a few leading empirical orthogonal functions (EOFs), as well as the detailed spectrum of the data's temporal evolution, have been well reproduced by the regression simulations. We have given a simple dynamical interpretation of these results in terms of synoptic-eddy feedback on the system's low-frequency variability. In modeling of SST data, a simple way to include the seasonal cycle into the regression model has been developed. The regression simulation in this case produces ENSO events with maximum amplitude in December/January, while the positive events generally tend to have a larger amplitude than the negative events -- a feature that cannot be
Multispectral colormapping using penalized least square regression
DEFF Research Database (Denmark)
Dissing, Bjørn Skovlund; Carstensen, Jens Michael; Larsen, Rasmus
2010-01-01
The authors propose a novel method to map a multispectral image into the device independent color space CIE-XYZ. This method provides a way to visualize multispectral images by predicting colorvalues from spectral values while maintaining interpretability and is tested on a light emitting diode......-XYZ color matching functions. The target of the regression is a well known color chart, and the models are validated using leave one out cross validation in order to maintain best possible generalization ability. The authors compare the method with a direct linear regression and see...
Salience Assignment for Multiple-Instance Regression
Wagstaff, Kiri L.; Lane, Terran
2007-01-01
We present a Multiple-Instance Learning (MIL) algorithm for determining the salience of each item in each bag with respect to the bag's real-valued label. We use an alternating-projections constrained optimization approach to simultaneously learn a regression model and estimate all salience values. We evaluate this algorithm on a significant real-world problem, crop yield modeling, and demonstrate that it provides more extensive, intuitive, and stable salience models than Primary-Instance Regression, which selects a single relevant item from each bag.
Almost opposite regression dependence in bivariate distributions
Siburg, Karl Friedrich; Stoimenov, Pavel A.
2014-01-01
Let X,Y be two continuous random variables. Investigating the regression dependence of Y on X, respectively, of X on Y, we show that the two of them can have almost opposite behavior. Indeed, given any e > 0, we construct a bivariate random vector (X,Y) such that the respective regression dependence measures r2|1(X,Y), r1|2(X,Y) ∈ [0,1] introduced in Dette et al. (2013) satisfy r2|1(X,Y) = 1 as well as r1|2(X,Y)
Demonstration of a Fiber Optic Regression Probe
Korman, Valentin; Polzin, Kurt A.
2010-01-01
The capability to provide localized, real-time monitoring of material regression rates in various applications has the potential to provide a new stream of data for development testing of various components and systems, as well as serving as a monitoring tool in flight applications. These applications include, but are not limited to, the regression of a combusting solid fuel surface, the ablation of the throat in a chemical rocket or the heat shield of an aeroshell, and the monitoring of erosion in long-life plasma thrusters. The rate of regression in the first application is very fast, while the second and third are increasingly slower. A recent fundamental sensor development effort has led to a novel regression, erosion, and ablation sensor technology (REAST). The REAST sensor allows for measurement of real-time surface erosion rates at a discrete surface location. The sensor is optical, using two different, co-located fiber-optics to perform the regression measurement. The disparate optical transmission properties of the two fiber-optics makes it possible to measure the regression rate by monitoring the relative light attenuation through the fibers. As the fibers regress along with the parent material in which they are embedded, the relative light intensities through the two fibers changes, providing a measure of the regression rate. The optical nature of the system makes it relatively easy to use in a variety of harsh, high temperature environments, and it is also unaffected by the presence of electric and magnetic fields. In addition, the sensor could be used to perform optical spectroscopy on the light emitted by a process and collected by fibers, giving localized measurements of various properties. The capability to perform an in-situ measurement of material regression rates is useful in addressing a variety of physical issues in various applications. An in-situ measurement allows for real-time data regarding the erosion rates, providing a quick method for
Regression models for predicting anthropometric measurements of ...
African Journals Online (AJOL)
... System (ANFIS) was employed to select the two most influential of the five input measurements. This search was separately conducted for each of the output measurements. Regression models were developed from the collected anthropometric data. Also, the predictive performance of these models was examined using ...
Linear Regression Models for Estimating True Subsurface ...
Indian Academy of Sciences (India)
47
For the fact that subsurface resistivity is nonlinear, the datasets were first. 14 transformed into logarithmic scale to satisfy the basic regression assumptions. Three. 15 models, one each for the three array types, are thus developed based on simple linear. 16 relationships between the dependent and independent variables.
Method for nonlinear exponential regression analysis
Junkin, B. G.
1972-01-01
Two computer programs developed according to two general types of exponential models for conducting nonlinear exponential regression analysis are described. Least squares procedure is used in which the nonlinear problem is linearized by expanding in a Taylor series. Program is written in FORTRAN 5 for the Univac 1108 computer.
Panel data specifications in nonparametric kernel regression
DEFF Research Database (Denmark)
Czekaj, Tomasz Gerard; Henningsen, Arne
parametric panel data estimators to analyse the production technology of Polish crop farms. The results of our nonparametric kernel regressions generally differ from the estimates of the parametric models but they only slightly depend on the choice of the kernel functions. Based on economic reasoning, we...
Predicting Social Trust with Binary Logistic Regression
Adwere-Boamah, Joseph; Hufstedler, Shirley
2015-01-01
This study used binary logistic regression to predict social trust with five demographic variables from a national sample of adult individuals who participated in The General Social Survey (GSS) in 2012. The five predictor variables were respondents' highest degree earned, race, sex, general happiness and the importance of personally assisting…
Spontaneous regression of an intraspinal disc cyst
Energy Technology Data Exchange (ETDEWEB)
Demaerel, P.; Eerens, I.; Wilms, G. [University Hospital, Leuven (Belgium). Dept. of Radiology; Goffin, J. [Dept. of Neurosurgery, University Hospitals, Leuven (Belgium)
2001-11-01
We present a patient with a so-called disc cyst. Its location in the ventrolateral epidural space and its communication with the herniated disc are clearly shown. The disc cyst developed rapidly and regressed spontaneously. This observation, which has not been reported until now, appears to support focal degeneration with cyst formation as the pathogenesis. (orig.)
Optimal Changepoint Tests for Normal Linear Regression
Donald W.K. Andrews; Inpyo Lee; Werner Ploberger
1992-01-01
This paper determines a class of finite sample optimal tests for the existence of a changepoint at an unknown time in a normal linear multiple regression model with known variance. Optimal tests for multiple changepoints are also derived. Power comparisons of several tests are provided based on simulations.
A Skew-Normal Mixture Regression Model
Liu, Min; Lin, Tsung-I
2014-01-01
A challenge associated with traditional mixture regression models (MRMs), which rest on the assumption of normally distributed errors, is determining the number of unobserved groups. Specifically, even slight deviations from normality can lead to the detection of spurious classes. The current work aims to (a) examine how sensitive the commonly…
Structural Break Tests Robust to Regression Misspecification
Abi Morshed, Alaa; Andreou, E.; Boldea, Otilia
2016-01-01
Structural break tests developed in the literature for regression models are sensitive to model misspecification. We show - analytically and through simulations - that the sup Wald test for breaks in the conditional mean and variance of a time series process exhibits severe size distortions when the
Assumptions of Multiple Regression: Correcting Two Misconceptions
Williams, Matt N.; Gomez Grajales, Carlos Alberto; Kurkiewicz, Dason
2013-01-01
In 2002, an article entitled "Four assumptions of multiple regression that researchers should always test" by Osborne and Waters was published in "PARE." This article has gone on to be viewed more than 275,000 times (as of August 2013), and it is one of the first results displayed in a Google search for "regression…
Invariant Ordering of Item-Total Regressions
Tijmstra, Jesper; Hessen, David J.; van der Heijden, Peter G. M.; Sijtsma, Klaas
2011-01-01
A new observable consequence of the property of invariant item ordering is presented, which holds under Mokken's double monotonicity model for dichotomous data. The observable consequence is an invariant ordering of the item-total regressions. Kendall's measure of concordance "W" and a weighted version of this measure are proposed as measures for…
The M Word: Multicollinearity in Multiple Regression.
Morrow-Howell, Nancy
1994-01-01
Notes that existence of substantial correlation between two or more independent variables creates problems of multicollinearity in multiple regression. Discusses multicollinearity problem in social work research in which independent variables are usually intercorrelated. Clarifies problems created by multicollinearity, explains detection of…
Finite Algorithms for Robust Linear Regression
DEFF Research Database (Denmark)
Madsen, Kaj; Nielsen, Hans Bruun
1990-01-01
The Huber M-estimator for robust linear regression is analyzed. Newton type methods for solution of the problem are defined and analyzed, and finite convergence is proved. Numerical experiments with a large number of test problems demonstrate efficiency and indicate that this kind of approach may...
Macroeconomic Forecasting Using Penalized Regression Methods
Smeekes, Stephan; Wijler, Etiënne
2016-01-01
We study the suitability of lasso-type penalized regression techniques when applied to macroeconomic forecasting with high-dimensional datasets. We consider performance of the lasso-type methods when the true DGP is a factor model, contradicting the sparsity assumption underlying penalized
Creativity and Regression on the Rorschach.
Lazar, Billie S.
This paper describes the results of a study to further test and replicate previous studies partially supporting Kris's view that creativity is a regression in the service of the ego. For this sample of 42 female art and business college students, it was predicted that (1) highly creative Ss (measured by the Torrance Tests) produce more, and more…
Assessing risk factors for periodontitis using regression
Lobo Pereira, J. A.; Ferreira, Maria Cristina; Oliveira, Teresa
2013-10-01
Multivariate statistical analysis is indispensable to assess the associations and interactions between different factors and the risk of periodontitis. Among others, regression analysis is a statistical technique widely used in healthcare to investigate and model the relationship between variables. In our work we study the impact of socio-demographic, medical and behavioral factors on periodontal health. Using regression, linear and logistic models, we can assess the relevance, as risk factors for periodontitis disease, of the following independent variables (IVs): Age, Gender, Diabetic Status, Education, Smoking status and Plaque Index. The multiple linear regression analysis model was built to evaluate the influence of IVs on mean Attachment Loss (AL). Thus, the regression coefficients along with respective p-values will be obtained as well as the respective p-values from the significance tests. The classification of a case (individual) adopted in the logistic model was the extent of the destruction of periodontal tissues defined by an Attachment Loss greater than or equal to 4 mm in 25% (AL≥4mm/≥25%) of sites surveyed. The association measures include the Odds Ratios together with the correspondent 95% confidence intervals.
Regression testing Ajax applications : Coping with dynamism
Roest, D.; Mesbah, A.; Van Deursen, A.
2009-01-01
Note: This paper is a pre-print of: Danny Roest, Ali Mesbah and Arie van Deursen. Regression Testing AJAX Applications: Coping with Dynamism. In Proceedings of the 3rd International Conference on Software Testing, Verification and Validation (ICST’10), Paris, France. IEEE Computer Society, 2010.
Regression Formulae for Predicting Hematologic and Liver ...
African Journals Online (AJOL)
Dr Femi Olaleye
Full Length Research Article. Regression Formulae for Predicting. Hematologic and Liver Functions from. Years of Exposure to Cement Dust in. Cement Factory Workers in Sokoto, Nigeria. Mojiminiyi, F.B.O.1, Merenu, I.A.2, Njoku, C.H.3, Ibrahim, M.T.O.2. Departments of Physiology1, Community Health2 and Medicine3,.
Measurement Error in Education and Growth Regressions*
Portela, Miguel; Alessie, Rob; Teulings, Coen
2010-01-01
The use of the perpetual inventory method for the construction of education data per country leads to systematic measurement error. This paper analyzes its effect on growth regressions. We suggest a methodology for correcting this error. The standard attenuation bias suggests that using these
Revisiting Regression in Autism: Heller's "Dementia Infantilis"
Westphal, Alexander; Schelinski, Stefanie; Volkmar, Fred; Pelphrey, Kevin
2013-01-01
Theodor Heller first described a severe regression of adaptive function in normally developing children, something he termed dementia infantilis, over one 100 years ago. Dementia infantilis is most closely related to the modern diagnosis, childhood disintegrative disorder. We translate Heller's paper, Uber Dementia Infantilis, and discuss…
A Logistic Regression Model for Personnel Selection.
Raju, Nambury S.; And Others
1991-01-01
A two-parameter logistic regression model for personnel selection is proposed. The model was tested with a database of 84,808 military enlistees. The probability of job success was related directly to trait levels, addressing such topics as selection, validity generalization, employee classification, selection bias, and utility-based fair…
Hierarchical Logistic Regression in Course Placement
Schulz, E. Matthew; Betebenner, Damian; Ahn, Meeyeon
2004-01-01
Whether hierarchical logistic regression can reduce the sample size requirement for estimating optimal cutoff scores in a course placement service where predictive validity is measured by a threshold utility function is explored. Data from courses with varying class size were randomly partitioned into two halves per course. Nonhierarchical and…
Targeting: Logistic Regression, Special Cases and Extensions
Directory of Open Access Journals (Sweden)
Helmut Schaeben
2014-12-01
Full Text Available Logistic regression is a classical linear model for logit-transformed conditional probabilities of a binary target variable. It recovers the true conditional probabilities if the joint distribution of predictors and the target is of log-linear form. Weights-of-evidence is an ordinary logistic regression with parameters equal to the differences of the weights of evidence if all predictor variables are discrete and conditionally independent given the target variable. The hypothesis of conditional independence can be tested in terms of log-linear models. If the assumption of conditional independence is violated, the application of weights-of-evidence does not only corrupt the predicted conditional probabilities, but also their rank transform. Logistic regression models, including the interaction terms, can account for the lack of conditional independence, appropriate interaction terms compensate exactly for violations of conditional independence. Multilayer artificial neural nets may be seen as nested regression-like models, with some sigmoidal activation function. Most often, the logistic function is used as the activation function. If the net topology, i.e., its control, is sufficiently versatile to mimic interaction terms, artificial neural nets are able to account for violations of conditional independence and yield very similar results. Weights-of-evidence cannot reasonably include interaction terms; subsequent modifications of the weights, as often suggested, cannot emulate the effect of interaction terms.
Nonparametric and semiparametric dynamic additive regression models
DEFF Research Database (Denmark)
Scheike, Thomas Harder; Martinussen, Torben
Dynamic additive regression models provide a flexible class of models for analysis of longitudinal data. The approach suggested in this work is suited for measurements obtained at random time points and aims at estimating time-varying effects. Both fully nonparametric and semiparametric models can...
Deriving the Regression Line with Algebra
Quintanilla, John A.
2017-01-01
Exploration with spreadsheets and reliance on previous skills can lead students to determine the line of best fit. To perform linear regression on a set of data, students in Algebra 2 (or, in principle, Algebra 1) do not have to settle for using the mysterious "black box" of their graphing calculators (or other classroom technologies).…
Complex Regression Functional And Load Tests Development
Directory of Open Access Journals (Sweden)
Anton Andreevich Krasnopevtsev
2015-10-01
Full Text Available The article describes practical approaches for realization of automatized regression functional and load testing on random software-hardware complex, based on «MARSh 3.0» sample. Testing automatization is being realized for «MARSh 3.0» information security increase.
Simulation Optimization through Regression or Kriging Metamodels
Kleijnen, J.P.C.
2017-01-01
This chapter surveys two methods for the optimization of real-world systems that are modelled through simulation. These methods use either linear regression metamodels, or Kriging (Gaussian processes). The metamodel type guides the design of the experiment; this design …fixes the input combinations
Functional data analysis of generalized regression quantiles
Guo, Mengmeng
2013-11-05
Generalized regression quantiles, including the conditional quantiles and expectiles as special cases, are useful alternatives to the conditional means for characterizing a conditional distribution, especially when the interest lies in the tails. We develop a functional data analysis approach to jointly estimate a family of generalized regression quantiles. Our approach assumes that the generalized regression quantiles share some common features that can be summarized by a small number of principal component functions. The principal component functions are modeled as splines and are estimated by minimizing a penalized asymmetric loss measure. An iterative least asymmetrically weighted squares algorithm is developed for computation. While separate estimation of individual generalized regression quantiles usually suffers from large variability due to lack of sufficient data, by borrowing strength across data sets, our joint estimation approach significantly improves the estimation efficiency, which is demonstrated in a simulation study. The proposed method is applied to data from 159 weather stations in China to obtain the generalized quantile curves of the volatility of the temperature at these stations. © 2013 Springer Science+Business Media New York.
Williams, John D.; Lindem, Alfred C.
Four computer programs using the general purpose multiple linear regression program have been developed. Setwise regression analysis is a stepwise procedure for sets of variables; there will be as many steps as there are sets. Covarmlt allows a solution to the analysis of covariance design with multiple covariates. A third program has three…
Controlling attribute effect in linear regression
Calders, Toon
2013-12-01
In data mining we often have to learn from biased data, because, for instance, data comes from different batches or there was a gender or racial bias in the collection of social data. In some applications it may be necessary to explicitly control this bias in the models we learn from the data. This paper is the first to study learning linear regression models under constraints that control the biasing effect of a given attribute such as gender or batch number. We show how propensity modeling can be used for factoring out the part of the bias that can be justified by externally provided explanatory attributes. Then we analytically derive linear models that minimize squared error while controlling the bias by imposing constraints on the mean outcome or residuals of the models. Experiments with discrimination-aware crime prediction and batch effect normalization tasks show that the proposed techniques are successful in controlling attribute effects in linear regression models. © 2013 IEEE.
Stochastic development regression using method of moments
DEFF Research Database (Denmark)
Kühnel, Line; Sommer, Stefan Horst
2017-01-01
This paper considers the estimation problem arising when inferring parameters in the stochastic development regression model for manifold valued non-linear data. Stochastic development regression captures the relation between manifold-valued response and Euclidean covariate variables using...... the stochastic development construction. It is thereby able to incorporate several covariate variables and random effects. The model is intrinsically defined using the connection of the manifold, and the use of stochastic development avoids linearizing the geometry. We propose to infer parameters using...... the Method of Moments procedure that matches known constraints on moments of the observations conditional on the latent variables. The performance of the model is investigated in a simulation example using data on finite dimensional landmark manifolds....
Particle Swarm Optimization and regression analysis I
Mohanty, Souyma D.
2012-04-01
Particle Swarm Optimization (PSO) is now widely used in many problems that require global optimization of high-dimensional and highly multi-modal functions. However, PSO has not yet seen widespread use in astronomical data analysis even though optimization problems in this field have become increasingly complex. In this two-part article, we first provide an overview of the PSO method in the concrete context of a ubiquitous problem in astronomy, namely, regression analysis. In particular, we consider the problem of optimizing the placement of knots in regression based on cubic splines (spline smoothing). The second part will describe an in-depth investigation of PSO in some realistic data analysis challenges.
OPTIMAL DESIGNS FOR SPLINE WAVELET REGRESSION MODELS.
Maronge, Jacob M; Zhai, Yi; Wiens, Douglas P; Fang, Zhide
2017-05-01
In this article we investigate the optimal design problem for some wavelet regression models. Wavelets are very flexible in modeling complex relations, and optimal designs are appealing as a means of increasing the experimental precision. In contrast to the designs for the Haar wavelet regression model (Herzberg and Traves 1994; Oyet and Wiens 2000), the I-optimal designs we construct are different from the D-optimal designs. We also obtain c-optimal designs. Optimal (D- and I-) quadratic spline wavelet designs are constructed, both analytically and numerically. A case study shows that a significant saving of resources may be realized by employing an optimal design. We also construct model robust designs, to address response misspecification arising from fitting an incomplete set of wavelets.
Inverse Regression for the Wiener Class of Systems
Lyzell, Christian; Enqvist, Martin
2011-01-01
The concept of inverse regression has turned out to be quite useful for dimension reduction in regression analysis problems. Using methods like sliced inverse regression (SIR) and directional regression (DR), some high-dimensional nonlinear regression problems can be turned into more tractable low-dimensional problems. Here, the usefulness of inverse regression for identification of nonlinear dynamical systems will be discussed. In particular, it will be shown that the inverse regression meth...
Correlated Action Effects in Decision Theoretic Regression
Boutilier, Craig
2013-01-01
Much recent research in decision theoretic planning has adopted Markov decision processes (MDPs) as the model of choice, and has attempted to make their solution more tractable by exploiting problem structure. One particular algorithm, structured policy construction achieves this by means of a decision theoretic analog of goal regression using action descriptions based on Bayesian networks with tree-structured conditional probability tables. The algorithm as presented is not able to deal with...
Multiple Imputations for Linear Regression Models
Brownstone, David
1991-01-01
Rubin (1987) has proposed multiple imputations as a general method for estimation in the presence of missing data. Rubinâ€™s results only strictly apply to Bayesian models, but Schenker and Welsh (1988) directly prove the consistency Â multiple imputations inference~ when there are missing values of the dependent variable in linear regression models. This paper extends and modifies Schenker and Welshâ€™s theorems to give conditions where multiple imputations yield consistent inferences for bo...
Logistic regression a self-learning text
Kleinbaum, David G
1994-01-01
This textbook provides students and professionals in the health sciences with a presentation of the use of logistic regression in research. The text is self-contained, and designed to be used both in class or as a tool for self-study. It arises from the author's many years of experience teaching this material and the notes on which it is based have been extensively used throughout the world.
In utero diagnosis of caudal regression syndrome
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Lindsey M. Negrete, BS
2015-01-01
Full Text Available We present a case of caudal regression syndrome (CRS, a relatively uncommon defect of the lower spine accompanied by a wide range of developmental abnormalities. CRS is closely associated with pregestational diabetes and is nearly 200 times more prevalent in infants of diabetic mothers (1, 2. We report a case of prenatally suspected CRS in a fetus of a nondiabetic mother and discuss how the initial neurological abnormalities found on imaging correlate with the postnatal clinical deficits.
Three Contributions to Robust Regression Diagnostics
Czech Academy of Sciences Publication Activity Database
Kalina, Jan
2015-01-01
Roč. 11, č. 2 (2015), s. 69-78 ISSN 1336-9180 Grant - others:GA ČR(CZ) GA13-01930S; Nadační fond na podporu vědy(CZ) Neuron Institutional support: RVO:67985807 Keywords : robust regression * robust econometrics * hypothesis testing Subject RIV: BA - General Mathematics http://www.degruyter.com/view/j/jamsi.2015.11.issue-2/jamsi-2015-0013/jamsi-2015-0013. xml ?format=INT
Prediction of Rainfall Using Logistic Regression
A. H. M. Rahmatullah Imon; Manos C. Roy; S. K. Bhattacharjee
2012-01-01
The use of logistic regression modeling has exploded during the past decade for prediction and forecasting. From its original acceptance in epidemiologic research, the method is now commonly employed in almost all branches of knowledge. Rainfall is one of the most important phenomena of climate system. It is well known that the variability and intensity of rainfall act on natural, agricultural, human and even total biological system. So it is essential to be able to predict rainfall by findi...
Geographically weighted regression model on poverty indicator
Slamet, I.; Nugroho, N. F. T. A.; Muslich
2017-12-01
In this research, we applied geographically weighted regression (GWR) for analyzing the poverty in Central Java. We consider Gaussian Kernel as weighted function. The GWR uses the diagonal matrix resulted from calculating kernel Gaussian function as a weighted function in the regression model. The kernel weights is used to handle spatial effects on the data so that a model can be obtained for each location. The purpose of this paper is to model of poverty percentage data in Central Java province using GWR with Gaussian kernel weighted function and to determine the influencing factors in each regency/city in Central Java province. Based on the research, we obtained geographically weighted regression model with Gaussian kernel weighted function on poverty percentage data in Central Java province. We found that percentage of population working as farmers, population growth rate, percentage of households with regular sanitation, and BPJS beneficiaries are the variables that affect the percentage of poverty in Central Java province. In this research, we found the determination coefficient R2 are 68.64%. There are two categories of district which are influenced by different of significance factors.
Mixed-effects regression models in linguistics
Heylen, Kris; Geeraerts, Dirk
2018-01-01
When data consist of grouped observations or clusters, and there is a risk that measurements within the same group are not independent, group-specific random effects can be added to a regression model in order to account for such within-group associations. Regression models that contain such group-specific random effects are called mixed-effects regression models, or simply mixed models. Mixed models are a versatile tool that can handle both balanced and unbalanced datasets and that can also be applied when several layers of grouping are present in the data; these layers can either be nested or crossed. In linguistics, as in many other fields, the use of mixed models has gained ground rapidly over the last decade. This methodological evolution enables us to build more sophisticated and arguably more realistic models, but, due to its technical complexity, also introduces new challenges. This volume brings together a number of promising new evolutions in the use of mixed models in linguistics, but also addres...
Spontaneous Regression of a Cervical Disk Herniation
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Emre Delen
2014-03-01
Full Text Available A 54 years old female patient was admitted to our outpatient clinic with a two months history of muscle spasms of her neck and pain radiating to the left upper extremity. Magnetic resonance imaging had shown a large left-sided paracentral disk herniation at the C6-C7 disk space (Figure 1. Neurological examination showed no obvious neurological deficit. She received conservative treatment including bed rest, rehabilitation, and analgesic drugs. After 13 months, requested by the patient, a second magnetic resonance imaging study showed resolution of the disc herniation.(Figure 2 Although the literature contains several reports about spontaneous regression of herniated lumbar disc without surgical intervention, that of phenomenon reported for herniated cervical level is rare, and such reports are few[1]. In conclusion, herniated intervertebral disc have the potential to spontaneously regress independently from the spine level. With further studies, determining the predictive signs for prognostic evaluation for spontaneous regression which would yield to conservative treatment would be beneficial.
General regression and representation model for classification.
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Jianjun Qian
Full Text Available Recently, the regularized coding-based classification methods (e.g. SRC and CRC show a great potential for pattern classification. However, most existing coding methods assume that the representation residuals are uncorrelated. In real-world applications, this assumption does not hold. In this paper, we take account of the correlations of the representation residuals and develop a general regression and representation model (GRR for classification. GRR not only has advantages of CRC, but also takes full use of the prior information (e.g. the correlations between representation residuals and representation coefficients and the specific information (weight matrix of image pixels to enhance the classification performance. GRR uses the generalized Tikhonov regularization and K Nearest Neighbors to learn the prior information from the training data. Meanwhile, the specific information is obtained by using an iterative algorithm to update the feature (or image pixel weights of the test sample. With the proposed model as a platform, we design two classifiers: basic general regression and representation classifier (B-GRR and robust general regression and representation classifier (R-GRR. The experimental results demonstrate the performance advantages of proposed methods over state-of-the-art algorithms.
Multitask Quantile Regression under the Transnormal Model.
Fan, Jianqing; Xue, Lingzhou; Zou, Hui
2016-01-01
We consider estimating multi-task quantile regression under the transnormal model, with focus on high-dimensional setting. We derive a surprisingly simple closed-form solution through rank-based covariance regularization. In particular, we propose the rank-based ℓ1 penalization with positive definite constraints for estimating sparse covariance matrices, and the rank-based banded Cholesky decomposition regularization for estimating banded precision matrices. By taking advantage of alternating direction method of multipliers, nearest correlation matrix projection is introduced that inherits sampling properties of the unprojected one. Our work combines strengths of quantile regression and rank-based covariance regularization to simultaneously deal with nonlinearity and nonnormality for high-dimensional regression. Furthermore, the proposed method strikes a good balance between robustness and efficiency, achieves the "oracle"-like convergence rate, and provides the provable prediction interval under the high-dimensional setting. The finite-sample performance of the proposed method is also examined. The performance of our proposed rank-based method is demonstrated in a real application to analyze the protein mass spectroscopy data.
Bayesian Inference of a Multivariate Regression Model
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Marick S. Sinay
2014-01-01
Full Text Available We explore Bayesian inference of a multivariate linear regression model with use of a flexible prior for the covariance structure. The commonly adopted Bayesian setup involves the conjugate prior, multivariate normal distribution for the regression coefficients and inverse Wishart specification for the covariance matrix. Here we depart from this approach and propose a novel Bayesian estimator for the covariance. A multivariate normal prior for the unique elements of the matrix logarithm of the covariance matrix is considered. Such structure allows for a richer class of prior distributions for the covariance, with respect to strength of beliefs in prior location hyperparameters, as well as the added ability, to model potential correlation amongst the covariance structure. The posterior moments of all relevant parameters of interest are calculated based upon numerical results via a Markov chain Monte Carlo procedure. The Metropolis-Hastings-within-Gibbs algorithm is invoked to account for the construction of a proposal density that closely matches the shape of the target posterior distribution. As an application of the proposed technique, we investigate a multiple regression based upon the 1980 High School and Beyond Survey.
Leukemia prediction using sparse logistic regression.
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Tapio Manninen
Full Text Available We describe a supervised prediction method for diagnosis of acute myeloid leukemia (AML from patient samples based on flow cytometry measurements. We use a data driven approach with machine learning methods to train a computational model that takes in flow cytometry measurements from a single patient and gives a confidence score of the patient being AML-positive. Our solution is based on an [Formula: see text] regularized logistic regression model that aggregates AML test statistics calculated from individual test tubes with different cell populations and fluorescent markers. The model construction is entirely data driven and no prior biological knowledge is used. The described solution scored a 100% classification accuracy in the DREAM6/FlowCAP2 Molecular Classification of Acute Myeloid Leukaemia Challenge against a golden standard consisting of 20 AML-positive and 160 healthy patients. Here we perform a more extensive validation of the prediction model performance and further improve and simplify our original method showing that statistically equal results can be obtained by using simple average marker intensities as features in the logistic regression model. In addition to the logistic regression based model, we also present other classification models and compare their performance quantitatively. The key benefit in our prediction method compared to other solutions with similar performance is that our model only uses a small fraction of the flow cytometry measurements making our solution highly economical.
[Caudal regression syndrome--two case reports].
Kokrdová, Z; Pavlíková, J
2008-01-01
The authors demonstrate two cases of caudal regression syndrome (CRS), a rare malformative syndrom, seen mainly in cases of maternal diabetes with poor metabolic control. Case report. Department of Obstetrics and Gynecology, Department of Medicine Regional Hospital Pardubice. The caudal regression syndrome (CRS) was revealed in two women with praegestational diabetes. The diagnosis was made at 18 and 20 weeks. The characteristic ultrasound findings include abrupt interruption of the spine and abnormal position of the lower limbs. The femur bones are fixed in a "V" pattern, giving a typical "Buddha's poise". A complete examination must be conducted for possible urinary and intestinal malformations. The mechanism leading to malformation is discussed in the article. To prevent pregnancy at the time of bad controlled diabetes is the only way to minimaze the risk of producing a congenitally malformed baby including caudal regression syndrom in the population of diabetic mothers. Family planning and supervision by the specialists is always advisable. Early diagnosis of CRS is possible using vaginal ultrasound. Emphasis is placed on the association of abrupt disruption of dorsal or lumbar spine and abnormal images of the lower limbs fixed in a,,V" formation, which is characteristic sign of CRS.
Humanoid environmental perception with Gaussian process regression
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Dingsheng Luo
2016-11-01
Full Text Available Nowadays, humanoids are increasingly expected acting in the real world to complete some high-level tasks humanly and intelligently. However, this is a hard issue due to that the real world is always extremely complicated and full of miscellaneous variations. As a consequence, for a real-world-acting robot, precisely perceiving the environmental changes might be an essential premise. Unlike human being, humanoid robot usually turns out to be with much less sensors to get enough information from the real world, which further leads the environmental perception problem to be more challenging. Although it can be tackled by establishing direct sensory mappings or adopting probabilistic filtering methods, the nonlinearity and uncertainty caused by both the complexity of the environment and the high degree of freedom of the robots will result in tough modeling difficulties. In our study, with the Gaussian process regression framework, an alternative learning approach to address such a modeling problem is proposed and discussed. Meanwhile, to debase the influence derived from limited sensors, the idea of fusing multiple sensory information is also involved. To evaluate the effectiveness, with two representative environment changing tasks, that is, suffering unknown external pushing and suddenly encountering sloped terrains, the proposed approach is applied to a humanoid, which is only equipped with a three-axis gyroscope and a three-axis accelerometer. Experimental results reveal that the proposed Gaussian process regression-based approach is effective in coping with the nonlinearity and uncertainty of the humanoid environmental perception problem. Further, a humanoid balancing controller is developed, which takes the output of the Gaussian process regression-based environmental perception as the seed to activate the corresponding balancing strategy. Both simulated and hardware experiments consistently show that our approach is valuable and leads to a
Prediction of Rainfall Using Logistic Regression
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A.H.M. Rahmatullah Imon
2012-07-01
Full Text Available Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman","serif";} The use of logistic regression modeling has exploded during the past decade for prediction and forecasting. From its original acceptance in epidemiologic research, the method is now commonly employed in almost all branches of knowledge. Rainfall is one of the most important phenomena of climate system. It is well known that the variability and intensity of rainfall act on natural, agricultural, human and even total biological system. So it is essential to be able to predict rainfall by finding out the appropriate predictors. In this paper an attempt has been made to use logistic regression for predicting rainfall. It is evident that the climatic data are often subjected to gross recording errors though this problem often goes unnoticed to the analysts. In this paper we have used very recent screening methods to check and correct the climatic data that we use in our study. We have used fourteen years’ daily rainfall data to formulate our model. Then we use two years’ observed daily rainfall data treating them as future data for the cross validation of our model. Our findings clearly show that if we are able to choose appropriate predictors for rainfall, logistic regression model can predict the rainfall very efficiently.
Bayesian regression of piecewise homogeneous Poisson processes
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Diego Sevilla
2015-12-01
Full Text Available In this paper, a Bayesian method for piecewise regression is adapted to handle counting processes data distributed as Poisson. A numerical code in Mathematica is developed and tested analyzing simulated data. The resulting method is valuable for detecting breaking points in the count rate of time series for Poisson processes. Received: 2 November 2015, Accepted: 27 November 2015; Edited by: R. Dickman; Reviewed by: M. Hutter, Australian National University, Canberra, Australia.; DOI: http://dx.doi.org/10.4279/PIP.070018 Cite as: D J R Sevilla, Papers in Physics 7, 070018 (2015
Mapping geogenic radon potential by regression kriging
Energy Technology Data Exchange (ETDEWEB)
Pásztor, László [Institute for Soil Sciences and Agricultural Chemistry, Centre for Agricultural Research, Hungarian Academy of Sciences, Department of Environmental Informatics, Herman Ottó út 15, 1022 Budapest (Hungary); Szabó, Katalin Zsuzsanna, E-mail: sz_k_zs@yahoo.de [Department of Chemistry, Institute of Environmental Science, Szent István University, Páter Károly u. 1, Gödöllő 2100 (Hungary); Szatmári, Gábor; Laborczi, Annamária [Institute for Soil Sciences and Agricultural Chemistry, Centre for Agricultural Research, Hungarian Academy of Sciences, Department of Environmental Informatics, Herman Ottó út 15, 1022 Budapest (Hungary); Horváth, Ákos [Department of Atomic Physics, Eötvös University, Pázmány Péter sétány 1/A, 1117 Budapest (Hungary)
2016-02-15
Radon ({sup 222}Rn) gas is produced in the radioactive decay chain of uranium ({sup 238}U) which is an element that is naturally present in soils. Radon is transported mainly by diffusion and convection mechanisms through the soil depending mainly on the physical and meteorological parameters of the soil and can enter and accumulate in buildings. Health risks originating from indoor radon concentration can be attributed to natural factors and is characterized by geogenic radon potential (GRP). Identification of areas with high health risks require spatial modeling, that is, mapping of radon risk. In addition to geology and meteorology, physical soil properties play a significant role in the determination of GRP. In order to compile a reliable GRP map for a model area in Central-Hungary, spatial auxiliary information representing GRP forming environmental factors were taken into account to support the spatial inference of the locally measured GRP values. Since the number of measured sites was limited, efficient spatial prediction methodologies were searched for to construct a reliable map for a larger area. Regression kriging (RK) was applied for the interpolation using spatially exhaustive auxiliary data on soil, geology, topography, land use and climate. RK divides the spatial inference into two parts. Firstly, the deterministic component of the target variable is determined by a regression model. The residuals of the multiple linear regression analysis represent the spatially varying but dependent stochastic component, which are interpolated by kriging. The final map is the sum of the two component predictions. Overall accuracy of the map was tested by Leave-One-Out Cross-Validation. Furthermore the spatial reliability of the resultant map is also estimated by the calculation of the 90% prediction interval of the local prediction values. The applicability of the applied method as well as that of the map is discussed briefly. - Highlights: • A new method
Paraneoplastic pemphigus regression after thymoma resection
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Stergiou Eleni
2008-08-01
Full Text Available Abstract Background Among human neoplasms thymomas are associated with highest frequency with paraneoplastic autoimmune diseases. Case presentation A case of a 42-year-old woman with paraneoplastic pemphigus as the first manifestation of thymoma is reported. Transsternal complete thymoma resection achieved pemphigus regression. The clinical correlations between pemphigus and thymoma are presented. Conclusion Our case report provides further evidence for the important role of autoantibodies in the pathogenesis of paraneoplastic skin diseases in thymoma patients. It also documents the improvement of the associated pemphigus after radical treatment of the thymoma.
A method for nonlinear exponential regression analysis
Junkin, B. G.
1971-01-01
A computer-oriented technique is presented for performing a nonlinear exponential regression analysis on decay-type experimental data. The technique involves the least squares procedure wherein the nonlinear problem is linearized by expansion in a Taylor series. A linear curve fitting procedure for determining the initial nominal estimates for the unknown exponential model parameters is included as an integral part of the technique. A correction matrix was derived and then applied to the nominal estimate to produce an improved set of model parameters. The solution cycle is repeated until some predetermined criterion is satisfied.
Multinomial logistic regression in workers' health
Grilo, Luís M.; Grilo, Helena L.; Gonçalves, Sónia P.; Junça, Ana
2017-11-01
In European countries, namely in Portugal, it is common to hear some people mentioning that they are exposed to excessive and continuous psychosocial stressors at work. This is increasing in diverse activity sectors, such as, the Services sector. A representative sample was collected from a Portuguese Services' organization, by applying a survey (internationally validated), which variables were measured in five ordered categories in Likert-type scale. A multinomial logistic regression model is used to estimate the probability of each category of the dependent variable general health perception where, among other independent variables, burnout appear as statistically significant.
Inferring gene regression networks with model trees
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Aguilar-Ruiz Jesus S
2010-10-01
Full Text Available Abstract Background Novel strategies are required in order to handle the huge amount of data produced by microarray technologies. To infer gene regulatory networks, the first step is to find direct regulatory relationships between genes building the so-called gene co-expression networks. They are typically generated using correlation statistics as pairwise similarity measures. Correlation-based methods are very useful in order to determine whether two genes have a strong global similarity but do not detect local similarities. Results We propose model trees as a method to identify gene interaction networks. While correlation-based methods analyze each pair of genes, in our approach we generate a single regression tree for each gene from the remaining genes. Finally, a graph from all the relationships among output and input genes is built taking into account whether the pair of genes is statistically significant. For this reason we apply a statistical procedure to control the false discovery rate. The performance of our approach, named REGNET, is experimentally tested on two well-known data sets: Saccharomyces Cerevisiae and E.coli data set. First, the biological coherence of the results are tested. Second the E.coli transcriptional network (in the Regulon database is used as control to compare the results to that of a correlation-based method. This experiment shows that REGNET performs more accurately at detecting true gene associations than the Pearson and Spearman zeroth and first-order correlation-based methods. Conclusions REGNET generates gene association networks from gene expression data, and differs from correlation-based methods in that the relationship between one gene and others is calculated simultaneously. Model trees are very useful techniques to estimate the numerical values for the target genes by linear regression functions. They are very often more precise than linear regression models because they can add just different linear
Non-Standard Semiparametric Regression via BRugs
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Jennifer K. Marley
2010-11-01
Full Text Available We provide several illustrations of Bayesian semiparametric regression analyses in the BRugs package. BRugs facilitates use of the BUGS inference engine from the R computing environment and allows analyses to be managed using scripts. The examples are chosen to represent an array of non-standard situations, for which mixed model software is not viable. The situations include: the response variable being outside of the one-parameter exponential family, data subject to missingness, data subject to measurement error and parameters entering the model via an index.
Cyclodextrin promotes atherosclerosis regression via macrophage reprogramming
DEFF Research Database (Denmark)
2016-01-01
Atherosclerosis is an inflammatory disease linked to elevated blood cholesterol concentrations. Despite ongoing advances in the prevention and treatment of atherosclerosis, cardiovascular disease remains the leading cause of death worldwide. Continuous retention of apolipoprotein B...... that increases cholesterol solubility in preventing and reversing atherosclerosis. We showed that CD treatment of murine atherosclerosis reduced atherosclerotic plaque size and CC load and promoted plaque regression even with a continued cholesterol-rich diet. Mechanistically, CD increased oxysterol production...... of CD as well as for augmented reverse cholesterol transport. Because CD treatment in humans is safe and CD beneficially affects key mechanisms of atherogenesis, it may therefore be used clinically to prevent or treat human atherosclerosis....
Affine Projection Algorithm Using Regressive Estimated Error
Zhang, Shu; Zhi, Yongfeng
2011-01-01
An affine projection algorithm using regressive estimated error (APA-REE) is presented in this paper. By redefining the iterated error of the affine projection algorithm (APA), a new algorithm is obtained, and it improves the adaptive filtering convergence rate. We analyze the iterated error signal and the stability for the APA-REE algorithm. The steady-state weights of the APA-REE algorithm are proved to be unbiased and consist. The simulation results show that the proposed algorithm has a f...
SDE based regression for random PDEs
Bayer, Christian
2016-01-06
A simulation based method for the numerical solution of PDE with random coefficients is presented. By the Feynman-Kac formula, the solution can be represented as conditional expectation of a functional of a corresponding stochastic differential equation driven by independent noise. A time discretization of the SDE for a set of points in the domain and a subsequent Monte Carlo regression lead to an approximation of the global solution of the random PDE. We provide an initial error and complexity analysis of the proposed method along with numerical examples illustrating its behaviour.
Spectral density regression for bivariate extremes
Castro Camilo, Daniela
2016-05-11
We introduce a density regression model for the spectral density of a bivariate extreme value distribution, that allows us to assess how extremal dependence can change over a covariate. Inference is performed through a double kernel estimator, which can be seen as an extension of the Nadaraya–Watson estimator where the usual scalar responses are replaced by mean constrained densities on the unit interval. Numerical experiments with the methods illustrate their resilience in a variety of contexts of practical interest. An extreme temperature dataset is used to illustrate our methods. © 2016 Springer-Verlag Berlin Heidelberg
Bry, Xavier; Verron, Thomas; Cazes, Pierre
2008-01-01
A variable group Y is assumed to depend upon R thematic variable groups X 1, >..., X R . We assume that components in Y depend linearly upon components in the Xr's. In this work, we propose a multiple covariance criterion which extends that of PLS regression to this multiple predictor groups situation. On this criterion, we build a PLS-type exploratory method - Structural Equation Exploratory Regression (SEER) - that allows to simultaneously perform dimension reduction in groups and investiga...
Ebrahimzadeh, Farzad; Hajizadeh, Ebrahim; Vahabi, Nasim; Almasian, Mohammad; Bakhteyar, Katayoon
2015-01-01
Unwanted pregnancy not intended by at least one of the parents has undesirable consequences for the family and the society. In the present study, three classification models were used and compared to predict unwanted pregnancies in an urban population. In this cross-sectional study, 887 pregnant mothers referring to health centers in Khorramabad, Iran, in 2012 were selected by the stratified and cluster sampling; relevant variables were measured and for prediction of unwanted pregnancy, logistic regression, discriminant analysis, and probit regression models and SPSS software version 21 were used. To compare these models, indicators such as sensitivity, specificity, the area under the ROC curve, and the percentage of correct predictions were used. The prevalence of unwanted pregnancies was 25.3%. The logistic and probit regression models indicated that parity and pregnancy spacing, contraceptive methods, household income and number of living male children were related to unwanted pregnancy. The performance of the models based on the area under the ROC curve was 0.735, 0.733, and 0.680 for logistic regression, probit regression, and linear discriminant analysis, respectively. Given the relatively high prevalence of unwanted pregnancies in Khorramabad, it seems necessary to revise family planning programs. Despite the similar accuracy of the models, if the researcher is interested in the interpretability of the results, the use of the logistic regression model is recommended.
Regression Models For Saffron Yields in Iran
S. H, Sanaeinejad; S. N, Hosseini
Saffron is an important crop in social and economical aspects in Khorassan Province (Northeast of Iran). In this research wetried to evaluate trends of saffron yield in recent years and to study the relationship between saffron yield and the climate change. A regression analysis was used to predict saffron yield based on 20 years of yield data in Birjand, Ghaen and Ferdows cities.Climatologically data for the same periods was provided by database of Khorassan Climatology Center. Climatologically data includedtemperature, rainfall, relative humidity and sunshine hours for ModelI, and temperature and rainfall for Model II. The results showed the coefficients of determination for Birjand, Ferdows and Ghaen for Model I were 0.69, 0.50 and 0.81 respectively. Also coefficients of determination for the same cities for model II were 0.53, 0.50 and 0.72 respectively. Multiple regression analysisindicated that among weather variables, temperature was the key parameter for variation ofsaffron yield. It was concluded that increasing temperature at spring was the main cause of declined saffron yield during recent years across the province. Finally, yield trend was predicted for the last 5 years using time series analysis.
Bayesian nonlinear regression for large small problems
Chakraborty, Sounak
2012-07-01
Statistical modeling and inference problems with sample sizes substantially smaller than the number of available covariates are challenging. This is known as large p small n problem. Furthermore, the problem is more complicated when we have multiple correlated responses. We develop multivariate nonlinear regression models in this setup for accurate prediction. In this paper, we introduce a full Bayesian support vector regression model with Vapnik\\'s ε-insensitive loss function, based on reproducing kernel Hilbert spaces (RKHS) under the multivariate correlated response setup. This provides a full probabilistic description of support vector machine (SVM) rather than an algorithm for fitting purposes. We have also introduced a multivariate version of the relevance vector machine (RVM). Instead of the original treatment of the RVM relying on the use of type II maximum likelihood estimates of the hyper-parameters, we put a prior on the hyper-parameters and use Markov chain Monte Carlo technique for computation. We have also proposed an empirical Bayes method for our RVM and SVM. Our methods are illustrated with a prediction problem in the near-infrared (NIR) spectroscopy. A simulation study is also undertaken to check the prediction accuracy of our models. © 2012 Elsevier Inc.
Supporting Regularized Logistic Regression Privately and Efficiently.
Li, Wenfa; Liu, Hongzhe; Yang, Peng; Xie, Wei
2016-01-01
As one of the most popular statistical and machine learning models, logistic regression with regularization has found wide adoption in biomedicine, social sciences, information technology, and so on. These domains often involve data of human subjects that are contingent upon strict privacy regulations. Concerns over data privacy make it increasingly difficult to coordinate and conduct large-scale collaborative studies, which typically rely on cross-institution data sharing and joint analysis. Our work here focuses on safeguarding regularized logistic regression, a widely-used statistical model while at the same time has not been investigated from a data security and privacy perspective. We consider a common use scenario of multi-institution collaborative studies, such as in the form of research consortia or networks as widely seen in genetics, epidemiology, social sciences, etc. To make our privacy-enhancing solution practical, we demonstrate a non-conventional and computationally efficient method leveraging distributing computing and strong cryptography to provide comprehensive protection over individual-level and summary data. Extensive empirical evaluations on several studies validate the privacy guarantee, efficiency and scalability of our proposal. We also discuss the practical implications of our solution for large-scale studies and applications from various disciplines, including genetic and biomedical studies, smart grid, network analysis, etc.
Supporting Regularized Logistic Regression Privately and Efficiently.
Directory of Open Access Journals (Sweden)
Wenfa Li
Full Text Available As one of the most popular statistical and machine learning models, logistic regression with regularization has found wide adoption in biomedicine, social sciences, information technology, and so on. These domains often involve data of human subjects that are contingent upon strict privacy regulations. Concerns over data privacy make it increasingly difficult to coordinate and conduct large-scale collaborative studies, which typically rely on cross-institution data sharing and joint analysis. Our work here focuses on safeguarding regularized logistic regression, a widely-used statistical model while at the same time has not been investigated from a data security and privacy perspective. We consider a common use scenario of multi-institution collaborative studies, such as in the form of research consortia or networks as widely seen in genetics, epidemiology, social sciences, etc. To make our privacy-enhancing solution practical, we demonstrate a non-conventional and computationally efficient method leveraging distributing computing and strong cryptography to provide comprehensive protection over individual-level and summary data. Extensive empirical evaluations on several studies validate the privacy guarantee, efficiency and scalability of our proposal. We also discuss the practical implications of our solution for large-scale studies and applications from various disciplines, including genetic and biomedical studies, smart grid, network analysis, etc.
Regression testing in the TOTEM DCS
Rodríguez, F. Lucas; Atanassov, I.; Burkimsher, P.; Frost, O.; Taskinen, J.; Tulimaki, V.
2012-12-01
The Detector Control System of the TOTEM experiment at the LHC is built with the industrial product WinCC OA (PVSS). The TOTEM system is generated automatically through scripts using as input the detector Product Breakdown Structure (PBS) structure and its pinout connectivity, archiving and alarm metainformation, and some other heuristics based on the naming conventions. When those initial parameters and automation code are modified to include new features, the resulting PVSS system can also introduce side-effects. On a daily basis, a custom developed regression testing tool takes the most recent code from a Subversion (SVN) repository and builds a new control system from scratch. This system is exported in plain text format using the PVSS export tool, and compared with a system previously validated by a human. A report is sent to the developers with any differences highlighted, in readiness for validation and acceptance as a new stable version. This regression approach is not dependent on any development framework or methodology. This process has been satisfactory during several months, proving to be a very valuable tool before deploying new versions in the production systems.
Cox regression model with doubly truncated data.
Rennert, Lior; Xie, Sharon X
2017-10-26
Truncation is a well-known phenomenon that may be present in observational studies of time-to-event data. While many methods exist to adjust for either left or right truncation, there are very few methods that adjust for simultaneous left and right truncation, also known as double truncation. We propose a Cox regression model to adjust for this double truncation using a weighted estimating equation approach, where the weights are estimated from the data both parametrically and nonparametrically, and are inversely proportional to the probability that a subject is observed. The resulting weighted estimators of the hazard ratio are consistent. The parametric weighted estimator is asymptotically normal and a consistent estimator of the asymptotic variance is provided. For the nonparametric weighted estimator, we apply the bootstrap technique to estimate the variance and confidence intervals. We demonstrate through extensive simulations that the proposed estimators greatly reduce the bias compared to the unweighted Cox regression estimator which ignores truncation. We illustrate our approach in an analysis of autopsy-confirmed Alzheimer's disease patients to assess the effect of education on survival. © 2017, The International Biometric Society.
Scientific Progress or Regress in Sports Physiology?
Böning, Dieter
2016-11-01
In modern societies there is strong belief in scientific progress, but, unfortunately, a parallel partial regress occurs because of often avoidable mistakes. Mistakes are mainly forgetting, erroneous theories, errors in experiments and manuscripts, prejudice, selected publication of "positive" results, and fraud. An example of forgetting is that methods introduced decades ago are used without knowing the underlying theories: Basic articles are no longer read or cited. This omission may cause incorrect interpretation of results. For instance, false use of actual base excess instead of standard base excess for calculation of the number of hydrogen ions leaving the muscles raised the idea that an unknown fixed acid is produced in addition to lactic acid during exercise. An erroneous theory led to the conclusion that lactate is not the anion of a strong acid but a buffer. Mistakes occur after incorrect application of a method, after exclusion of unwelcome values, during evaluation of measurements by false calculations, or during preparation of manuscripts. Co-authors, as well as reviewers, do not always carefully read papers before publication. Peer reviewers might be biased against a hypothesis or an author. A general problem is selected publication of positive results. An example of fraud in sports medicine is the presence of doped subjects in groups of investigated athletes. To reduce regress, it is important that investigators search both original and recent articles on a topic and conscientiously examine the data. All co-authors and reviewers should read the text thoroughly and inspect all tables and figures in a manuscript.
Optimization of DWDM Demultiplexer Using Regression Analysis
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Venkatachalam Rajarajan Balaji
2016-01-01
Full Text Available We propose a novel twelve-channel Dense Wavelength Division Multiplexing (DWDM demultiplexer, using the two-dimensional photonic crystal (2D PC with square resonant cavity (SRC of ITU-T G.694.1 standard. The DWDM demultiplexer consists of an input waveguide, SRC, and output waveguide. The SRC in the proposed demultiplexer consists of square resonator and microcavity. The microcavity center rod radius (Rm is proportional to refractive index. The refractive index property of the rods filters the wavelengths of odd and even channels. The proposed microcavity can filter twelve ITU-T G.694.1 standard wavelengths with 0.2 nm/25 GHz channel spacing between the wavelengths. From the simulation, we optimize the rod radius and wavelength with linear regression analysis. From the regression analysis, we can achieve 95% of accuracy with an average quality factor of 7890, the uniform spectral line-width of 0.2 nm, the transmission efficiency of 90%, crosstalk of −42 dB, and footprint of about 784 μm2.
Downscaling Wind Forecasts via Clustering and Regression
Lee, H. S.; Zhang, Y.; Liu, Y.; Wu, L.; He, Y.; Schaake, J. C.
2016-12-01
Wind is an important weather variable and a key determinant of evaporation, snowfall and coastal flooding. At present, wind information from medium-range weather forecast is of limited accuracy, and the associated resolution is often too coarse to be used directly for hydrologic prediction purposes. This work presents a statistical post-processing framework that will be used to generate fine-scale wind products to serve the NOAA's National Water Model effort. The prototype of this framework consists of two components: a) a cluster analysis module that classifies Automated Surface Observing System (ASOS) stations into multiple groups based on elevation and/or surface roughness lengths derived from National Land Cover Database 2011 (NLCD2011), and b) a regression module based on the Heteroscedastic Extended Logistic Regression (HXLR) technique that statistically downscales GEFS wind hindcasts to the location of the closest station within each identified cluster. The efficacy of the framework is assessed for a region that is roughly the service area of NOAA's Middle Atlantic River Forecast Center (MARFC). For this region, wind hindcasts generated from Global Ensemble Forecast System (GEFS) are downscaled and corrected using digital elevation model and National Land Cover Database; observations from ASOS serve both as the predictands for establishing the relationship, and as the reference for validation. Our results showed that this framework considerably enhance the quality of wind forecast, with Nash-Sutcliffe efficiency of the downscaled wind speed improved by 0.2 - 0.4 relative to raw GEFS forecast.
Optical proximity correction with principal component regression
Gao, Peiran; Gu, Allan; Zakhor, Avideh
2008-03-01
An important step in today's Integrated Circuit (IC) manufacturing is optical proximity correction (OPC). In model based OPC, masks are systematically modified to compensate for the non-ideal optical and process effects of optical lithography system. The polygons in the layout are fragmented, and simulations are performed to determine the image intensity pattern on the wafer. Then the mask is perturbed by moving the fragments to match the desired wafer pattern. This iterative process continues until the pattern on the wafer matches the desired one. Although OPC increases the fidelity of pattern transfer to the wafer, it is quite CPU intensive; OPC for modern IC designs can take days to complete on computer clusters with thousands of CPU. In this paper, techniques from statistical machine learning are used to predict the fragment movements. The goal is to reduce the number of iterations required in model based OPC by using a fast and efficient solution as the initial guess to model based OPC. To determine the best model, we train and evaluate several principal component regression models based on prediction error. Experimental results show that fragment movement predictions via regression model significantly decrease the number of iterations required in model based OPC.
A reconsideration of the concept of regression.
Dowling, A Scott
2004-01-01
Regression has been a useful psychoanalytic concept, linking present mental functioning with past experiences and levels of functioning. The concept originated as an extension of the evolutionary zeitgeist of the day as enunciated by H. Spencer and H. Jackson and applied by Freud to psychological phenomena. The value system implicit in the contrast of evolution/progression vs dissolution/regression has given rise to unfortunate and powerful assumptions of social, cultural, developmental and individual value as embodied in notions of "higher," "lower;" "primitive," "mature," "archaic," and "advanced." The unhelpful results of these assumptions are evident, for example, in attitudes concerning cultural, sexual, and social "correctness, " same-sex object choice, and goals of treatment. An alternative, a continuously constructed, continuously emerging mental life, in analogy to the ever changing, continuous physical body, is suggested. This view retains the fundamentals of psychoanalysis, for example, unconscious mental life, drive, defense, and psychic structure, but stresses a functional, ever changing, present oriented understanding of mental life as contrasted with a static, onion-layered view.
Regression trees for regulatory element identification.
Phuong, Tu Minh; Lee, Doheon; Lee, Kwang Hyung
2004-03-22
The transcription of a gene is largely determined by short sequence motifs that serve as binding sites for transcription factors. Recent findings suggest direct relationships between the motifs and gene expression levels. In this work, we present a method for identifying regulatory motifs. Our method makes use of tree-based techniques for recovering the relationships between motifs and gene expression levels. We treat regulatory motifs and gene expression levels as predictor variables and responses, respectively, and use a regression tree model to identify the structural relationships between them. The regression tree methodology is extended to handle responses from multiple experiments by modifying the split function. The significance of regulatory elements is determined by analyzing tree structures and using a variable importance measure. When applied to two data sets of the yeast Saccharomyces cerevisiae, the method successfully identifies most of the regulatory motifs that are known to control gene transcription under the given experimental conditions, and suggests several new putative motifs. Analysis of the tree structures also reconfirms several pairs of motifs that are known to regulate gene transcription in combination. http://if.kaist.ac.kr/~phuong/RegTree
Ogutu, Joseph O; Schulz-Streeck, Torben; Piepho, Hans-Peter
2012-05-21
Genomic selection (GS) is emerging as an efficient and cost-effective method for estimating breeding values using molecular markers distributed over the entire genome. In essence, it involves estimating the simultaneous effects of all genes or chromosomal segments and combining the estimates to predict the total genomic breeding value (GEBV). Accurate prediction of GEBVs is a central and recurring challenge in plant and animal breeding. The existence of a bewildering array of approaches for predicting breeding values using markers underscores the importance of identifying approaches able to efficiently and accurately predict breeding values. Here, we comparatively evaluate the predictive performance of six regularized linear regression methods-- ridge regression, ridge regression BLUP, lasso, adaptive lasso, elastic net and adaptive elastic net-- for predicting GEBV using dense SNP markers. We predicted GEBVs for a quantitative trait using a dataset on 3000 progenies of 20 sires and 200 dams and an accompanying genome consisting of five chromosomes with 9990 biallelic SNP-marker loci simulated for the QTL-MAS 2011 workshop. We applied all the six methods that use penalty-based (regularization) shrinkage to handle datasets with far more predictors than observations. The lasso, elastic net and their adaptive extensions further possess the desirable property that they simultaneously select relevant predictive markers and optimally estimate their effects. The regression models were trained with a subset of 2000 phenotyped and genotyped individuals and used to predict GEBVs for the remaining 1000 progenies without phenotypes. Predictive accuracy was assessed using the root mean squared error, the Pearson correlation between predicted GEBVs and (1) the true genomic value (TGV), (2) the true breeding value (TBV) and (3) the simulated phenotypic values based on fivefold cross-validation (CV). The elastic net, lasso, adaptive lasso and the adaptive elastic net all had
Interpreting parameters in the logistic regression model with random effects
DEFF Research Database (Denmark)
Larsen, Klaus; Petersen, Jørgen Holm; Budtz-Jørgensen, Esben
2000-01-01
interpretation, interval odds ratio, logistic regression, median odds ratio, normally distributed random effects......interpretation, interval odds ratio, logistic regression, median odds ratio, normally distributed random effects...
Logistic regression against a divergent Bayesian network
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Noel Antonio Sánchez Trujillo
2015-01-01
Full Text Available This article is a discussion about two statistical tools used for prediction and causality assessment: logistic regression and Bayesian networks. Using data of a simulated example from a study assessing factors that might predict pulmonary emphysema (where fingertip pigmentation and smoking are considered; we posed the following questions. Is pigmentation a confounding, causal or predictive factor? Is there perhaps another factor, like smoking, that confounds? Is there a synergy between pigmentation and smoking? The results, in terms of prediction, are similar with the two techniques; regarding causation, differences arise. We conclude that, in decision-making, the sum of both: a statistical tool, used with common sense, and previous evidence, taking years or even centuries to develop; is better than the automatic and exclusive use of statistical resources.
Statistical learning from a regression perspective
Berk, Richard A
2016-01-01
This textbook considers statistical learning applications when interest centers on the conditional distribution of the response variable, given a set of predictors, and when it is important to characterize how the predictors are related to the response. As a first approximation, this can be seen as an extension of nonparametric regression. This fully revised new edition includes important developments over the past 8 years. Consistent with modern data analytics, it emphasizes that a proper statistical learning data analysis derives from sound data collection, intelligent data management, appropriate statistical procedures, and an accessible interpretation of results. A continued emphasis on the implications for practice runs through the text. Among the statistical learning procedures examined are bagging, random forests, boosting, support vector machines and neural networks. Response variables may be quantitative or categorical. As in the first edition, a unifying theme is supervised learning that can be trea...
Robust mediation analysis based on median regression.
Yuan, Ying; Mackinnon, David P
2014-03-01
Mediation analysis has many applications in psychology and the social sciences. The most prevalent methods typically assume that the error distribution is normal and homoscedastic. However, this assumption may rarely be met in practice, which can affect the validity of the mediation analysis. To address this problem, we propose robust mediation analysis based on median regression. Our approach is robust to various departures from the assumption of homoscedasticity and normality, including heavy-tailed, skewed, contaminated, and heteroscedastic distributions. Simulation studies show that under these circumstances, the proposed method is more efficient and powerful than standard mediation analysis. We further extend the proposed robust method to multilevel mediation analysis, and demonstrate through simulation studies that the new approach outperforms the standard multilevel mediation analysis. We illustrate the proposed method using data from a program designed to increase reemployment and enhance mental health of job seekers. (c) 2014 APA, all rights reserved.
Adaptive regression for modeling nonlinear relationships
Knafl, George J
2016-01-01
This book presents methods for investigating whether relationships are linear or nonlinear and for adaptively fitting appropriate models when they are nonlinear. Data analysts will learn how to incorporate nonlinearity in one or more predictor variables into regression models for different types of outcome variables. Such nonlinear dependence is often not considered in applied research, yet nonlinear relationships are common and so need to be addressed. A standard linear analysis can produce misleading conclusions, while a nonlinear analysis can provide novel insights into data, not otherwise possible. A variety of examples of the benefits of modeling nonlinear relationships are presented throughout the book. Methods are covered using what are called fractional polynomials based on real-valued power transformations of primary predictor variables combined with model selection based on likelihood cross-validation. The book covers how to formulate and conduct such adaptive fractional polynomial modeling in the s...
Conjoined legs: Sirenomelia or caudal regression syndrome?
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Sakti Prasad Das
2013-01-01
Full Text Available Presence of single umbilical persistent vitelline artery distinguishes sirenomelia from caudal regression syndrome. We report a case of a12-year-old boy who had bilateral umbilical arteries presented with fusion of both legs in the lower one third of leg. Both feet were rudimentary. The right foot had a valgus rocker-bottom deformity. All toes were present but rudimentary. The left foot showed absence of all toes. Physical examination showed left tibia vara. The chest evaluation in sitting revealed pigeon chest and elevated right shoulder. Posterior examination of the trunk showed thoracic scoliosis with convexity to right. The patient was operated and at 1 year followup the boy had two separate legs with a good aesthetic and functional results.
Macrophages, Dendritic Cells, and Regression of Atherosclerosis
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Jonathan E. Feig
2012-07-01
Full Text Available Atherosclerosis is the number one cause of death in the Western world. It results from the interaction between modified lipoproteins and monocyte-derived cells such as macrophages, dendritic cells, T cells, and other cellular elements of the arterial wall. This inflammatory process can ultimately lead to the development of complex lesions, or plaques, that protrude into the arterial lumen. Ultimately, plaque rupture and thrombosis can occur leading to the clinical complications of myocardial infarction or stroke. Although each of the cell types plays roles in the pathogenesis of atherosclerosis, in this review, the focus will be primarily on the monocyte derived cells- macrophages and dendritic cells. The roles of these cell types in atherogenesis will be highlighted. Finally, the mechanisms of atherosclerosis regression as it relates to these cells will be discussed.
Nonparametric additive regression for repeatedly measured data
Carroll, R. J.
2009-05-20
We develop an easily computed smooth backfitting algorithm for additive model fitting in repeated measures problems. Our methodology easily copes with various settings, such as when some covariates are the same over repeated response measurements. We allow for a working covariance matrix for the regression errors, showing that our method is most efficient when the correct covariance matrix is used. The component functions achieve the known asymptotic variance lower bound for the scalar argument case. Smooth backfitting also leads directly to design-independent biases in the local linear case. Simulations show our estimator has smaller variance than the usual kernel estimator. This is also illustrated by an example from nutritional epidemiology. © 2009 Biometrika Trust.
Early development and regression in Rett syndrome.
Lee, J Y L; Leonard, H; Piek, J P; Downs, J
2013-12-01
This study utilized developmental profiling to examine symptoms in 14 girls with genetically confirmed Rett syndrome and whose families were participating in the Australian Rett syndrome or InterRett database. Regression was mostly characterized by loss of hand and/or communication skills (13/14) except one girl demonstrated slowing of skill development. Social withdrawal and inconsolable crying often developed simultaneously (9/14), with social withdrawal for shorter duration than inconsolable crying. Previously acquired gross motor skills declined in just over half of the sample (8/14), mostly observed as a loss of balance. Early abnormalities such as vomiting and strabismus were also seen. Our findings provide additional insight into the early clinical profile of Rett syndrome. © 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
Entrepreneurial intention modeling using hierarchical multiple regression
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Marina Jeger
2014-12-01
Full Text Available The goal of this study is to identify the contribution of effectuation dimensions to the predictive power of the entrepreneurial intention model over and above that which can be accounted for by other predictors selected and confirmed in previous studies. As is often the case in social and behavioral studies, some variables are likely to be highly correlated with each other. Therefore, the relative amount of variance in the criterion variable explained by each of the predictors depends on several factors such as the order of variable entry and sample specifics. The results show the modest predictive power of two dimensions of effectuation prior to the introduction of the theory of planned behavior elements. The article highlights the main advantages of applying hierarchical regression in social sciences as well as in the specific context of entrepreneurial intention formation, and addresses some of the potential pitfalls that this type of analysis entails.
DRREP: deep ridge regressed epitope predictor.
Sher, Gene; Zhi, Degui; Zhang, Shaojie
2017-10-03
The ability to predict epitopes plays an enormous role in vaccine development in terms of our ability to zero in on where to do a more thorough in-vivo analysis of the protein in question. Though for the past decade there have been numerous advancements and improvements in epitope prediction, on average the best benchmark prediction accuracies are still only around 60%. New machine learning algorithms have arisen within the domain of deep learning, text mining, and convolutional networks. This paper presents a novel analytically trained and string kernel using deep neural network, which is tailored for continuous epitope prediction, called: Deep Ridge Regressed Epitope Predictor (DRREP). DRREP was tested on long protein sequences from the following datasets: SARS, Pellequer, HIV, AntiJen, and SEQ194. DRREP was compared to numerous state of the art epitope predictors, including the most recently published predictors called LBtope and DMNLBE. Using area under ROC curve (AUC), DRREP achieved a performance improvement over the best performing predictors on SARS (13.7%), HIV (8.9%), Pellequer (1.5%), and SEQ194 (3.1%), with its performance being matched only on the AntiJen dataset, by the LBtope predictor, where both DRREP and LBtope achieved an AUC of 0.702. DRREP is an analytically trained deep neural network, thus capable of learning in a single step through regression. By combining the features of deep learning, string kernels, and convolutional networks, the system is able to perform residue-by-residue prediction of continues epitopes with higher accuracy than the current state of the art predictors.
Wheeler, David; Tiefelsdorf, Michael
2005-06-01
Present methodological research on geographically weighted regression (GWR) focuses primarily on extensions of the basic GWR model, while ignoring well-established diagnostics tests commonly used in standard global regression analysis. This paper investigates multicollinearity issues surrounding the local GWR coefficients at a single location and the overall correlation between GWR coefficients associated with two different exogenous variables. Results indicate that the local regression coefficients are potentially collinear even if the underlying exogenous variables in the data generating process are uncorrelated. Based on these findings, applied GWR research should practice caution in substantively interpreting the spatial patterns of local GWR coefficients. An empirical disease-mapping example is used to motivate the GWR multicollinearity problem. Controlled experiments are performed to systematically explore coefficient dependency issues in GWR. These experiments specify global models that use eigenvectors from a spatial link matrix as exogenous variables.
Logistic regression applied to natural hazards: rare event logistic regression with replications
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M. Guns
2012-06-01
Full Text Available Statistical analysis of natural hazards needs particular attention, as most of these phenomena are rare events. This study shows that the ordinary rare event logistic regression, as it is now commonly used in geomorphologic studies, does not always lead to a robust detection of controlling factors, as the results can be strongly sample-dependent. In this paper, we introduce some concepts of Monte Carlo simulations in rare event logistic regression. This technique, so-called rare event logistic regression with replications, combines the strength of probabilistic and statistical methods, and allows overcoming some of the limitations of previous developments through robust variable selection. This technique was here developed for the analyses of landslide controlling factors, but the concept is widely applicable for statistical analyses of natural hazards.
Logistic regression applied to natural hazards: rare event logistic regression with replications
Guns, M.; Vanacker, V.
2012-06-01
Statistical analysis of natural hazards needs particular attention, as most of these phenomena are rare events. This study shows that the ordinary rare event logistic regression, as it is now commonly used in geomorphologic studies, does not always lead to a robust detection of controlling factors, as the results can be strongly sample-dependent. In this paper, we introduce some concepts of Monte Carlo simulations in rare event logistic regression. This technique, so-called rare event logistic regression with replications, combines the strength of probabilistic and statistical methods, and allows overcoming some of the limitations of previous developments through robust variable selection. This technique was here developed for the analyses of landslide controlling factors, but the concept is widely applicable for statistical analyses of natural hazards.
Smith, Paul F; Ganesh, Siva; Liu, Ping
2013-10-30
Regression is a common statistical tool for prediction in neuroscience. However, linear regression is by far the most common form of regression used, with regression trees receiving comparatively little attention. In this study, the results of conventional multiple linear regression (MLR) were compared with those of random forest regression (RFR), in the prediction of the concentrations of 9 neurochemicals in the vestibular nucleus complex and cerebellum that are part of the l-arginine biochemical pathway (agmatine, putrescine, spermidine, spermine, l-arginine, l-ornithine, l-citrulline, glutamate and γ-aminobutyric acid (GABA)). The R(2) values for the MLRs were higher than the proportion of variance explained values for the RFRs: 6/9 of them were ≥ 0.70 compared to 4/9 for RFRs. Even the variables that had the lowest R(2) values for the MLRs, e.g. ornithine (0.50) and glutamate (0.61), had much lower proportion of variance explained values for the RFRs (0.27 and 0.49, respectively). The RSE values for the MLRs were lower than those for the RFRs in all but two cases. In general, MLRs seemed to be superior to the RFRs in terms of predictive value and error. In the case of this data set, MLR appeared to be superior to RFR in terms of its explanatory value and error. This result suggests that MLR may have advantages over RFR for prediction in neuroscience with this kind of data set, but that RFR can still have good predictive value in some cases. Copyright © 2013 Elsevier B.V. All rights reserved.
Ridge regression estimator: combining unbiased and ordinary ridge regression methods of estimation
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Sharad Damodar Gore
2009-10-01
Full Text Available Statistical literature has several methods for coping with multicollinearity. This paper introduces a new shrinkage estimator, called modified unbiased ridge (MUR. This estimator is obtained from unbiased ridge regression (URR in the same way that ordinary ridge regression (ORR is obtained from ordinary least squares (OLS. Properties of MUR are derived. Results on its matrix mean squared error (MMSE are obtained. MUR is compared with ORR and URR in terms of MMSE. These results are illustrated with an example based on data generated by Hoerl and Kennard (1975.
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Hong-Juan Li
2013-04-01
Full Text Available Electric load forecasting is an important issue for a power utility, associated with the management of daily operations such as energy transfer scheduling, unit commitment, and load dispatch. Inspired by strong non-linear learning capability of support vector regression (SVR, this paper presents a SVR model hybridized with the empirical mode decomposition (EMD method and auto regression (AR for electric load forecasting. The electric load data of the New South Wales (Australia market are employed for comparing the forecasting performances of different forecasting models. The results confirm the validity of the idea that the proposed model can simultaneously provide forecasting with good accuracy and interpretability.
Spatial vulnerability assessments by regression kriging
Pásztor, László; Laborczi, Annamária; Takács, Katalin; Szatmári, Gábor
2016-04-01
information representing IEW or GRP forming environmental factors were taken into account to support the spatial inference of the locally experienced IEW frequency and measured GRP values respectively. An efficient spatial prediction methodology was applied to construct reliable maps, namely regression kriging (RK) using spatially exhaustive auxiliary data on soil, geology, topography, land use and climate. RK divides the spatial inference into two parts. Firstly the deterministic component of the target variable is determined by a regression model. The residuals of the multiple linear regression analysis represent the spatially varying but dependent stochastic component, which are interpolated by kriging. The final map is the sum of the two component predictions. Application of RK also provides the possibility of inherent accuracy assessment. The resulting maps are characterized by global and local measures of its accuracy. Additionally the method enables interval estimation for spatial extension of the areas of predefined risk categories. All of these outputs provide useful contribution to spatial planning, action planning and decision making. Acknowledgement: Our work was partly supported by the Hungarian National Scientific Research Foundation (OTKA, Grant No. K105167).
Automation of Flight Software Regression Testing
Tashakkor, Scott B.
2016-01-01
NASA is developing the Space Launch System (SLS) to be a heavy lift launch vehicle supporting human and scientific exploration beyond earth orbit. SLS will have a common core stage, an upper stage, and different permutations of boosters and fairings to perform various crewed or cargo missions. Marshall Space Flight Center (MSFC) is writing the Flight Software (FSW) that will operate the SLS launch vehicle. The FSW is developed in an incremental manner based on "Agile" software techniques. As the FSW is incrementally developed, testing the functionality of the code needs to be performed continually to ensure that the integrity of the software is maintained. Manually testing the functionality on an ever-growing set of requirements and features is not an efficient solution and therefore needs to be done automatically to ensure testing is comprehensive. To support test automation, a framework for a regression test harness has been developed and used on SLS FSW. The test harness provides a modular design approach that can compile or read in the required information specified by the developer of the test. The modularity provides independence between groups of tests and the ability to add and remove tests without disturbing others. This provides the SLS FSW team a time saving feature that is essential to meeting SLS Program technical and programmatic requirements. During development of SLS FSW, this technique has proved to be a useful tool to ensure all requirements have been tested, and that desired functionality is maintained, as changes occur. It also provides a mechanism for developers to check functionality of the code that they have developed. With this system, automation of regression testing is accomplished through a scheduling tool and/or commit hooks. Key advantages of this test harness capability includes execution support for multiple independent test cases, the ability for developers to specify precisely what they are testing and how, the ability to add
A Nonparametric Bayesian Methodology for Regression Discontinuity Designs
Branson, Zach; Rischard, Maxime; Bornn, Luke; Miratrix, Luke
2017-01-01
One of the most popular methodologies for estimating the average treatment effect at the threshold in a regression discontinuity design is local linear regression (LLR), which places larger weight on units closer to the threshold. We propose a Gaussian process regression method that acts as a Bayesian analog to LLR for sharp regression discontinuity designs. Our Gaussian process regression method provides a flexible fit for treatment and control responses by placing a general prior on the mea...
Directory of Open Access Journals (Sweden)
Qiutong Jin
2016-06-01
Full Text Available Estimating the spatial distribution of precipitation is an important and challenging task in hydrology, climatology, ecology, and environmental science. In order to generate a highly accurate distribution map of average annual precipitation for the Loess Plateau in China, multiple linear regression Kriging (MLRK and geographically weighted regression Kriging (GWRK methods were employed using precipitation data from the period 1980–2010 from 435 meteorological stations. The predictors in regression Kriging were selected by stepwise regression analysis from many auxiliary environmental factors, such as elevation (DEM, normalized difference vegetation index (NDVI, solar radiation, slope, and aspect. All predictor distribution maps had a 500 m spatial resolution. Validation precipitation data from 130 hydrometeorological stations were used to assess the prediction accuracies of the MLRK and GWRK approaches. Results showed that both prediction maps with a 500 m spatial resolution interpolated by MLRK and GWRK had a high accuracy and captured detailed spatial distribution data; however, MLRK produced a lower prediction error and a higher variance explanation than GWRK, although the differences were small, in contrast to conclusions from similar studies.
Boosted Regression Tree Models to Explain Watershed ...
Boosted regression tree (BRT) models were developed to quantify the nonlinear relationships between landscape variables and nutrient concentrations in a mesoscale mixed land cover watershed during base-flow conditions. Factors that affect instream biological components, based on the Index of Biotic Integrity (IBI), were also analyzed. Seasonal BRT models at two spatial scales (watershed and riparian buffered area [RBA]) for nitrite-nitrate (NO2-NO3), total Kjeldahl nitrogen, and total phosphorus (TP) and annual models for the IBI score were developed. Two primary factors — location within the watershed (i.e., geographic position, stream order, and distance to a downstream confluence) and percentage of urban land cover (both scales) — emerged as important predictor variables. Latitude and longitude interacted with other factors to explain the variability in summer NO2-NO3 concentrations and IBI scores. BRT results also suggested that location might be associated with indicators of sources (e.g., land cover), runoff potential (e.g., soil and topographic factors), and processes not easily represented by spatial data indicators. Runoff indicators (e.g., Hydrological Soil Group D and Topographic Wetness Indices) explained a substantial portion of the variability in nutrient concentrations as did point sources for TP in the summer months. The results from our BRT approach can help prioritize areas for nutrient management in mixed-use and heavily impacted watershed
A rotor optimization using regression analysis
Giansante, N.
1984-01-01
The design and development of helicopter rotors is subject to the many design variables and their interactions that effect rotor operation. Until recently, selection of rotor design variables to achieve specified rotor operational qualities has been a costly, time consuming, repetitive task. For the past several years, Kaman Aerospace Corporation has successfully applied multiple linear regression analysis, coupled with optimization and sensitivity procedures, in the analytical design of rotor systems. It is concluded that approximating equations can be developed rapidly for a multiplicity of objective and constraint functions and optimizations can be performed in a rapid and cost effective manner; the number and/or range of design variables can be increased by expanding the data base and developing approximating functions to reflect the expanded design space; the order of the approximating equations can be expanded easily to improve correlation between analyzer results and the approximating equations; gradients of the approximating equations can be calculated easily and these gradients are smooth functions reducing the risk of numerical problems in the optimization; the use of approximating functions allows the problem to be started easily and rapidly from various initial designs to enhance the probability of finding a global optimum; and the approximating equations are independent of the analysis or optimization codes used.
Gravitational Wave Emulation Using Gaussian Process Regression
Doctor, Zoheyr; Farr, Ben; Holz, Daniel
2017-01-01
Parameter estimation (PE) for gravitational wave signals from compact binary coalescences (CBCs) requires reliable template waveforms which span the parameter space. Waveforms from numerical relativity are accurate but computationally expensive, so approximate templates are typically used for PE. These `approximants', while quick to compute, can introduce systematic errors and bias PE results. We describe a machine learning method for generating CBC waveforms and uncertainties using existing accurate waveforms as a training set. Coefficients of a reduced order waveform model are computed and each treated as arising from a Gaussian process. These coefficients and their uncertainties are then interpolated using Gaussian process regression (GPR). As a proof of concept, we construct a training set of approximant waveforms (rather than NR waveforms) in the two-dimensional space of chirp mass and mass ratio and interpolate new waveforms with GPR. We demonstrate that the mismatch between interpolated waveforms and approximants is below the 1% level for an appropriate choice of training set and GPR kernel hyperparameters.
Spline regression hashing for fast image search.
Liu, Yang; Wu, Fei; Yang, Yi; Zhuang, Yueting; Hauptmann, Alexander G
2012-10-01
Techniques for fast image retrieval over large databases have attracted considerable attention due to the rapid growth of web images. One promising way to accelerate image search is to use hashing technologies, which represent images by compact binary codewords. In this way, the similarity between images can be efficiently measured in terms of the Hamming distance between their corresponding binary codes. Although plenty of methods on generating hash codes have been proposed in recent years, there are still two key points that needed to be improved: 1) how to precisely preserve the similarity structure of the original data and 2) how to obtain the hash codes of the previously unseen data. In this paper, we propose our spline regression hashing method, in which both the local and global data similarity structures are exploited. To better capture the local manifold structure, we introduce splines developed in Sobolev space to find the local data mapping function. Furthermore, our framework simultaneously learns the hash codes of the training data and the hash function for the unseen data, which solves the out-of-sample problem. Extensive experiments conducted on real image datasets consisting of over one million images show that our proposed method outperforms the state-of-the-art techniques.
Interactive natural image segmentation via spline regression.
Xiang, Shiming; Nie, Feiping; Zhang, Chunxia; Zhang, Changshui
2009-07-01
This paper presents an interactive algorithm for segmentation of natural images. The task is formulated as a problem of spline regression, in which the spline is derived in Sobolev space and has a form of a combination of linear and Green's functions. Besides its nonlinear representation capability, one advantage of this spline in usage is that, once it has been constructed, no parameters need to be tuned to data. We define this spline on the user specified foreground and background pixels, and solve its parameters (the combination coefficients of functions) from a group of linear equations. To speed up spline construction, K-means clustering algorithm is employed to cluster the user specified pixels. By taking the cluster centers as representatives, this spline can be easily constructed. The foreground object is finally cut out from its background via spline interpolation. The computational complexity of the proposed algorithm is linear in the number of the pixels to be segmented. Experiments on diverse natural images, with comparison to existing algorithms, illustrate the validity of our method.
Free Software Development. 1. Fitting Statistical Regressions
Directory of Open Access Journals (Sweden)
Lorentz JÄNTSCHI
2002-12-01
Full Text Available The present paper is focused on modeling of statistical data processing with applications in field of material science and engineering. A new method of data processing is presented and applied on a set of 10 Ni–Mn–Ga ferromagnetic ordered shape memory alloys that are known to exhibit phonon softening and soft mode condensation into a premartensitic phase prior to the martensitic transformation itself. The method allows to identify the correlations between data sets and to exploit them later in statistical study of alloys. An algorithm for computing data was implemented in preprocessed hypertext language (PHP, a hypertext markup language interface for them was also realized and put onto comp.east.utcluj.ro educational web server, and it is accessible via http protocol at the address http://vl.academicdirect.ro/applied_statistics/linear_regression/multiple/v1.5/. The program running for the set of alloys allow to identify groups of alloys properties and give qualitative measure of correlations between properties. Surfaces of property dependencies are also fitted.
2012-01-01
Background Genomic selection (GS) is emerging as an efficient and cost-effective method for estimating breeding values using molecular markers distributed over the entire genome. In essence, it involves estimating the simultaneous effects of all genes or chromosomal segments and combining the estimates to predict the total genomic breeding value (GEBV). Accurate prediction of GEBVs is a central and recurring challenge in plant and animal breeding. The existence of a bewildering array of approaches for predicting breeding values using markers underscores the importance of identifying approaches able to efficiently and accurately predict breeding values. Here, we comparatively evaluate the predictive performance of six regularized linear regression methods-- ridge regression, ridge regression BLUP, lasso, adaptive lasso, elastic net and adaptive elastic net-- for predicting GEBV using dense SNP markers. Methods We predicted GEBVs for a quantitative trait using a dataset on 3000 progenies of 20 sires and 200 dams and an accompanying genome consisting of five chromosomes with 9990 biallelic SNP-marker loci simulated for the QTL-MAS 2011 workshop. We applied all the six methods that use penalty-based (regularization) shrinkage to handle datasets with far more predictors than observations. The lasso, elastic net and their adaptive extensions further possess the desirable property that they simultaneously select relevant predictive markers and optimally estimate their effects. The regression models were trained with a subset of 2000 phenotyped and genotyped individuals and used to predict GEBVs for the remaining 1000 progenies without phenotypes. Predictive accuracy was assessed using the root mean squared error, the Pearson correlation between predicted GEBVs and (1) the true genomic value (TGV), (2) the true breeding value (TBV) and (3) the simulated phenotypic values based on fivefold cross-validation (CV). Results The elastic net, lasso, adaptive lasso and the
Deep Human Parsing with Active Template Regression.
Liang, Xiaodan; Liu, Si; Shen, Xiaohui; Yang, Jianchao; Liu, Luoqi; Dong, Jian; Lin, Liang; Yan, Shuicheng
2015-12-01
In this work, the human parsing task, namely decomposing a human image into semantic fashion/body regions, is formulated as an active template regression (ATR) problem, where the normalized mask of each fashion/body item is expressed as the linear combination of the learned mask templates, and then morphed to a more precise mask with the active shape parameters, including position, scale and visibility of each semantic region. The mask template coefficients and the active shape parameters together can generate the human parsing results, and are thus called the structure outputs for human parsing. The deep Convolutional Neural Network (CNN) is utilized to build the end-to-end relation between the input human image and the structure outputs for human parsing. More specifically, the structure outputs are predicted by two separate networks. The first CNN network is with max-pooling, and designed to predict the template coefficients for each label mask, while the second CNN network is without max-pooling to preserve sensitivity to label mask position and accurately predict the active shape parameters. For a new image, the structure outputs of the two networks are fused to generate the probability of each label for each pixel, and super-pixel smoothing is finally used to refine the human parsing result. Comprehensive evaluations on a large dataset well demonstrate the significant superiority of the ATR framework over other state-of-the-arts for human parsing. In particular, the F1-score reaches 64.38 percent by our ATR framework, significantly higher than 44.76 percent based on the state-of-the-art algorithm [28].
Detection of epistatic effects with logic regression and a classical linear regression model.
Malina, Magdalena; Ickstadt, Katja; Schwender, Holger; Posch, Martin; Bogdan, Małgorzata
2014-02-01
To locate multiple interacting quantitative trait loci (QTL) influencing a trait of interest within experimental populations, usually methods as the Cockerham's model are applied. Within this framework, interactions are understood as the part of the joined effect of several genes which cannot be explained as the sum of their additive effects. However, if a change in the phenotype (as disease) is caused by Boolean combinations of genotypes of several QTLs, this Cockerham's approach is often not capable to identify them properly. To detect such interactions more efficiently, we propose a logic regression framework. Even though with the logic regression approach a larger number of models has to be considered (requiring more stringent multiple testing correction) the efficient representation of higher order logic interactions in logic regression models leads to a significant increase of power to detect such interactions as compared to a Cockerham's approach. The increase in power is demonstrated analytically for a simple two-way interaction model and illustrated in more complex settings with simulation study and real data analysis.
Technological Forecasting with a Multiple Regression Analysis Approach.
Luftig, Jeffrey T.; Norton, Willis P.
1981-01-01
This article examines simple and multiple regression analysis as forecasting tools, and details the process by which multiple regression analysis may be used to increase the accuracy of the technology forecast. (CT)
Dimension Reduction and Discretization in Stochastic Problems by Regression Method
DEFF Research Database (Denmark)
Ditlevsen, Ove Dalager
1996-01-01
The chapter mainly deals with dimension reduction and field discretizations based directly on the concept of linear regression. Several examples of interesting applications in stochastic mechanics are also given.Keywords: Random fields discretization, Linear regression, Stochastic interpolation...
An Additive-Multiplicative Cox-Aalen Regression Model
DEFF Research Database (Denmark)
Scheike, Thomas H.; Zhang, Mei-Jie
2002-01-01
Aalen model; additive risk model; counting processes; Cox regression; survival analysis; time-varying effects......Aalen model; additive risk model; counting processes; Cox regression; survival analysis; time-varying effects...
Applied Multiple Linear Regression: A General Research Strategy
Smith, Brandon B.
1969-01-01
Illustrates some of the basic concepts and procedures for using regression analysis in experimental design, analysis of variance, analysis of covariance, and curvilinear regression. Applications to evaluation of instruction and vocational education programs are illustrated. (GR)
Data analysis using regression and multilevel/hierarchical models
National Research Council Canada - National Science Library
Gelman, Andrew; Hill, Jennifer
2007-01-01
"Data Analysis Using Regression and Multilevel/Hierarchical Models is a comprehensive manual for the applied researcher who wants to perform data analysis using linear and nonlinear regression and multilevel models...
Security Regression Testing Framework For Web Application Development
Waheed, Usman
2014-01-01
A framework and process that explains how to perform security regression testing for web applications. This paper discusses and proposes a framework based on open source tools that can be used to perform automated security regression testing of web applications.
An Overview on Regression Models for Discrete Longitudinal Responses
Sutradhar, Brajendra C.
2003-01-01
In the longitudinal regression setup, interest may be focused primarily on the regression parameters for the marginal expectations of the longitudinal responses, the longitudinal correlation parameters being of secondary interest. Second, interest may be focused on both the regression and the longitudinal correlation parameters. Under the first setup, there exists a "working'' correlation matrix based generalized estimating equation (GEE) approach for the estimation of the regression paramete...
"A regression error specification test (RESET) for generalized linear models".
Sunil Sapra
2005-01-01
Generalized linear models (GLMs) are generalizations of linear regression models, which allow fitting regression models to response data that follow a general exponential family. GLMs are used widely in social sciences for fitting regression models to count data, qualitative response data and duration data. While a variety of specification tests have been developed for the linear regression model and are routinely applied for testing for misspecification of functional form, omitted variables,...
Using Dominance Analysis to Determine Predictor Importance in Logistic Regression
Azen, Razia; Traxel, Nicole
2009-01-01
This article proposes an extension of dominance analysis that allows researchers to determine the relative importance of predictors in logistic regression models. Criteria for choosing logistic regression R[superscript 2] analogues were determined and measures were selected that can be used to perform dominance analysis in logistic regression. A…
Meta-Modeling by Symbolic Regression and Pareto Simulated Annealing
Stinstra, E.; Rennen, G.; Teeuwen, G.J.A.
2006-01-01
The subject of this paper is a new approach to Symbolic Regression.Other publications on Symbolic Regression use Genetic Programming.This paper describes an alternative method based on Pareto Simulated Annealing.Our method is based on linear regression for the estimation of constants.Interval
Orthogonal Projection in Teaching Regression and Financial Mathematics
Kachapova, Farida; Kachapov, Ilias
2010-01-01
Two improvements in teaching linear regression are suggested. The first is to include the population regression model at the beginning of the topic. The second is to use a geometric approach: to interpret the regression estimate as an orthogonal projection and the estimation error as the distance (which is minimized by the projection). Linear…
Assessment of School Merit with Multiple Regression: Methods and Critique.
Tate, Richard L.
1986-01-01
Regression-based adjustment of student outcomes for the assessment of the merit of schools is considered. First, the basics of causal modeling and multiple regression are briefly reviewed. Then, two common regression-based adjustment procedures are described, pointing out that the validity of the final assessments depends on: (1) the degree to…
Li, Jiangtong; Luo, Yongdao; Dai, Honglin
2018-01-01
Water is the source of life and the essential foundation of all life. With the development of industrialization, the phenomenon of water pollution is becoming more and more frequent, which directly affects the survival and development of human. Water quality detection is one of the necessary measures to protect water resources. Ultraviolet (UV) spectral analysis is an important research method in the field of water quality detection, which partial least squares regression (PLSR) analysis method is becoming predominant technology, however, in some special cases, PLSR's analysis produce considerable errors. In order to solve this problem, the traditional principal component regression (PCR) analysis method was improved by using the principle of PLSR in this paper. The experimental results show that for some special experimental data set, improved PCR analysis method performance is better than PLSR. The PCR and PLSR is the focus of this paper. Firstly, the principal component analysis (PCA) is performed by MATLAB to reduce the dimensionality of the spectral data; on the basis of a large number of experiments, the optimized principal component is extracted by using the principle of PLSR, which carries most of the original data information. Secondly, the linear regression analysis of the principal component is carried out with statistic package for social science (SPSS), which the coefficients and relations of principal components can be obtained. Finally, calculating a same water spectral data set by PLSR and improved PCR, analyzing and comparing two results, improved PCR and PLSR is similar for most data, but improved PCR is better than PLSR for data near the detection limit. Both PLSR and improved PCR can be used in Ultraviolet spectral analysis of water, but for data near the detection limit, improved PCR's result better than PLSR.
Morales, Esteban; de Leon, John Mark S; Abdollahi, Niloufar; Yu, Fei; Nouri-Mahdavi, Kouros; Caprioli, Joseph
2016-03-01
The study was conducted to evaluate threshold smoothing algorithms to enhance prediction of the rates of visual field (VF) worsening in glaucoma. We studied 798 patients with primary open-angle glaucoma and 6 or more years of follow-up who underwent 8 or more VF examinations. Thresholds at each VF location for the first 4 years or first half of the follow-up time (whichever was greater) were smoothed with clusters defined by the nearest neighbor (NN), Garway-Heath, Glaucoma Hemifield Test (GHT), and weighting by the correlation of rates at all other VF locations. Thresholds were regressed with a pointwise exponential regression (PER) model and a pointwise linear regression (PLR) model. Smaller root mean square error (RMSE) values of the differences between the observed and the predicted thresholds at last two follow-ups indicated better model predictions. The mean (SD) follow-up times for the smoothing and prediction phase were 5.3 (1.5) and 10.5 (3.9) years. The mean RMSE values for the PER and PLR models were unsmoothed data, 6.09 and 6.55; NN, 3.40 and 3.42; Garway-Heath, 3.47 and 3.48; GHT, 3.57 and 3.74; and correlation of rates, 3.59 and 3.64. Smoothed VF data predicted better than unsmoothed data. Nearest neighbor provided the best predictions; PER also predicted consistently more accurately than PLR. Smoothing algorithms should be used when forecasting VF results with PER or PLR. The application of smoothing algorithms on VF data can improve forecasting in VF points to assist in treatment decisions.
Henderson, Daniel J.
2008-01-01
This paper presents a method to test for multimodality of an estimated kernel density of parameter estimates from a local-linear least-squares regression derivative. The procedure is laid out in seven simple steps and a suggestion for implementation is proposed. A Monte Carlo exercise is used to examine the finite sample properties of the test along with those from a calibrated version of it which corrects for the conservative nature of Silverman-type tests. The test is included in a study...
Morphodynamics of a cyclic prograding delta: the Red River, Vietnam
Maren, D.S. van
2004-01-01
River deltas are inhabited by over 60% of the world population, and are, consequently, of paramount agricultural and economical importance. They constitute unique wetland envi ronments which gives river deltas ecological importance as well. Additionally, many deltas contain large accumulations of
Sparse reduced-rank regression with covariance estimation
Chen, Lisha
2014-12-08
Improving the predicting performance of the multiple response regression compared with separate linear regressions is a challenging question. On the one hand, it is desirable to seek model parsimony when facing a large number of parameters. On the other hand, for certain applications it is necessary to take into account the general covariance structure for the errors of the regression model. We assume a reduced-rank regression model and work with the likelihood function with general error covariance to achieve both objectives. In addition we propose to select relevant variables for reduced-rank regression by using a sparsity-inducing penalty, and to estimate the error covariance matrix simultaneously by using a similar penalty on the precision matrix. We develop a numerical algorithm to solve the penalized regression problem. In a simulation study and real data analysis, the new method is compared with two recent methods for multivariate regression and exhibits competitive performance in prediction and variable selection.
Extrinsic local regression on manifold-valued data
Lin, Lizhen; St Thomas, Brian; Zhu, Hongtu; Dunson, David B.
2017-01-01
We propose an extrinsic regression framework for modeling data with manifold valued responses and Euclidean predictors. Regression with manifold responses has wide applications in shape analysis, neuroscience, medical imaging and many other areas. Our approach embeds the manifold where the responses lie onto a higher dimensional Euclidean space, obtains a local regression estimate in that space, and then projects this estimate back onto the image of the manifold. Outside the regression setting both intrinsic and extrinsic approaches have been proposed for modeling i.i.d manifold-valued data. However, to our knowledge our work is the first to take an extrinsic approach to the regression problem. The proposed extrinsic regression framework is general, computationally efficient and theoretically appealing. Asymptotic distributions and convergence rates of the extrinsic regression estimates are derived and a large class of examples are considered indicating the wide applicability of our approach. PMID:29225385
Qin, Li-Tang; Liu, Shu-Shen; Liu, Hai-Ling; Zhang, Yong-Hong
2010-01-01
Accurate description of hormetic dose-response curves (DRC) is a key step for the determination of the efficacy and hazards of the pollutants with the hormetic phenomenon. This study tries to use support vector regression (SVR) and least squares support vector regression (LS-SVR) to address the problem of curve fitting existing in hormesis. The SVR and LS-SVR, which are entirely different from the non-linear fitting methods used to describe hormetic effects based on large sample, are at present only optimum methods based on small sample often encountered in the experimental toxicology. The tuning parameters (C and p1 for SVR, gam and sig2 for LS-SVR) determining SVR and LS-SVR models were obtained by both the internal and external validation of the models. The internal validation was performed by using leave-one-out (LOO) cross-validation and the external validation was performed by splitting the whole data set (12 data points) into the same size (six data points) of training set and test set. The results show that SVR and LS-SVR can accurately describe not only for the hermetic J-shaped DRC of seven water-soluble organic solvents consisting of acetonitrile, methanol, ethanol, acetone, ether, tetrahydrofuran, and isopropanol, but also for the classical sigmoid DRC of six pesticides including simetryn, prometon, bromacil, velpar, diquat-dibromide monohydrate, and dichlorvos. Copyright 2009 Elsevier Ltd. All rights reserved.
Recent advances in sequence stratigraphy: The Lowstand and transgressive systems tracts
Energy Technology Data Exchange (ETDEWEB)
Posamentier, H.W. (ARCO Exploration and Production Technology, Plano, TX (United States)); Allen, G.P. (TOTAL Centre Scientifique et Technique, St. Remy le Chevreuses (France))
1993-09-01
On basin margins characterized by a ramp physiography, relative sea level fall induces basinally isolated, shelf-perched, forced-regression shoreline and deltaic deposition rather than deep-water submarine-fan turbidite deposition. These deposits comprise the early lowstand systems tract. In proximal locations, the sequence boundary at the base of these lowstand deposits is expressed as an erosional unconformity, whereas in distal settings this surface occurs as a correlative conformity. The stratigraphic discontinuity at the top of these lowstand deposits is a ravinement surface and in places can be more striking than the sequence-bounding surface at the base. significant erosion due to fluvial processes during forced regression and subsequent shoreface processes during transgression may be common at the tops of these lowstand deposits. The transgressive systems tract commonly comprises, proximally to distally, backstepped barrier beach deposits, sheet-like lag deposits, and [open quotes]healing phase[close quotes] deposits. This latter unit has not been described widely, but volumetrically may contain a significant part of the transgressive systems tract. Healing phase deposits comprise a wedge of sediment that onlaps the last clinoform of the underlying progradational phase. These sediments commonly are derived by cannibalization from the top or edge of the subjacent progradational phase deposits (i.e., either highstand or lowstand), during and immediately after transgression. These deposits are referred to as the healing phase because their deposition tends to [open quotes]heal over[close quotes] the relatively steep clinoform slope of the underlying progradational phase after transgression has resulted in landward shift of the depocenter. Although sometimes misinterpreted as lowstand deposits, these depositional units commonly do not contain significant reservoir facies.
Meaney, Christopher; Moineddin, Rahim
2014-01-24
In biomedical research, response variables are often encountered which have bounded support on the open unit interval--(0,1). Traditionally, researchers have attempted to estimate covariate effects on these types of response data using linear regression. Alternative modelling strategies may include: beta regression, variable-dispersion beta regression, and fractional logit regression models. This study employs a Monte Carlo simulation design to compare the statistical properties of the linear regression model to that of the more novel beta regression, variable-dispersion beta regression, and fractional logit regression models. In the Monte Carlo experiment we assume a simple two sample design. We assume observations are realizations of independent draws from their respective probability models. The randomly simulated draws from the various probability models are chosen to emulate average proportion/percentage/rate differences of pre-specified magnitudes. Following simulation of the experimental data we estimate average proportion/percentage/rate differences. We compare the estimators in terms of bias, variance, type-1 error and power. Estimates of Monte Carlo error associated with these quantities are provided. If response data are beta distributed with constant dispersion parameters across the two samples, then all models are unbiased and have reasonable type-1 error rates and power profiles. If the response data in the two samples have different dispersion parameters, then the simple beta regression model is biased. When the sample size is small (N0 = N1 = 25) linear regression has superior type-1 error rates compared to the other models. Small sample type-1 error rates can be improved in beta regression models using bias correction/reduction methods. In the power experiments, variable-dispersion beta regression and fractional logit regression models have slightly elevated power compared to linear regression models. Similar results were observed if the
On weighted and locally polynomial directional quantile regression
Czech Academy of Sciences Publication Activity Database
Boček, Pavel; Šiman, Miroslav
2017-01-01
Roč. 32, č. 3 (2017), s. 929-946 ISSN 0943-4062 R&D Projects: GA ČR GA14-07234S Institutional support: RVO:67985556 Keywords : Quantile regression * Nonparametric regression * Nonparametric regression Subject RIV: IN - Informatics, Computer Science Impact factor: 0.434, year: 2016 http:// library .utia.cas.cz/separaty/2017/SI/bocek-0458380.pdf
Using a Natural Experiment to Examine Tobacco Tax Regressivity
Adel Bosch; Steven F. Koch
2014-01-01
We take advantage of a tobacco tax hike that occurred during the collection of the South African Income and Expenditure Survey to examine the regressivity of tobacco taxes. We are also able to examine the relative change in regressivity following the tax increase. Like previous research into commodity taxes, we find that tobacco taxes are regressive. However, we find that tobacco tax increases reduce the tax burden at the lower end of the income distribution, such that after the cigarette tax...
Language regression in children with Autism Spectrum Disorders.
Kumar, Suman; Karmakar, Probir; Mohanan, Akhil
2014-02-01
Regression in autism applies to the phenomenon of apparently normal early development followed by the loss of previously acquired skills and manifestation of symptoms of autism. Estimates of the frequency of regression in autism range from 10% to 50%. Although there are tools available to evaluate and diagnose Autism Spectrum Disorders, however, there is no published tool available in Indian context to identify the children with ASD at an early age. The study was aimed to describe the differences in language regression between children with ASD and typically developing children and also to determine the age of regression. Regression screening tool, a questionnaire was developed based on Regression Supplement Form (Goldberg et al., 2003). The skills were validated by five Clinical Psychologists. It comprised of 16 skills which included domains like, 'spoken language and non verbal communication', 'social interest and responsiveness' and 'play and imagination'. This retrospective study was conducted on a single group. The participants consisted of parents of 30 children with ASD (22 males and 8 females). The findings revealed a significant regression in children with ASD. The mean regression age is 20.19 months (SD-5.2). The regression profile of the children with ASD revealed regression of language skills occurred at 19.16 months followed by non language skills at 20.5 months. Based on the findings it can be stated that inclusion of regression screening tool will offer clinicians a convenient tool to examine the phenomena of regression in children with ASD and identify them as early as 21 months of age for early intervention. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.
Internet Purchases in European Union Countries: Multiple Linear Regression Approach
Ksenija Dumičić; Anita Čeh Časni; Irena Palić
2014-01-01
This paper examines economic and Information and Communication Technology (ICT) development influence on recently increasing Internet purchases by individuals for European Union member states. After a growing trend for Internet purchases in EU27 was noticed, all possible regression analysis was applied using nine independent variables in 2011. Finally, two linear regression models were studied in detail. Conducted simple linear regression analysis confirmed the research hypothesis that the In...
Quantile Regression in the Study of Developmental Sciences
Petscher, Yaacov; Logan, Jessica A. R.
2013-01-01
Linear regression analysis is one of the most common techniques applied in developmental research, but only allows for an estimate of the average relations between the predictor(s) and the outcome. This study describes quantile regression, which provides estimates of the relations between the predictor(s) and outcome, but across multiple points of the outcome’s distribution. Using data from the High School and Beyond and U.S. Sustained Effects Study databases, quantile regression is demonstra...
Bayesian extreme quantile regression for hidden Markov models
Koutsourelis, Antonios
2012-01-01
This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel University The main contribution of this thesis is the introduction of Bayesian quantile regression for hidden Markov models, especially when we have to deal with extreme quantile regression analysis, as there is a limited research to inference conditional quantiles for hidden Markov models, under a Bayesian approach. The first objective is to compare Bayesian extreme quantile regression and th...
Otvos, Ervin G.; Carter, Gregory A.
2013-09-01
Basic differences between non-deltaic regressive and deltaic transgressive barrier islands reflect major contrasts in geological settings and sediment sources. Two island groups on the N. Gulf of Mexico provide unique perspectives of genetic and geomorphic contrasts applicable in a worldwide context. The near-extinction of the deltaic transgressive Chandeleur barriers and reduction of the sturdier prograded Mississippi-Alabama (MS-AL) chain are related to differences in sediment sources, storm, and anthropogenic impact. 160 years of documentary evidence points to contrasting geological settings, development history, sediment sources, and island morphology as responsible for different island erodibility and life spans. The non-deltaic chain received larger volumes of coarser, less erodible medium sand from the NE Gulf coast. Onshore sand flux from reworked delta deposits received from the retreating delta shoreface initiated the fragile, thin, and isolated transgressive Chandeleur islands. Fine-grained sand from unconsolidated muds of abandoned Mississippi-St. Bernard delta lobes maintained two distinct transgressive barrier island categories. In the absence of quantitative data on cross-shore transport, discrepancies between estimated littoral drift volumes and sand reserves for nourishment remain unexplained. Medium-sandy MS-AL barriers have resisted storm events far better than delta barriers. However, even the former chain did undergo 26 to 53% area reduction since 1848. Anthropogenic intervention stymied island growth. Emerging intertidal berm-basins formed on sandy shoal platforms in storm-eliminated sectors have contributed to partial island recovery. Delta attrition by wave erosion, tectonic, and compactional subsidence had accelerated delta lobe and barrier island decay. Intensive storm erosion culminating in and following Hurricane Katrina came close to eradicate the highly vulnerable Chandeleur barrier chain. Lacking adequate nourishment, after
Acupuncture and Spontaneous Regression of a Radiculopathic Cervical Herniated Disc
Directory of Open Access Journals (Sweden)
Kim Sung-Ha
2012-06-01
Full Text Available The spontaneous regression of herniated cervical discs is not a well-established phenomenon. However, we encountered a case of a spontaneous regression of a severe radiculopathic herniated cervical disc that was treated with acupuncture, pharmacopuncture, and herb medicine. The symptoms were improved within 12 months of treatment. Magnetic resonance imaging (MRI conducted at that time revealed marked regression of the herniated disc. This case provides an additional example of spontaneous regression of a herniated cervical disc documented by MRI following non-surgical treatment.
Transference regression and psychoanalytic technique with infantile personalities.
Kernberg, O F
1991-01-01
This paper describes a particular form of 'silent' regression during the psychoanalytic treatment of infantile personalities (a type of character pathology related to the hysterical personality). This regression is characterized by the development of rapid interchange in the roles enacted in the transference and projected on to the analyst, an apparent 'disconnexion' of the transference material from that dominant when the patient is in non-regressed states, and the intensification of the analyst's counter-transference reactions. The diagnosis and management of these regressive transferences requires a particular interpretive style and strategy outlined in this paper and illustrated with two case vignettes.
Regression in primary cutaneous melanoma: etiopathogenesis and clinical significance.
Aung, Phyu P; Nagarajan, Priyadharsini; Prieto, Victor G
2017-02-27
Though not required currently for staging, regression is a histopathologic parameter typically reported upon diagnosis of an invasive primary cutaneous melanoma. The studies examining the prognostic significance of regression in patient outcome have yielded controversial findings; likely because the definition and assessment of regression have not been consistent, in addition to subjectivity of pathologists' interpretation. Regression is histologically characterized by variable decrease in the number of melanoma cells accompanied by the presence of a host response consisting of dermal fibrosis, inflammatory infiltrate, melanophages, ectatic blood vessels, epidermal attenuation, and/or apoptosis of keratinocytes or melanocytes; the relative extent of these features depends on the stage of the regression. However, the magnitudes to which these individual changes must be present to meet the threshold of histologic regression have not been well defined or agreed upon, and thus, the definition and classification of histologic regression in melanoma varies considerably among institutions and even among individual pathologists. In order to determine the clinical significance of histologic analysis of regression, there is a compelling need for a universal scheme to objectively define and assess histologic regression in primary cutaneous melanoma, so that the biologic and prognostic significance of this process may be completely understood.Laboratory Investigation advance online publication, 27 February 2017; doi:10.1038/labinvest.2017.8.
Neither fixed nor random: weighted least squares meta-regression.
Stanley, T D; Doucouliagos, Hristos
2017-03-01
Our study revisits and challenges two core conventional meta-regression estimators: the prevalent use of 'mixed-effects' or random-effects meta-regression analysis and the correction of standard errors that defines fixed-effects meta-regression analysis (FE-MRA). We show how and explain why an unrestricted weighted least squares MRA (WLS-MRA) estimator is superior to conventional random-effects (or mixed-effects) meta-regression when there is publication (or small-sample) bias that is as good as FE-MRA in all cases and better than fixed effects in most practical applications. Simulations and statistical theory show that WLS-MRA provides satisfactory estimates of meta-regression coefficients that are practically equivalent to mixed effects or random effects when there is no publication bias. When there is publication selection bias, WLS-MRA always has smaller bias than mixed effects or random effects. In practical applications, an unrestricted WLS meta-regression is likely to give practically equivalent or superior estimates to fixed-effects, random-effects, and mixed-effects meta-regression approaches. However, random-effects meta-regression remains viable and perhaps somewhat preferable if selection for statistical significance (publication bias) can be ruled out and when random, additive normal heterogeneity is known to directly affect the 'true' regression coefficient. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.
Meta-Regression: A Framework for Robust Reactive Optimization
DEFF Research Database (Denmark)
McClary, Dan; Syrotiuk, Violet R.; Kulahci, Murat
2007-01-01
Maintaining optimal performance as the conditions of a system change is a challenging problem. To solve this problem, we present meta-regression, a general methodology for alleviating traditional difficulties in nonlinear regression modelling. Meta-regression allows for reactive optimization......, in which system components self-organize to changing conditions in a manner that is robust, or affected minimally by other sources of variability. Meta-regression extends profiling, providing a methodology for model-building when there is incomplete knowledge of the mechanisms and interactions...
Regression calibration with more surrogates than mismeasured variables
Kipnis, Victor
2012-06-29
In a recent paper (Weller EA, Milton DK, Eisen EA, Spiegelman D. Regression calibration for logistic regression with multiple surrogates for one exposure. Journal of Statistical Planning and Inference 2007; 137: 449-461), the authors discussed fitting logistic regression models when a scalar main explanatory variable is measured with error by several surrogates, that is, a situation with more surrogates than variables measured with error. They compared two methods of adjusting for measurement error using a regression calibration approximate model as if it were exact. One is the standard regression calibration approach consisting of substituting an estimated conditional expectation of the true covariate given observed data in the logistic regression. The other is a novel two-stage approach when the logistic regression is fitted to multiple surrogates, and then a linear combination of estimated slopes is formed as the estimate of interest. Applying estimated asymptotic variances for both methods in a single data set with some sensitivity analysis, the authors asserted superiority of their two-stage approach. We investigate this claim in some detail. A troubling aspect of the proposed two-stage method is that, unlike standard regression calibration and a natural form of maximum likelihood, the resulting estimates are not invariant to reparameterization of nuisance parameters in the model. We show, however, that, under the regression calibration approximation, the two-stage method is asymptotically equivalent to a maximum likelihood formulation, and is therefore in theory superior to standard regression calibration. However, our extensive finite-sample simulations in the practically important parameter space where the regression calibration model provides a good approximation failed to uncover such superiority of the two-stage method. We also discuss extensions to different data structures.
A comparative study of multiple regression analysis and back ...
Indian Academy of Sciences (India)
Abhijit Sarkar
prediction for robotic GMAW-based rapid manufacturing through a neural network and a second-order regression analysis. J. Intell. Manuf. 25(1): 157–163. [32] Kim I-S, Lee S-H and Yarlagadda P K 2003 Comparison of multiple regression and back propagation neural network approaches in modelling top bead height of ...
General Nature of Multicollinearity in Multiple Regression Analysis.
Liu, Richard
1981-01-01
Discusses multiple regression, a very popular statistical technique in the field of education. One of the basic assumptions in regression analysis requires that independent variables in the equation should not be highly correlated. The problem of multicollinearity and some of the solutions to it are discussed. (Author)
Interpreting Bivariate Regression Coefficients: Going beyond the Average
Halcoussis, Dennis; Phillips, G. Michael
2010-01-01
Statistics, econometrics, investment analysis, and data analysis classes often review the calculation of several types of averages, including the arithmetic mean, geometric mean, harmonic mean, and various weighted averages. This note shows how each of these can be computed using a basic regression framework. By recognizing when a regression model…
Multiple Regression in a Two-Way Layout.
Lindley, Dennis V.
This paper discusses Bayesian m-group regression where the groups are arranged in a two-way layout into m rows and n columns, there still being a regression of y on the x's within each group. The mathematical model is then provided as applied to the case where the rows correspond to high schools and the columns to colleges: the predictor variables…
Methods of Detecting Outliers in A Regression Analysis Model ...
African Journals Online (AJOL)
PROF. O. E. OSUAGWU
2013-06-01
Jun 1, 2013 ... Abstract. This study detects outliers in a univariate and bivariate data by using both Rosner's and. Grubb's test in a regression analysis model. The study shows how an observation that causes the least square point estimate of a Regression model to be substantially different from what it would be if the ...
Methods of Detecting Outliers in A Regression Analysis Model. | Ogu ...
African Journals Online (AJOL)
This study detects outliers in a univariate and bivariate data by using both Rosner's and Grubb's test in a regression analysis model. The study shows how an observation that causes the least square point estimate of a Regression model to be substantially different from what it would be if the observation were removed from ...
Multivariate Regression of Liver on Intestine of Mice: A ...
African Journals Online (AJOL)
FIRST LADY
log) regressions were performed. He chose the log-log curves because its variance was more uniform. The statistical comparison of different regression models (linear and stepwise linear), the likelihood ratio test was used (Engels, Sinzinkayo, De vlas and. Gryseels, 1997). The statistical analyses of Karanja, Colley, Nahlen ...
A test for the parameters of multiple linear regression models ...
African Journals Online (AJOL)
A test for the parameters of multiple linear regression models is developed for conducting tests simultaneously on all the parameters of multiple linear regression models. The test is robust relative to the assumptions of homogeneity of variances and absence of serial correlation of the classical F-test. Under certain null and ...
A Methodology for Generating Placement Rules that Utilizes Logistic Regression
Wurtz, Keith
2008-01-01
The purpose of this article is to provide the necessary tools for institutional researchers to conduct a logistic regression analysis and interpret the results. Aspects of the logistic regression procedure that are necessary to evaluate models are presented and discussed with an emphasis on cutoff values and choosing the appropriate number of…
Tax Evasion, Information Reporting, and the Regressive Bias Hypothesis
DEFF Research Database (Denmark)
Boserup, Simon Halphen; Pinje, Jori Veng
A robust prediction from the tax evasion literature is that optimal auditing induces a regressive bias in effective tax rates compared to statutory rates. If correct, this will have important distributional consequences. Nevertheless, the regressive bias hypothesis has never been tested empirically...
The Solvability of Probabilistic Regresses. A Reply to Frederik Herzberg
Atkinson, David; Peijnenburg, Jeanne
We have earlier shown by construction that a proposition can have a well-defined nonzero probability, even if it is justified by an infinite probabilistic regress. We thought this to be an adequate rebuttal of foundationalist claims that probabilistic regresses must lead either to an indeterminate,
A logistic regression estimating function for spatial Gibbs point processes
DEFF Research Database (Denmark)
Baddeley, Adrian; Coeurjolly, Jean-François; Rubak, Ege
We propose a computationally efficient logistic regression estimating function for spatial Gibbs point processes. The sample points for the logistic regression consist of the observed point pattern together with a random pattern of dummy points. The estimating function is closely related...
Spatial correlation in Bayesian logistic regression with misclassification
DEFF Research Database (Denmark)
Bihrmann, Kristine; Toft, Nils; Nielsen, Søren Saxmose
2014-01-01
Standard logistic regression assumes that the outcome is measured perfectly. In practice, this is often not the case, which could lead to biased estimates if not accounted for. This study presents Bayesian logistic regression with adjustment for misclassification of the outcome applied to data...
Controlling the Type I Error Rate in Stepwise Regression Analysis.
Pohlmann, John T.
1979-01-01
The type I error rate in stepwise regression analysis deserves serious consideration by researchers. The problem-wide error rate is the probability of selecting any variable when all variables have population regression weights of zero. Appropriate significance tests are presented and a Monte Carlo experiment is described. (Author/CTM)
Using Multiple Linear Regression Techniques to Quantify Carbon ...
African Journals Online (AJOL)
komla
locations, the study applied the stepwise multiple regression technique to identify ecological variables that would .... Data analyses. The Statistical Package for Social Sciences (SPSS 8.0) for Windows programme was used for statistical analyses of the data. Multiple linear regression methods were applied to analyse the.
Semisupervised Clustering by Iterative Partition and Regression with Neuroscience Applications
Qian, Guoqi; Wu, Yuehua; Ferrari, Davide; Qiao, Puxue; Hollande, Frédéric
2016-01-01
Regression clustering is a mixture of unsupervised and supervised statistical learning and data mining method which is found in a wide range of applications including artificial intelligence and neuroscience. It performs unsupervised learning when it clusters the data according to their respective unobserved regression hyperplanes. The method also performs supervised learning when it fits regression hyperplanes to the corresponding data clusters. Applying regression clustering in practice requires means of determining the underlying number of clusters in the data, finding the cluster label of each data point, and estimating the regression coefficients of the model. In this paper, we review the estimation and selection issues in regression clustering with regard to the least squares and robust statistical methods. We also provide a model selection based technique to determine the number of regression clusters underlying the data. We further develop a computing procedure for regression clustering estimation and selection. Finally, simulation studies are presented for assessing the procedure, together with analyzing a real data set on RGB cell marking in neuroscience to illustrate and interpret the method. PMID:27212939
Statistical analysis of sediment toxicity by additive monotone regression splines
Boer, de W.J.; Besten, den P.J.; Braak, ter C.J.F.
2002-01-01
Modeling nonlinearity and thresholds in dose-effect relations is a major challenge, particularly in noisy data sets. Here we show the utility of nonlinear regression with additive monotone regression splines. These splines lead almost automatically to the estimation of thresholds. We applied this
Population-Sample Regression in the Estimation of Population Proportions
Weitzman, R. A.
2006-01-01
Focusing on a single sample obtained randomly with replacement from a single population, this article examines the regression of population on sample proportions and develops an unbiased estimator of the square of the correlation between them. This estimator turns out to be the regression coefficient. Use of the squared-correlation estimator as a…
Mixed Frequency Data Sampling Regression Models: The R Package midasr
Directory of Open Access Journals (Sweden)
Eric Ghysels
2016-08-01
Full Text Available When modeling economic relationships it is increasingly common to encounter data sampled at different frequencies. We introduce the R package midasr which enables estimating regression models with variables sampled at different frequencies within a MIDAS regression framework put forward in work by Ghysels, Santa-Clara, and Valkanov (2002. In this article we define a general autoregressive MIDAS regression model with multiple variables of different frequencies and show how it can be specified using the familiar R formula interface and estimated using various optimization methods chosen by the researcher. We discuss how to check the validity of the estimated model both in terms of numerical convergence and statistical adequacy of a chosen regression specification, how to perform model selection based on a information criterion, how to assess forecasting accuracy of the MIDAS regression model and how to obtain a forecast aggregation of different MIDAS regression models. We illustrate the capabilities of the package with a simulated MIDAS regression model and give two empirical examples of application of MIDAS regression.
Testing the equality of nonparametric regression curves based on ...
African Journals Online (AJOL)
Abstract. In this work we propose a new methodology for the comparison of two regression functions f1 and f2 in the case of homoscedastic error structure and a fixed design. Our approach is based on the empirical Fourier coefficients of the regression functions f1 and f2 respectively. As our main results we obtain the ...
Spontaneous regression of metastases from malignant melanoma: a case report
DEFF Research Database (Denmark)
Kalialis, Louise V; Drzewiecki, Krzysztof T; Mohammadi, Mahin
2008-01-01
A case of a 61-year-old male with widespread metastatic melanoma is presented 5 years after complete spontaneous cure. Spontaneous regression occurred in cutaneous, pulmonary, hepatic and cerebral metastases. A review of the literature reveals seven cases of regression of cerebral metastases...
Semisupervised Clustering by Iterative Partition and Regression with Neuroscience Applications.
Qian, Guoqi; Wu, Yuehua; Ferrari, Davide; Qiao, Puxue; Hollande, Frédéric
2016-01-01
Regression clustering is a mixture of unsupervised and supervised statistical learning and data mining method which is found in a wide range of applications including artificial intelligence and neuroscience. It performs unsupervised learning when it clusters the data according to their respective unobserved regression hyperplanes. The method also performs supervised learning when it fits regression hyperplanes to the corresponding data clusters. Applying regression clustering in practice requires means of determining the underlying number of clusters in the data, finding the cluster label of each data point, and estimating the regression coefficients of the model. In this paper, we review the estimation and selection issues in regression clustering with regard to the least squares and robust statistical methods. We also provide a model selection based technique to determine the number of regression clusters underlying the data. We further develop a computing procedure for regression clustering estimation and selection. Finally, simulation studies are presented for assessing the procedure, together with analyzing a real data set on RGB cell marking in neuroscience to illustrate and interpret the method.
Moderation analysis using a two-level regression model.
Yuan, Ke-Hai; Cheng, Ying; Maxwell, Scott
2014-10-01
Moderation analysis is widely used in social and behavioral research. The most commonly used model for moderation analysis is moderated multiple regression (MMR) in which the explanatory variables of the regression model include product terms, and the model is typically estimated by least squares (LS). This paper argues for a two-level regression model in which the regression coefficients of a criterion variable on predictors are further regressed on moderator variables. An algorithm for estimating the parameters of the two-level model by normal-distribution-based maximum likelihood (NML) is developed. Formulas for the standard errors (SEs) of the parameter estimates are provided and studied. Results indicate that, when heteroscedasticity exists, NML with the two-level model gives more efficient and more accurate parameter estimates than the LS analysis of the MMR model. When error variances are homoscedastic, NML with the two-level model leads to essentially the same results as LS with the MMR model. Most importantly, the two-level regression model permits estimating the percentage of variance of each regression coefficient that is due to moderator variables. When applied to data from General Social Surveys 1991, NML with the two-level model identified a significant moderation effect of race on the regression of job prestige on years of education while LS with the MMR model did not. An R package is also developed and documented to facilitate the application of the two-level model.
Testing hypotheses for differences between linear regression lines
Stanley J. Zarnoch
2009-01-01
Five hypotheses are identified for testing differences between simple linear regression lines. The distinctions between these hypotheses are based on a priori assumptions and illustrated with full and reduced models. The contrast approach is presented as an easy and complete method for testing for overall differences between the regressions and for making pairwise...
Implicit collinearity effect in linear regression: Application to basal ...
African Journals Online (AJOL)
Collinearity of predictor variables is a severe problem in the least square regression analysis. It contributes to the instability of regression coefficients and leads to a wrong prediction accuracy. Despite these problems, studies are conducted with a large number of observed and derived variables linked with a response ...
Regression Commonality Analysis: A Technique for Quantitative Theory Building
Nimon, Kim; Reio, Thomas G., Jr.
2011-01-01
When it comes to multiple linear regression analysis (MLR), it is common for social and behavioral science researchers to rely predominately on beta weights when evaluating how predictors contribute to a regression model. Presenting an underutilized statistical technique, this article describes how organizational researchers can use commonality…
Changes in persistence, spurious regressions and the Fisher hypothesis
DEFF Research Database (Denmark)
Kruse, Robinson; Ventosa-Santaulària, Daniel; Noriega, Antonio E.
Declining inflation persistence has been documented in numerous studies. When such series are analyzed in a regression framework in conjunction with other persistent time series, spurious regressions are likely to occur. We propose to use the coefficient of determination R2 as a test statistic to...
Augmenting Data with Published Results in Bayesian Linear Regression
de Leeuw, Christiaan; Klugkist, Irene
2012-01-01
In most research, linear regression analyses are performed without taking into account published results (i.e., reported summary statistics) of similar previous studies. Although the prior density in Bayesian linear regression could accommodate such prior knowledge, formal models for doing so are absent from the literature. The goal of this…
Quantile Regression in the Study of Developmental Sciences
Petscher, Yaacov; Logan, Jessica A. R.
2014-01-01
Linear regression analysis is one of the most common techniques applied in developmental research, but only allows for an estimate of the average relations between the predictor(s) and the outcome. This study describes quantile regression, which provides estimates of the relations between the predictor(s) and outcome, but across multiple points of…
quantifying the stock of soil organic carbon using multiple regression
African Journals Online (AJOL)
Osondu
2012-03-15
Mar 15, 2012 ... QUANTIFYING THE STOCK OF SOIL ORGANIC CARBON USING MULTIPLE REGRESSION MODEL. IN A FALLOW VEGETATION, ... plots were collected and subjected to linear regression analysis. The analysis generated three ... and soils are principal reservoirs of carbon, as they help to reduce the ...
Correlation-regression model for physico-chemical quality of ...
African Journals Online (AJOL)
abusaad
Multiple regression models can predict EC at 5% level of significance. Nitrate, chlorides, TDS and ... Key words: Groundwater, water quality, bore well, water supply, correlation, regression. INTRODUCTION. Groundwater is the prime .... reservoir located 10 to 25 km away from the city and through more than 1850 bore wells ...
Semisupervised Clustering by Iterative Partition and Regression with Neuroscience Applications
Directory of Open Access Journals (Sweden)
Guoqi Qian
2016-01-01
Full Text Available Regression clustering is a mixture of unsupervised and supervised statistical learning and data mining method which is found in a wide range of applications including artificial intelligence and neuroscience. It performs unsupervised learning when it clusters the data according to their respective unobserved regression hyperplanes. The method also performs supervised learning when it fits regression hyperplanes to the corresponding data clusters. Applying regression clustering in practice requires means of determining the underlying number of clusters in the data, finding the cluster label of each data point, and estimating the regression coefficients of the model. In this paper, we review the estimation and selection issues in regression clustering with regard to the least squares and robust statistical methods. We also provide a model selection based technique to determine the number of regression clusters underlying the data. We further develop a computing procedure for regression clustering estimation and selection. Finally, simulation studies are presented for assessing the procedure, together with analyzing a real data set on RGB cell marking in neuroscience to illustrate and interpret the method.
Regression-based sib pair linkage analysis for binary traits
Zeegers, MPA; Rice, JP; Rijsdijk, FV; Abecasis, GR; Sham, PC
2003-01-01
The Haseman-Elston (HE) regression method offers a mathematically and computationally simpler alternative to variance-components (VC) models for the linkage analysis of quantitative traits. However, current versions of HE regression and VC models are not optimised for binary traits. Here, we present
Tutorial on Using Regression Models with Count Outcomes Using R
Directory of Open Access Journals (Sweden)
A. Alexander Beaujean
2016-02-01
Full Text Available Education researchers often study count variables, such as times a student reached a goal, discipline referrals, and absences. Most researchers that study these variables use typical regression methods (i.e., ordinary least-squares either with or without transforming the count variables. In either case, using typical regression for count data can produce parameter estimates that are biased, thus diminishing any inferences made from such data. As count-variable regression models are seldom taught in training programs, we present a tutorial to help educational researchers use such methods in their own research. We demonstrate analyzing and interpreting count data using Poisson, negative binomial, zero-inflated Poisson, and zero-inflated negative binomial regression models. The count regression methods are introduced through an example using the number of times students skipped class. The data for this example are freely available and the R syntax used run the example analyses are included in the Appendix.
Parameters Estimation of Geographically Weighted Ordinal Logistic Regression (GWOLR) Model
Zuhdi, Shaifudin; Retno Sari Saputro, Dewi; Widyaningsih, Purnami
2017-06-01
A regression model is the representation of relationship between independent variable and dependent variable. The dependent variable has categories used in the logistic regression model to calculate odds on. The logistic regression model for dependent variable has levels in the logistics regression model is ordinal. GWOLR model is an ordinal logistic regression model influenced the geographical location of the observation site. Parameters estimation in the model needed to determine the value of a population based on sample. The purpose of this research is to parameters estimation of GWOLR model using R software. Parameter estimation uses the data amount of dengue fever patients in Semarang City. Observation units used are 144 villages in Semarang City. The results of research get GWOLR model locally for each village and to know probability of number dengue fever patient categories.
Quantile Regression in the Study of Developmental Sciences
Petscher, Yaacov; Logan, Jessica A. R.
2014-01-01
Linear regression analysis is one of the most common techniques applied in developmental research, but only allows for an estimate of the average relations between the predictor(s) and the outcome. This study describes quantile regression, which provides estimates of the relations between the predictor(s) and outcome, but across multiple points of the outcome’s distribution. Using data from the High School and Beyond and U.S. Sustained Effects Study databases, quantile regression is demonstrated and contrasted with linear regression when considering models with: (a) one continuous predictor, (b) one dichotomous predictor, (c) a continuous and a dichotomous predictor, and (d) a longitudinal application. Results from each example exhibited the differential inferences which may be drawn using linear or quantile regression. PMID:24329596
Spontaneous regression of metastases from malignant melanoma: a case report
DEFF Research Database (Denmark)
Kalialis, Louise V; Drzewiecki, Krzysztof T; Mohammadi, Mahin
2008-01-01
A case of a 61-year-old male with widespread metastatic melanoma is presented 5 years after complete spontaneous cure. Spontaneous regression occurred in cutaneous, pulmonary, hepatic and cerebral metastases. A review of the literature reveals seven cases of regression of cerebral metastases......; this report is the first to document complete spontaneous regression of cerebral metastases from malignant melanoma by means of computed tomography scans. Spontaneous regression is defined as the partial or complete disappearance of a malignant tumour in the absence of all treatment or in the presence...... of therapy, which is considered inadequate to exert a significant influence on neoplastic disease. The incidence of spontaneous regression of metastases from malignant melanoma is approximately one per 400 patients, and possible mechanisms include immunologic, endocrine, inflammatory and tumour nutritional...
Regression of posterior uveal melanomas following cobalt-60 plaque radiotherapy
Energy Technology Data Exchange (ETDEWEB)
Cruess, A.F.; Augsburger, J.J.; Shields, J.A.; Brady, L.W.; Markoe, A.M.; Day, J.L.
1984-12-01
A method has been devised for evaluating the rate and extent of regression of the first 100 consecutive patients with a posterior uveal melanoma that had been managed by Cobalt-60 plaque radiotherapy at Wills Eye Hospital. It was found that the average posterior uveal melanoma in the series did not regress rapidly to a flat, depigmented scar but shrank slowly and persisted as a residual mass approximately 50% of the thickness of the original tumor at 54 months following Cobalt-60 plaque radiotherapy. The authors also found that the rate and extent of regression of the tumors in patients who subsequently developed metastatic melanoma were not appreciably different from the rate and extent of regression of the tumors in patients who remained well systemically. These observations indicate that the rate and extent of regression of posterior uveal melanomas following Cobalt-60 plaque radiotherapy are poor indicators of the prognosis of the affected patients for subsequent development of clinical metastatic disease.
Koon, Sharon; Petscher, Yaacov
2015-01-01
The purpose of this report was to explicate the use of logistic regression and classification and regression tree (CART) analysis in the development of early warning systems. It was motivated by state education leaders' interest in maintaining high classification accuracy while simultaneously improving practitioner understanding of the rules by…
Crawford, John R.; Garthwaite, Paul H.; Denham, Annie K.; Chelune, Gordon J.
2012-01-01
Regression equations have many useful roles in psychological assessment. Moreover, there is a large reservoir of published data that could be used to build regression equations; these equations could then be employed to test a wide variety of hypotheses concerning the functioning of individual cases. This resource is currently underused because…
Tary, A.K.; Duncan, M. FitzGerald; Weddle, T.K.
2007-01-01
In eastern coastal Maine, many flat-topped landforms, often identified as glacial-marine deltas, are cultivated for blueberry production. These agriculturally valuable features are not exploited for aggregate resources, severely limiting stratigraphic exposure. Coring is often forbidden; where permissible, coarse-grained surficial sediments make coring and sediment retrieval difficult. Ground penetrating radar (GPR) has become an invaluable tool in an ongoing study of the otherwise inaccessible subsurface morphology in this region and provides a means of detailing the large-scale sedimentary structures comprising these features. GPR studies allow us to reassess previous depositional interpretations and to develop alternative developmental models. The work presented here focuses on Pineo Ridge, a large, flat-topped ice-marginal glacial-marine delta complex with a strong linear trend and two distinct landform zones, informally termed East Pineo and West Pineo. Previous workers have described each zone separately due to local morphological variation. Our GPR work further substantiates this geomorphic differentiation. East Pineo developed as a series of deltaic lobes prograding southward from an ice-contact margin during the local marine highstand. GPR data do not suggest postdepositional modification by ice-margin re-advance. We suggest that West Pineo has a more complex, two-stage depositional history. The southern section of the feature consists of southward-prograding deltaic lobes deposited during retreat of the Laurentide ice margin, with later erosional modification during marine regression. The northern section of West Pineo formed as a series of northward-prograd- ing deltaic lobes as sediment-laden meltwater may have been diverted by the existing deposits of the southern section of West Pineo. ?? 2007 The Geological Society of America. All rights reserved.
DEFF Research Database (Denmark)
Sharifzadeh, Sara; Skytte, Jacob Lercke; Nielsen, Otto Højager Attermann
2012-01-01
Statistical solutions find wide spread use in food and medicine quality control. We investigate the effect of different regression and sparse regression methods for a viscosity estimation problem using the spectro-temporal features from new Sub-Surface Laser Scattering (SLS) vision system. From...... with sparse LAR, lasso and Elastic Net (EN) sparse regression methods. Due to the inconsistent measurement condition, Locally Weighted Scatter plot Smoothing (Loess) has been employed to alleviate the undesired variation in the estimated viscosity. The experimental results of applying different methods show...... that, the sparse regression lasso outperforms other methods. In addition, the use of local smoothing has improved the results considerably for all regression methods. Due to the sparsity of lasso, this result would assist to design a simpler vision system with less spectral bands....
Independent contrasts and PGLS regression estimators are equivalent.
Blomberg, Simon P; Lefevre, James G; Wells, Jessie A; Waterhouse, Mary
2012-05-01
We prove that the slope parameter of the ordinary least squares regression of phylogenetically independent contrasts (PICs) conducted through the origin is identical to the slope parameter of the method of generalized least squares (GLSs) regression under a Brownian motion model of evolution. This equivalence has several implications: 1. Understanding the structure of the linear model for GLS regression provides insight into when and why phylogeny is important in comparative studies. 2. The limitations of the PIC regression analysis are the same as the limitations of the GLS model. In particular, phylogenetic covariance applies only to the response variable in the regression and the explanatory variable should be regarded as fixed. Calculation of PICs for explanatory variables should be treated as a mathematical idiosyncrasy of the PIC regression algorithm. 3. Since the GLS estimator is the best linear unbiased estimator (BLUE), the slope parameter estimated using PICs is also BLUE. 4. If the slope is estimated using different branch lengths for the explanatory and response variables in the PIC algorithm, the estimator is no longer the BLUE, so this is not recommended. Finally, we discuss whether or not and how to accommodate phylogenetic covariance in regression analyses, particularly in relation to the problem of phylogenetic uncertainty. This discussion is from both frequentist and Bayesian perspectives.
Impact of multicollinearity on small sample hydrologic regression models
Kroll, Charles N.; Song, Peter
2013-06-01
Often hydrologic regression models are developed with ordinary least squares (OLS) procedures. The use of OLS with highly correlated explanatory variables produces multicollinearity, which creates highly sensitive parameter estimators with inflated variances and improper model selection. It is not clear how to best address multicollinearity in hydrologic regression models. Here a Monte Carlo simulation is developed to compare four techniques to address multicollinearity: OLS, OLS with variance inflation factor screening (VIF), principal component regression (PCR), and partial least squares regression (PLS). The performance of these four techniques was observed for varying sample sizes, correlation coefficients between the explanatory variables, and model error variances consistent with hydrologic regional regression models. The negative effects of multicollinearity are magnified at smaller sample sizes, higher correlations between the variables, and larger model error variances (smaller R2). The Monte Carlo simulation indicates that if the true model is known, multicollinearity is present, and the estimation and statistical testing of regression parameters are of interest, then PCR or PLS should be employed. If the model is unknown, or if the interest is solely on model predictions, is it recommended that OLS be employed since using more complicated techniques did not produce any improvement in model performance. A leave-one-out cross-validation case study was also performed using low-streamflow data sets from the eastern United States. Results indicate that OLS with stepwise selection generally produces models across study regions with varying levels of multicollinearity that are as good as biased regression techniques such as PCR and PLS.
Background stratified Poisson regression analysis of cohort data
Energy Technology Data Exchange (ETDEWEB)
Richardson, David B. [University of North Carolina at Chapel Hill, Department of Epidemiology, School of Public Health, Chapel Hill, NC (United States); Langholz, Bryan [Keck School of Medicine, University of Southern California, Division of Biostatistics, Department of Preventive Medicine, Los Angeles, CA (United States)
2012-03-15
Background stratified Poisson regression is an approach that has been used in the analysis of data derived from a variety of epidemiologically important studies of radiation-exposed populations, including uranium miners, nuclear industry workers, and atomic bomb survivors. We describe a novel approach to fit Poisson regression models that adjust for a set of covariates through background stratification while directly estimating the radiation-disease association of primary interest. The approach makes use of an expression for the Poisson likelihood that treats the coefficients for stratum-specific indicator variables as 'nuisance' variables and avoids the need to explicitly estimate the coefficients for these stratum-specific parameters. Log-linear models, as well as other general relative rate models, are accommodated. This approach is illustrated using data from the Life Span Study of Japanese atomic bomb survivors and data from a study of underground uranium miners. The point estimate and confidence interval obtained from this 'conditional' regression approach are identical to the values obtained using unconditional Poisson regression with model terms for each background stratum. Moreover, it is shown that the proposed approach allows estimation of background stratified Poisson regression models of non-standard form, such as models that parameterize latency effects, as well as regression models in which the number of strata is large, thereby overcoming the limitations of previously available statistical software for fitting background stratified Poisson regression models. (orig.)
Nonlinear Forecasting With Many Predictors Using Kernel Ridge Regression
DEFF Research Database (Denmark)
Exterkate, Peter; Groenen, Patrick J.F.; Heij, Christiaan
This paper puts forward kernel ridge regression as an approach for forecasting with many predictors that are related nonlinearly to the target variable. In kernel ridge regression, the observed predictor variables are mapped nonlinearly into a high-dimensional space, where estimation of the predi......This paper puts forward kernel ridge regression as an approach for forecasting with many predictors that are related nonlinearly to the target variable. In kernel ridge regression, the observed predictor variables are mapped nonlinearly into a high-dimensional space, where estimation...... of the predictive regression model is based on a shrinkage estimator to avoid overfitting. We extend the kernel ridge regression methodology to enable its use for economic time-series forecasting, by including lags of the dependent variable or other individual variables as predictors, as typically desired...... in macroeconomic and financial applications. Monte Carlo simulations as well as an empirical application to various key measures of real economic activity confirm that kernel ridge regression can produce more accurate forecasts than traditional linear and nonlinear methods for dealing with many predictors based...
Ordinary least squares regression is indicated for studies of allometry.
Kilmer, J T; Rodríguez, R L
2017-01-01
When it comes to fitting simple allometric slopes through measurement data, evolutionary biologists have been torn between regression methods. On the one hand, there is the ordinary least squares (OLS) regression, which is commonly used across many disciplines of biology to fit lines through data, but which has a reputation for underestimating slopes when measurement error is present. On the other hand, there is the reduced major axis (RMA) regression, which is often recommended as a substitute for OLS regression in studies of allometry, but which has several weaknesses of its own. Here, we review statistical theory as it applies to evolutionary biology and studies of allometry. We point out that the concerns that arise from measurement error for OLS regression are small and straightforward to deal with, whereas RMA has several key properties that make it unfit for use in the field of allometry. The recommended approach for researchers interested in allometry is to use OLS regression on measurements taken with low (but realistically achievable) measurement error. If measurement error is unavoidable and relatively large, it is preferable to correct for slope attenuation rather than to turn to RMA regression, or to take the expected amount of attenuation into account when interpreting the data. © 2016 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2016 European Society For Evolutionary Biology.
Spontaneous regression of a lumbar disc herniation: case report
Directory of Open Access Journals (Sweden)
Mostarchid Brahim El
2016-12-01
Full Text Available Lumbar disc herniation is a common disease that induces back pain and radicular pain. Some cases require conservative treatment or at times relived spontaneously. Spontaneous regression of disc herniation is an atypical clinical presentation, and it has been recognized with the advancement of recent advances in imaging techniques. We present a 35-year-old woman presented a spontaneous regression of a lumbar disc herniation with good outcome after intensive physical therapy program. Spontaneous regression of disc herniation is thought to occur via an inflammatory reaction with molecular mechanisms of phagocytic processes.
Bias-Robust Estimates of Regression Based on Projections
Maronna, Ricardo A.; Yohai, Victor J
1993-01-01
A new class of bias-robust estimates of multiple regression is introduced. If $y$ and $x$ are two real random variables, let $T(y, x)$ be a univariate robust estimate of regression of $y$ on $x$ through the origin. The regression estimate $\\mathbf{T}(y, \\mathbf{x})$ of a random variable $y$ on a random vector $\\mathbf{x} = (x_1,\\cdots, x_p)'$ is defined as the vector $\\mathbf{t} \\in \\mathfrak{R}^p$ which minimizes $\\sup_{\\|\\mathbf{\\lambda}\\| = 1} \\mid T(y - \\mathbf{t'x, \\lambda' x}) \\mid s(\\m...
Developmental regression in autism: research and conceptual questions
Directory of Open Access Journals (Sweden)
Carolina Lampreia
2013-11-01
Full Text Available The subject of developmental regression in autism has gained importance and a growing number of studies have been conducted in recent years. It is a major issue indicating that there is not a unique form of autism onset. However the phenomenon itself and the concept of regression have been the subject of some debate: there is no consensus on the existence of regression, as there is no consensus on its definition. The aim of this paper is to review the research literature in this area and to introduce some conceptual questions about its existence and its definition.
Estimation of transport airplane aerodynamics using multiple stepwise regression
Keskar, D. A.; Klein, V.; Batterson, J. G.
1985-01-01
This paper presents an application of multiple stepwise regression to the flight test data of a typical transport airplane. The flight test data was carefully preprocessed to eliminate aliasing, time skews and high frequency noise. The data consisted both of basic certification maneuvers, such as wind-up-turns and maneuvers suitable for parameter estimation, such as responses to elevator pulses and doublets. It is shown that the results of multiple stepwise regression techniques compare favorably with the results obtained from maximum likelihood estimation. Finally, it is concluded that multiple stepwise regression could be a fast economical way to estimate transport airplane aerodynamics.
Quantiles Regression Approach to Identifying the Determinant of Breastfeeding Duration
Mahdiyah; Norsiah Mohamed, Wan; Ibrahim, Kamarulzaman
In this study, quantiles regression approach is applied to the data of Malaysian Family Life Survey (MFLS), to identify factors which are significantly related to the different conditional quantiles of the breastfeeding duration. It is known that the classical linear regression methods are based on minimizing residual sum of squared, but quantiles regression use a mechanism which are based on the conditional median function and the full range of other conditional quantile functions. Overall, it is found that the period of breastfeeding is significantly related to place of living, religion and total number of children in the family.
FBH1 Catalyzes Regression of Stalled Replication Forks
DEFF Research Database (Denmark)
Fugger, Kasper; Mistrik, Martin; Neelsen, Kai J
2015-01-01
DNA replication fork perturbation is a major challenge to the maintenance of genome integrity. It has been suggested that processing of stalled forks might involve fork regression, in which the fork reverses and the two nascent DNA strands anneal. Here, we show that FBH1 catalyzes regression...... of a model replication fork in vitro and promotes fork regression in vivo in response to replication perturbation. Cells respond to fork stalling by activating checkpoint responses requiring signaling through stress-activated protein kinases. Importantly, we show that FBH1, through its helicase activity...... a model whereby FBH1 promotes early checkpoint signaling by remodeling of stalled DNA replication forks....
Spontaneous regression of metastases from melanoma: review of the literature
DEFF Research Database (Denmark)
Kalialis, Louise Vennegaard; Drzewiecki, Krzysztof T; Klyver, Helle
2009-01-01
Regression of metastatic melanoma is a rare event, and review of the literature reveals a total of 76 reported cases since 1866. The proposed mechanisms include immunologic, endocrine, inflammatory and metastatic tumour nutritional factors. We conclude from this review that although the precise...... mechanisms remain unknown, some event must trigger the immune system to produce a stronger than normal response that results in regression of the melanoma metastases. Immunologic studies of patients with regression may disclose the underlying mechanisms and lead to new therapies of disseminated melanoma....
ARC Code TI: Block-GP: Scalable Gaussian Process Regression
National Aeronautics and Space Administration — Block GP is a Gaussian Process regression framework for multimodal data, that can be an order of magnitude more scalable than existing state-of-the-art nonlinear...
Block-GP: Scalable Gaussian Process Regression for Multimodal Data
National Aeronautics and Space Administration — Regression problems on massive data sets are ubiquitous in many application domains including the Internet, earth and space sciences, and finances. In many cases,...
Principal components regression of body measurements in five ...
African Journals Online (AJOL)
Principal component regression can be used to classify independent and informative variables thereby eliminating redundant information for the purpose of reducing costs of chicken genetic programmes. Keywords: Body weight, Biometric traits, Principal component, Orthogonal, Eigenvalues and Linear measurement ...
Research and analyze of physical health using multiple regression analysis
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T. S. Kyi
2014-01-01
Full Text Available This paper represents the research which is trying to create a mathematical model of the "healthy people" using the method of regression analysis. The factors are the physical parameters of the person (such as heart rate, lung capacity, blood pressure, breath holding, weight height coefficient, flexibility of the spine, muscles of the shoulder belt, abdominal muscles, squatting, etc.., and the response variable is an indicator of physical working capacity. After performing multiple regression analysis, obtained useful multiple regression models that can predict the physical performance of boys the aged of fourteen to seventeen years. This paper represents the development of regression model for the sixteen year old boys and analyzed results.
An Efficient Local Algorithm for Distributed Multivariate Regression
National Aeronautics and Space Administration — This paper offers a local distributed algorithm for multivariate regression in large peer-to-peer environments. The algorithm is designed for distributed...
A Scalable Local Algorithm for Distributed Multivariate Regression
National Aeronautics and Space Administration — This paper offers a local distributed algorithm for multivariate regression in large peer-to-peer environments. The algorithm can be used for distributed...
MODELING SNAKE MICROHABITAT FROM RADIOTELEMETRY STUDIES USING POLYTOMOUS LOGISTIC REGRESSION
Multivariate analysis of snake microhabitat has historically used techniques that were derived under assumptions of normality and common covariance structure (e.g., discriminant function analysis, MANOVA). In this study, polytomous logistic regression (PLR which does not require ...
Weighted Quantile Regression for AR model with Infinite Variance Errors.
Chen, Zhao; Li, Runze; Wu, Yaohua
2012-09-01
Autoregressive (AR) models with finite variance errors have been well studied. This paper is concerned with AR models with heavy-tailed errors, which is useful in various scientific research areas. Statistical estimation for AR models with infinite variance errors is very different from those for AR models with finite variance errors. In this paper, we consider a weighted quantile regression for AR models to deal with infinite variance errors. We further propose an induced smoothing method to deal with computational challenges in weighted quantile regression. We show that the difference between weighted quantile regression estimate and its smoothed version is negligible. We further propose a test for linear hypothesis on the regression coefficients. We conduct Monte Carlo simulation study to assess the finite sample performance of the proposed procedures. We illustrate the proposed methodology by an empirical analysis of a real-life data set.
Gaussian Process Regression for WDM System Performance Prediction
DEFF Research Database (Denmark)
Wass, Jesper; Thrane, Jakob; Piels, Molly
2017-01-01
Gaussian process regression is numerically and experimentally investigated to predict the bit error rate of a 24 x 28 CiBd QPSK WDM system. The proposed method produces accurate predictions from multi-dimensional and sparse measurement data....
Distributed Monitoring of the R2 Statistic for Linear Regression
National Aeronautics and Space Administration — The problem of monitoring a multivariate linear regression model is relevant in studying the evolving relationship between a set of input variables (features) and...
A VBA-based Simulation for Teaching Simple Linear Regression
Jones, Gregory Todd; Hagtvedt, Reidar; Jones, Kari
2004-01-01
In spite of the name, simple linear regression presents a number of conceptual difficulties, particularly for introductory students. This article describes a simulation tool that provides a hands-on method for illuminating the relationship between parameters and sample statistics.
Solenoidal filtering of volumetric velocity measurements using Gaussian process regression
Azijli, I.; Dwight, R.P.
2015-01-01
Volumetric velocity measurements of incompressible flows contain spurious divergence due to measurement noise, despite mass conservation dictating that the velocity field must be divergence-free (solenoidal). We investigate the use of Gaussian process regression to filter spurious divergence,
Testing for Stock Market Contagion: A Quantile Regression Approach
S.Y. Park (Sung); W. Wang (Wendun); N. Huang (Naijing)
2015-01-01
markdownabstract__Abstract__ Regarding the asymmetric and leptokurtic behavior of financial data, we propose a new contagion test in the quantile regression framework that is robust to model misspecification. Unlike conventional correlation-based tests, the proposed quantile contagion test
Radiation regression patterns after cobalt plaque insertion for retinoblastoma
Energy Technology Data Exchange (ETDEWEB)
Buys, R.J.; Abramson, D.H.; Ellsworth, R.M.; Haik, B.
1983-08-01
An analysis of 31 eyes of 30 patients who had been treated with cobalt plaques for retinoblastoma disclosed that a type I radiation regression pattern developed in 15 patients; type II, in one patient, and type III, in five patients. Nine patients had a regression pattern characterized by complete destruction of the tumor, the surrounding choroid, and all of the vessels in the area into which the plaque was inserted. This resulting white scar, corresponding to the sclerae only, was classified as a type IV radiation regression pattern. There was no evidence of tumor recurrence in patients with type IV regression patterns, with an average follow-up of 6.5 years, after receiving cobalt plaque therapy. Twenty-nine of these 30 patients had been unsuccessfully treated with at least one other modality (ie, light coagulation, cryotherapy, external beam radiation, or chemotherapy).
Synthesis analysis of regression models with a continuous outcome.
Zhou, Xiao-Hua; Hu, Nan; Hu, Guizhou; Root, Martin
2009-05-15
To estimate the multivariate regression model from multiple individual studies, it would be challenging to obtain results if the input from individual studies only provide univariate or incomplete multivariate regression information. Samsa et al. (J. Biomed. Biotechnol. 2005; 2:113-123) proposed a simple method to combine coefficients from univariate linear regression models into a multivariate linear regression model, a method known as synthesis analysis. However, the validity of this method relies on the normality assumption of the data, and it does not provide variance estimates. In this paper we propose a new synthesis method that improves on the existing synthesis method by eliminating the normality assumption, reducing bias, and allowing for the variance estimation of the estimated parameters. (c) 2009 John Wiley & Sons, Ltd.
BOX-COX REGRESSION METHOD IN TIME SCALING
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ATİLLA GÖKTAŞ
2013-06-01
Full Text Available Box-Cox regression method with λj, for j = 1, 2, ..., k, power transformation can be used when dependent variable and error term of the linear regression model do not satisfy the continuity and normality assumptions. The situation obtaining the smallest mean square error when optimum power λj, transformation for j = 1, 2, ..., k, of Y has been discussed. Box-Cox regression method is especially appropriate to adjust existence skewness or heteroscedasticity of error terms for a nonlinear functional relationship between dependent and explanatory variables. In this study, the advantage and disadvantage use of Box-Cox regression method have been discussed in differentiation and differantial analysis of time scale concept.
Exploration of walking behavior in Vermont using spatial regression.
2015-06-01
This report focuses on the relationship between walking and its contributing factors by : applying spatial regression methods. Using the Vermont data from the New England : Transportation Survey (NETS), walking variables as well as 170 independent va...
Segmented Regression Based on B-Splines with Solved Examples
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Miloš Kaňka
2015-12-01
Full Text Available The subject of the paper is segmented linear, quadratic, and cubic regression based on B-spline basis functions. In this article we expose the formulas for the computation of B-splines of order one, two, and three that is needed to construct linear, quadratic, and cubic regression. We list some interesting properties of these functions. For a clearer understanding we give the solutions of a couple of elementary exercises regarding these functions.
Ballistic limit curve regression for Freedom Station orbital debris shields
Jolly, William H.; Williamsen, Joel W.
1992-01-01
A procedure utilized at Marshall Space Flight Center to formulate ballistic limit curves for the Space Station Freedom's manned module orbital debris shields is presented. A stepwise linear least squares regression method similar to that employed by Burch (1967) is used to relate a penetration parameter to various projectile and target descriptors. A stepwise regression was also conducted with the model reduced to lower forms, thus eliminating the effects of generalized assumptions.
THE USE OF REGRESSION ANALYSIS IN MARKETING RESEARCH
DUMIRESCU Luigi; STANCIU Oana; TICHINDELEAN Mihai; VINEREAN Simona
2012-01-01
The purpose of the paper is to illustrate the applicability of the linear multiple regression model within a marketing research based on primary, quantitative data. The theoretical background of the developed regression model is the value-chain concept of relationship marketing. In this sense, the authors presume that the outcome variable of the model, the monetary value of one purchase, depends on the clients’ expectations regarding seven dimensions of the company’s offer. The paper is struc...
A Note on Implementing Box-Cox Quantile Regression
Wilke, Ralf A.; Fitzenberger, Bernd; Zhang, Xuan
2005-01-01
The Box-Cox quantile regression model using the two stage method introduced by Chamberlain (1994) and Buchinsky (1995) provides an attractive extension of linear quantile regression techniques. However, a major numerical problem exists when implementing this method which has not been addressed so far in the literature. We suggest a simple solution modifying the estimator slightly. This modification is easy to implement. The modified estimator is still [square root] n-consistent and its asympt...
An Original Stepwise Multilevel Logistic Regression Analysis of Discriminatory Accuracy
DEFF Research Database (Denmark)
Merlo, Juan; Wagner, Philippe; Ghith, Nermin
2016-01-01
BACKGROUND AND AIM: Many multilevel logistic regression analyses of "neighbourhood and health" focus on interpreting measures of associations (e.g., odds ratio, OR). In contrast, multilevel analysis of variance is rarely considered. We propose an original stepwise analytical approach that disting......BACKGROUND AND AIM: Many multilevel logistic regression analyses of "neighbourhood and health" focus on interpreting measures of associations (e.g., odds ratio, OR). In contrast, multilevel analysis of variance is rarely considered. We propose an original stepwise analytical approach...
Simulation Experiments in Practice: Statistical Design and Regression Analysis
Kleijnen, J.P.C.
2007-01-01
In practice, simulation analysts often change only one factor at a time, and use graphical analysis of the resulting Input/Output (I/O) data. The goal of this article is to change these traditional, naïve methods of design and analysis, because statistical theory proves that more information is obtained when applying Design Of Experiments (DOE) and linear regression analysis. Unfortunately, classic DOE and regression analysis assume a single simulation response that is normally and independen...
Adaptive Regression and Classification Models with Applications in Insurance
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Jekabsons Gints
2014-07-01
Full Text Available Nowadays, in the insurance industry the use of predictive modeling by means of regression and classification techniques is becoming increasingly important and popular. The success of an insurance company largely depends on the ability to perform such tasks as credibility estimation, determination of insurance premiums, estimation of probability of claim, detecting insurance fraud, managing insurance risk. This paper discusses regression and classification modeling for such types of prediction problems using the method of Adaptive Basis Function Construction
Multiple regression analyses in clinical child and adolescent psychology.
Jaccard, James; Guilamo-Ramos, Vincent; Johansson, Margaret; Bouris, Alida
2006-09-01
A major form of data analysis in clinical child and adolescent psychology is multiple regression. This article reviews issues in the application of such methods in light of the research designs typical of this field. Issues addressed include controlling covariates, evaluation of predictor relevance, comparing predictors, analysis of moderation, analysis of mediation, assumption violations, outliers, limited dependent variables, and directed regression and its relation to structural equation modeling. Analytic guidelines are provided within each domain.
On the relationship between regression analysis and mathematical programming
Dong Qian Wang; Stefanka Chukova; C. D. Lai
2004-01-01
The interaction between linear, quadratic programming and regression analysis are explored by both statistical and operations research methods. Estimation and optimization problems are formulated in two different ways: on one hand linear and quadratic programming problems are formulated and solved by statistical methods, and on the other hand the solution of the linear regression model with constraints makes use of the simplex methods of linear or quadratic programming. Example...
Hypothesis Testing of Parameters for Ordinary Linear Circular Regression
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Abdul Ghapor Hussin
2006-07-01
Full Text Available This paper presents the hypothesis testing of parameters for ordinary linear circular regression model assuming the circular random error distributed as von Misses distribution. The main interests are in testing of the intercept and slope parameter of the regression line. As an illustration, this hypothesis testing will be used in analyzing the wind and wave direction data recorded by two different techniques which are HF radar system and anchored wave buoy.
Real estate value prediction using multivariate regression models
Manjula, R.; Jain, Shubham; Srivastava, Sharad; Rajiv Kher, Pranav
2017-11-01
The real estate market is one of the most competitive in terms of pricing and the same tends to vary significantly based on a lot of factors, hence it becomes one of the prime fields to apply the concepts of machine learning to optimize and predict the prices with high accuracy. Therefore in this paper, we present various important features to use while predicting housing prices with good accuracy. We have described regression models, using various features to have lower Residual Sum of Squares error. While using features in a regression model some feature engineering is required for better prediction. Often a set of features (multiple regressions) or polynomial regression (applying a various set of powers in the features) is used for making better model fit. For these models are expected to be susceptible towards over fitting ridge regression is used to reduce it. This paper thus directs to the best application of regression models in addition to other techniques to optimize the result.
Direction of Effects in Multiple Linear Regression Models.
Wiedermann, Wolfgang; von Eye, Alexander
2015-01-01
Previous studies analyzed asymmetric properties of the Pearson correlation coefficient using higher than second order moments. These asymmetric properties can be used to determine the direction of dependence in a linear regression setting (i.e., establish which of two variables is more likely to be on the outcome side) within the framework of cross-sectional observational data. Extant approaches are restricted to the bivariate regression case. The present contribution extends the direction of dependence methodology to a multiple linear regression setting by analyzing distributional properties of residuals of competing multiple regression models. It is shown that, under certain conditions, the third central moments of estimated regression residuals can be used to decide upon direction of effects. In addition, three different approaches for statistical inference are discussed: a combined D'Agostino normality test, a skewness difference test, and a bootstrap difference test. Type I error and power of the procedures are assessed using Monte Carlo simulations, and an empirical example is provided for illustrative purposes. In the discussion, issues concerning the quality of psychological data, possible extensions of the proposed methods to the fourth central moment of regression residuals, and potential applications are addressed.
Regression models for the quantification of Parkinsonian bradykinesia.
Kim, Ji-Won; Kwon, Yuri; Yun, Ju-Seok; Heo, Jae-Hoon; Eom, Gwang-Moon; Tack, Gye-Rae; Lim, Tae-Hong; Koh, Seong-Beom
2015-01-01
The aim of this study was to develop regression models for the quantification of parkinsonian bradykinesia. Forty patients with Parkinson's disease participated in this study. Angular velocity was measured using gyro sensor during finger tapping, forearm-rotation, and toe tapping tasks and the severity of bradykinesia was rated by two independent neurologists. Various characteristic variables were derived from the sensor signal. Stepwise multiple linear regression analysis was performed to develop models predicting the bradykinesia score with the characteristic variables as input. To evaluate the ability of the regression models to discriminate different bradykinesia scores, ANOVA and post hoc test were performed. Major determinants of the bradykinesia score differed among clinical tasks and between raters. The regression models were better than any single characteristic variable in terms of the ability to differentiate bradykinesia scores. Specifically, the regression models could differentiate all pairs of the bradykinesia scores (pmultiple regression models reflecting these differences would be beneficial for the quantification of bradykinesia because the cardinal features included in the determination of bradykinesia score differ among tasks as well as among the raters.
Descriptor Learning via Supervised Manifold Regularization for Multioutput Regression.
Zhen, Xiantong; Yu, Mengyang; Islam, Ali; Bhaduri, Mousumi; Chan, Ian; Li, Shuo
2017-09-01
Multioutput regression has recently shown great ability to solve challenging problems in both computer vision and medical image analysis. However, due to the huge image variability and ambiguity, it is fundamentally challenging to handle the highly complex input-target relationship of multioutput regression, especially with indiscriminate high-dimensional representations. In this paper, we propose a novel supervised descriptor learning (SDL) algorithm for multioutput regression, which can establish discriminative and compact feature representations to improve the multivariate estimation performance. The SDL is formulated as generalized low-rank approximations of matrices with a supervised manifold regularization. The SDL is able to simultaneously extract discriminative features closely related to multivariate targets and remove irrelevant and redundant information by transforming raw features into a new low-dimensional space aligned to targets. The achieved discriminative while compact descriptor largely reduces the variability and ambiguity for multioutput regression, which enables more accurate and efficient multivariate estimation. We conduct extensive evaluation of the proposed SDL on both synthetic data and real-world multioutput regression tasks for both computer vision and medical image analysis. Experimental results have shown that the proposed SDL can achieve high multivariate estimation accuracy on all tasks and largely outperforms the algorithms in the state of the arts. Our method establishes a novel SDL framework for multioutput regression, which can be widely used to boost the performance in different applications.
bayesQR: A Bayesian Approach to Quantile Regression
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Dries F. Benoit
2017-01-01
Full Text Available After its introduction by Koenker and Basset (1978, quantile regression has become an important and popular tool to investigate the conditional response distribution in regression. The R package bayesQR contains a number of routines to estimate quantile regression parameters using a Bayesian approach based on the asymmetric Laplace distribution. The package contains functions for the typical quantile regression with continuous dependent variable, but also supports quantile regression for binary dependent variables. For both types of dependent variables, an approach to variable selection using the adaptive lasso approach is provided. For the binary quantile regression model, the package also contains a routine that calculates the fitted probabilities for each vector of predictors. In addition, functions for summarizing the results, creating traceplots, posterior histograms and drawing quantile plots are included. This paper starts with a brief overview of the theoretical background of the models used in the bayesQR package. The main part of this paper discusses the computational problems that arise in the implementation of the procedure and illustrates the usefulness of the package through selected examples.
Acute Regression in Young People with Down Syndrome
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Clotilde Mircher
2017-05-01
Full Text Available Abstract: Adolescents and young adults with Down syndrome (DS can present a rapid regression with loss of independence and daily skills. Causes of regression are unknown and treatment is most of the time symptomatic. We did a retrospective cohort study of regression cases: patients were born between 1959 and 2000, and were followed from 1984 to now. We found 30 DS patients aged 11 to 30 years old with history of regression. Regression occurred regardless of the cognitive level (severe, moderate, or mild intellectual disability (ID. Patients presented psychiatric symptoms (catatonia, depression, delusions, stereotypies, etc., partial or total loss of independence in activities of daily living (dressing, toilet, meals, and continence, language impairment (silence, whispered voice, etc., and loss of academic skills. All patients experienced severe emotional stress prior to regression, which may be considered the trigger. Partial or total recovery was observed for about 50% of them. In our cohort, girls were more frequently affected than boys (64%. Neurobiological hypotheses are discussed as well as preventative and therapeutic approaches.
Soyoung Park; Se-Yeong Hamm; Hang-Tak Jeon; Jinsoo Kim
2017-01-01
This study mapped and analyzed groundwater potential using two different models, logistic regression (LR) and multivariate adaptive regression splines (MARS), and compared the results. A spatial database was constructed for groundwater well data and groundwater influence factors. Groundwater well data with a high potential yield of ≥70 m3/d were extracted, and 859 locations (70%) were used for model training, whereas the other 365 locations (30%) were used for model validation. We analyzed 16...
Regression models for public health surveillance data: a simulation study.
Kim, H; Kriebel, D
2009-11-01
Poisson regression is now widely used in epidemiology, but researchers do not always evaluate the potential for bias in this method when the data are overdispersed. This study used simulated data to evaluate sources of overdispersion in public health surveillance data and compare alternative statistical models for analysing such data. If count data are overdispersed, Poisson regression will not correctly estimate the variance. A model called negative binomial 2 (NB2) can correct for overdispersion, and may be preferred for analysis of count data. This paper compared the performance of Poisson and NB2 regression with simulated overdispersed injury surveillance data. Monte Carlo simulation was used to assess the utility of the NB2 regression model as an alternative to Poisson regression for data which had several different sources of overdispersion. Simulated injury surveillance datasets were created in which an important predictor variable was omitted, as well as with an incorrect offset (denominator). The simulations evaluated the ability of Poisson regression and NB2 to correctly estimate the true determinants of injury and their confidence intervals. The NB2 model was effective in reducing overdispersion, but it could not reduce bias in point estimates which resulted from omitting a covariate which was a confounder, nor could it reduce bias from using an incorrect offset. One advantage of NB2 over Poisson for overdispersed data was that the confidence interval for a covariate was considerably wider with the former, providing an indication that the Poisson model did not fit well. When overdispersion is detected in a Poisson regression model, the NB2 model should be fit as an alternative. If there is no longer overdispersion, then the NB2 results may be preferred. However, it is important to remember that NB2 cannot correct for bias from omitted covariates or from using an incorrect offset.
Evaluation of Linear Regression Simultaneous Myoelectric Control Using Intramuscular EMG.
Smith, Lauren H; Kuiken, Todd A; Hargrove, Levi J
2016-04-01
The objective of this study was to evaluate the ability of linear regression models to decode patterns of muscle coactivation from intramuscular electromyogram (EMG) and provide simultaneous myoelectric control of a virtual 3-DOF wrist/hand system. Performance was compared to the simultaneous control of conventional myoelectric prosthesis methods using intramuscular EMG (parallel dual-site control)-an approach that requires users to independently modulate individual muscles in the residual limb, which can be challenging for amputees. Linear regression control was evaluated in eight able-bodied subjects during a virtual Fitts' law task and was compared to performance of eight subjects using parallel dual-site control. An offline analysis also evaluated how different types of training data affected prediction accuracy of linear regression control. The two control systems demonstrated similar overall performance; however, the linear regression method demonstrated improved performance for targets requiring use of all three DOFs, whereas parallel dual-site control demonstrated improved performance for targets that required use of only one DOF. Subjects using linear regression control could more easily activate multiple DOFs simultaneously, but often experienced unintended movements when trying to isolate individual DOFs. Offline analyses also suggested that the method used to train linear regression systems may influence controllability. Linear regression myoelectric control using intramuscular EMG provided an alternative to parallel dual-site control for 3-DOF simultaneous control at the wrist and hand. The two methods demonstrated different strengths in controllability, highlighting the tradeoff between providing simultaneous control and the ability to isolate individual DOFs when desired.
Alternative regression models to assess increase in childhood BMI
Directory of Open Access Journals (Sweden)
Mansmann Ulrich
2008-09-01
Full Text Available Abstract Background Body mass index (BMI data usually have skewed distributions, for which common statistical modeling approaches such as simple linear or logistic regression have limitations. Methods Different regression approaches to predict childhood BMI by goodness-of-fit measures and means of interpretation were compared including generalized linear models (GLMs, quantile regression and Generalized Additive Models for Location, Scale and Shape (GAMLSS. We analyzed data of 4967 children participating in the school entry health examination in Bavaria, Germany, from 2001 to 2002. TV watching, meal frequency, breastfeeding, smoking in pregnancy, maternal obesity, parental social class and weight gain in the first 2 years of life were considered as risk factors for obesity. Results GAMLSS showed a much better fit regarding the estimation of risk factors effects on transformed and untransformed BMI data than common GLMs with respect to the generalized Akaike information criterion. In comparison with GAMLSS, quantile regression allowed for additional interpretation of prespecified distribution quantiles, such as quantiles referring to overweight or obesity. The variables TV watching, maternal BMI and weight gain in the first 2 years were directly, and meal frequency was inversely significantly associated with body composition in any model type examined. In contrast, smoking in pregnancy was not directly, and breastfeeding and parental social class were not inversely significantly associated with body composition in GLM models, but in GAMLSS and partly in quantile regression models. Risk factor specific BMI percentile curves could be estimated from GAMLSS and quantile regression models. Conclusion GAMLSS and quantile regression seem to be more appropriate than common GLMs for risk factor modeling of BMI data.
Bioprocess data mining using regularized regression and random forests.
Hassan, Syeda; Farhan, Muhammad; Mangayil, Rahul; Huttunen, Heikki; Aho, Tommi
2013-01-01
In bioprocess development, the needs of data analysis include (1) getting overview to existing data sets, (2) identifying primary control parameters, (3) determining a useful control direction, and (4) planning future experiments. In particular, the integration of multiple data sets causes that these needs cannot be properly addressed by regression models that assume linear input-output relationship or unimodality of the response function. Regularized regression and random forests, on the other hand, have several properties that may appear important in this context. They are capable, e.g., in handling small number of samples with respect to the number of variables, feature selection, and the visualization of response surfaces in order to present the prediction results in an illustrative way. In this work, the applicability of regularized regression (Lasso) and random forests (RF) in bioprocess data mining was examined, and their performance was benchmarked against multiple linear regression. As an example, we used data from a culture media optimization study for microbial hydrogen production. All the three methods were capable in providing a significant model when the five variables of the culture media optimization were linearly included in modeling. However, multiple linear regression failed when also the multiplications and squares of the variables were included in modeling. In this case, the modeling was still successful with Lasso (correlation between the observed and predicted yield was 0.69) and RF (0.91). We found that both regularized regression and random forests were able to produce feasible models, and the latter was efficient in capturing the non-linearity in the data. In this kind of a data mining task of bioprocess data, both methods outperform multiple linear regression.
Crawford, John R; Garthwaite, Paul H; Denham, Annie K; Chelune, Gordon J
2012-12-01
Regression equations have many useful roles in psychological assessment. Moreover, there is a large reservoir of published data that could be used to build regression equations; these equations could then be employed to test a wide variety of hypotheses concerning the functioning of individual cases. This resource is currently underused because (a) not all psychologists are aware that regression equations can be built not only from raw data but also using only basic summary data for a sample, and (b) the computations involved are tedious and prone to error. In an attempt to overcome these barriers, Crawford and Garthwaite (2007) provided methods to build and apply simple linear regression models using summary statistics as data. In the present study, we extend this work to set out the steps required to build multiple regression models from sample summary statistics and the further steps required to compute the associated statistics for drawing inferences concerning an individual case. We also develop, describe, and make available a computer program that implements these methods. Although there are caveats associated with the use of the methods, these need to be balanced against pragmatic considerations and against the alternative of either entirely ignoring a pertinent data set or using it informally to provide a clinical "guesstimate." Upgraded versions of earlier programs for regression in the single case are also provided; these add the point and interval estimates of effect size developed in the present article.
Learning from data with localized regression and differential evolution
Buckner, Mark A.
2003-07-01
Learning from data is fast becoming the rule rather than the exception for many science and engineering research problems, particularly those encountered in nuclear engineering. Problems associated with learning from data fall under the more general category of inverse problems . A data-drive inverse problem involves constructing a predictive model of a target system from a collection of input/output observations. One of the difficulties associated with constructing a model that approximates such unknown causes based solely on observations of their effects is that collinearities in the input data result in the problem being ill-posed. Ill-posed problems cause models obtained by conventional techniques, such as linear regression, neural networks and kernel techniques, to become unstable, producing unreliable results. Methods of regularization using ordinary ridge regression (ORR) and kernel regression (KR) have been proposed as viable solutions to ill-posed problems. Successful application of ORR and KR require the selection of optimal parameter values---ridge parameters for ORR and bandwidth parameters for KR. The common practice for both methods is to select a single parameter based on minimizing an objective function which is an estimate of empirical risk. The single parameter value is then applied to all predictor variables indiscriminately, in a sort of one-size-fits-all fashion. Versions of ORR and KR have been proposed that make use of individual localized ridge and a matrix of localized bandwidth parameters that are optimally selected based on the relevance of their associated predictor variables to reducing empirical risk. While the practical and theoretical value of both localized regression techniques is recognized they have obtained limited use because of the difficulties associated with selecting multiple optimal ridge parameters for localized ridge regression (LRR)---defined as the localized ridge regression problem---and multiple optimal bandwidth
Many regression algorithms, one unified model: A review.
Stulp, Freek; Sigaud, Olivier
2015-09-01
Regression is the process of learning relationships between inputs and continuous outputs from example data, which enables predictions for novel inputs. The history of regression is closely related to the history of artificial neural networks since the seminal work of Rosenblatt (1958). The aims of this paper are to provide an overview of many regression algorithms, and to demonstrate how the function representation whose parameters they regress fall into two classes: a weighted sum of basis functions, or a mixture of linear models. Furthermore, we show that the former is a special case of the latter. Our ambition is thus to provide a deep understanding of the relationship between these algorithms, that, despite being derived from very different principles, use a function representation that can be captured within one unified model. Finally, step-by-step derivations of the algorithms from first principles and visualizations of their inner workings allow this article to be used as a tutorial for those new to regression. Copyright © 2015 Elsevier Ltd. All rights reserved.
Analysis of Sting Balance Calibration Data Using Optimized Regression Models
Ulbrich, N.; Bader, Jon B.
2010-01-01
Calibration data of a wind tunnel sting balance was processed using a candidate math model search algorithm that recommends an optimized regression model for the data analysis. During the calibration the normal force and the moment at the balance moment center were selected as independent calibration variables. The sting balance itself had two moment gages. Therefore, after analyzing the connection between calibration loads and gage outputs, it was decided to choose the difference and the sum of the gage outputs as the two responses that best describe the behavior of the balance. The math model search algorithm was applied to these two responses. An optimized regression model was obtained for each response. Classical strain gage balance load transformations and the equations of the deflection of a cantilever beam under load are used to show that the search algorithm s two optimized regression models are supported by a theoretical analysis of the relationship between the applied calibration loads and the measured gage outputs. The analysis of the sting balance calibration data set is a rare example of a situation when terms of a regression model of a balance can directly be derived from first principles of physics. In addition, it is interesting to note that the search algorithm recommended the correct regression model term combinations using only a set of statistical quality metrics that were applied to the experimental data during the algorithm s term selection process.
Digression and Value Concatenation to Enable Privacy-Preserving Regression.
Li, Xiao-Bai; Sarkar, Sumit
2014-09-01
Regression techniques can be used not only for legitimate data analysis, but also to infer private information about individuals. In this paper, we demonstrate that regression trees, a popular data-analysis and data-mining technique, can be used to effectively reveal individuals' sensitive data. This problem, which we call a "regression attack," has not been addressed in the data privacy literature, and existing privacy-preserving techniques are not appropriate in coping with this problem. We propose a new approach to counter regression attacks. To protect against privacy disclosure, our approach introduces a novel measure, called digression , which assesses the sensitive value disclosure risk in the process of building a regression tree model. Specifically, we develop an algorithm that uses the measure for pruning the tree to limit disclosure of sensitive data. We also propose a dynamic value-concatenation method for anonymizing data, which better preserves data utility than a user-defined generalization scheme commonly used in existing approaches. Our approach can be used for anonymizing both numeric and categorical data. An experimental study is conducted using real-world financial, economic and healthcare data. The results of the experiments demonstrate that the proposed approach is very effective in protecting data privacy while preserving data quality for research and analysis.
Simple and multiple linear regression: sample size considerations.
Hanley, James A
2016-11-01
The suggested "two subjects per variable" (2SPV) rule of thumb in the Austin and Steyerberg article is a chance to bring out some long-established and quite intuitive sample size considerations for both simple and multiple linear regression. This article distinguishes two of the major uses of regression models that imply very different sample size considerations, neither served well by the 2SPV rule. The first is etiological research, which contrasts mean Y levels at differing "exposure" (X) values and thus tends to focus on a single regression coefficient, possibly adjusted for confounders. The second research genre guides clinical practice. It addresses Y levels for individuals with different covariate patterns or "profiles." It focuses on the profile-specific (mean) Y levels themselves, estimating them via linear compounds of regression coefficients and covariates. By drawing on long-established closed-form variance formulae that lie beneath the standard errors in multiple regression, and by rearranging them for heuristic purposes, one arrives at quite intuitive sample size considerations for both research genres. Copyright Â© 2016 Elsevier Inc. All rights reserved.
Optimization of Regression Models of Experimental Data Using Confirmation Points
Ulbrich, N.
2010-01-01
A new search metric is discussed that may be used to better assess the predictive capability of different math term combinations during the optimization of a regression model of experimental data. The new search metric can be determined for each tested math term combination if the given experimental data set is split into two subsets. The first subset consists of data points that are only used to determine the coefficients of the regression model. The second subset consists of confirmation points that are exclusively used to test the regression model. The new search metric value is assigned after comparing two values that describe the quality of the fit of each subset. The first value is the standard deviation of the PRESS residuals of the data points. The second value is the standard deviation of the response residuals of the confirmation points. The greater of the two values is used as the new search metric value. This choice guarantees that both standard deviations are always less or equal to the value that is used during the optimization. Experimental data from the calibration of a wind tunnel strain-gage balance is used to illustrate the application of the new search metric. The new search metric ultimately generates an optimized regression model that was already tested at regression model independent confirmation points before it is ever used to predict an unknown response from a set of regressors.
Study of Rapid-Regression Liquefying Hybrid Rocket Fuels
Zilliac, Greg; DeZilwa, Shane; Karabeyoglu, M. Arif; Cantwell, Brian J.; Castellucci, Paul
2004-01-01
A report describes experiments directed toward the development of paraffin-based hybrid rocket fuels that burn at regression rates greater than those of conventional hybrid rocket fuels like hydroxyl-terminated butadiene. The basic approach followed in this development is to use materials such that a hydrodynamically unstable liquid layer forms on the melting surface of a burning fuel body. Entrainment of droplets from the liquid/gas interface can substantially increase the rate of fuel mass transfer, leading to surface regression faster than can be achieved using conventional fuels. The higher regression rate eliminates the need for the complex multi-port grain structures of conventional solid rocket fuels, making it possible to obtain acceptable performance from single-port structures. The high-regression-rate fuels contain no toxic or otherwise hazardous components and can be shipped commercially as non-hazardous commodities. Among the experiments performed on these fuels were scale-up tests using gaseous oxygen. The data from these tests were found to agree with data from small-scale, low-pressure and low-mass-flux laboratory tests and to confirm the expectation that these fuels would burn at high regression rates, chamber pressures, and mass fluxes representative of full-scale rocket motors.
A Multiple Regression Approach to Normalization of Spatiotemporal Gait Features.
Wahid, Ferdous; Begg, Rezaul; Lythgo, Noel; Hass, Chris J; Halgamuge, Saman; Ackland, David C
2016-04-01
Normalization of gait data is performed to reduce the effects of intersubject variations due to physical characteristics. This study reports a multiple regression normalization approach for spatiotemporal gait data that takes into account intersubject variations in self-selected walking speed and physical properties including age, height, body mass, and sex. Spatiotemporal gait data including stride length, cadence, stance time, double support time, and stride time were obtained from healthy subjects including 782 children, 71 adults, 29 elderly subjects, and 28 elderly Parkinson's disease (PD) patients. Data were normalized using standard dimensionless equations, a detrending method, and a multiple regression approach. After normalization using dimensionless equations and the detrending method, weak to moderate correlations between walking speed, physical properties, and spatiotemporal gait features were observed (0.01 multiple regression method reduced these correlations to weak values (|r| multiple regression approach revealed significant differences in these features as well as in cadence, stance time, and stride time. The proposed multiple regression normalization may be useful in machine learning, gait classification, and clinical evaluation of pathological gait patterns.
Sample Size Requirements for Traditional and Regression-Based Norms.
Oosterhuis, Hannah E M; van der Ark, L Andries; Sijtsma, Klaas
2016-04-01
Test norms enable determining the position of an individual test taker in the group. The most frequently used approach to obtain test norms is traditional norming. Regression-based norming may be more efficient than traditional norming and is rapidly growing in popularity, but little is known about its technical properties. A simulation study was conducted to compare the sample size requirements for traditional and regression-based norming by examining the 95% interpercentile ranges for percentile estimates as a function of sample size, norming method, size of covariate effects on the test score, test length, and number of answer categories in an item. Provided the assumptions of the linear regression model hold in the data, for a subdivision of the total group into eight equal-size subgroups, we found that regression-based norming requires samples 2.5 to 5.5 times smaller than traditional norming. Sample size requirements are presented for each norming method, test length, and number of answer categories. We emphasize that additional research is needed to establish sample size requirements when the assumptions of the linear regression model are violated. © The Author(s) 2015.
Regression Model Optimization for the Analysis of Experimental Data
Ulbrich, N.
2009-01-01
A candidate math model search algorithm was developed at Ames Research Center that determines a recommended math model for the multivariate regression analysis of experimental data. The search algorithm is applicable to classical regression analysis problems as well as wind tunnel strain gage balance calibration analysis applications. The algorithm compares the predictive capability of different regression models using the standard deviation of the PRESS residuals of the responses as a search metric. This search metric is minimized during the search. Singular value decomposition is used during the search to reject math models that lead to a singular solution of the regression analysis problem. Two threshold dependent constraints are also applied. The first constraint rejects math models with insignificant terms. The second constraint rejects math models with near-linear dependencies between terms. The math term hierarchy rule may also be applied as an optional constraint during or after the candidate math model search. The final term selection of the recommended math model depends on the regressor and response values of the data set, the user s function class combination choice, the user s constraint selections, and the result of the search metric minimization. A frequently used regression analysis example from the literature is used to illustrate the application of the search algorithm to experimental data.
Regression of Pathological Cardiac Hypertrophy: Signaling Pathways and Therapeutic Targets
Hou, Jianglong; Kang, Y. James
2012-01-01
Pathological cardiac hypertrophy is a key risk factor for heart failure. It is associated with increased interstitial fibrosis, cell death and cardiac dysfunction. The progression of pathological cardiac hypertrophy has long been considered as irreversible. However, recent clinical observations and experimental studies have produced evidence showing the reversal of pathological cardiac hypertrophy. Left ventricle assist devices used in heart failure patients for bridging to transplantation not only improve peripheral circulation but also often cause reverse remodeling of the geometry and recovery of the function of the heart. Dietary supplementation with physiologically relevant levels of copper can reverse pathological cardiac hypertrophy in mice. Angiogenesis is essential and vascular endothelial growth factor (VEGF) is a constitutive factor for the regression. The action of VEGF is mediated by VEGF receptor-1, whose activation is linked to cyclic GMP-dependent protein kinase-1 (PKG-1) signaling pathways, and inhibition of cyclic GMP degradation leads to regression of pathological cardiac hypertrophy. Most of these pathways are regulated by hypoxia-inducible factor. Potential therapeutic targets for promoting the regression include: promotion of angiogenesis, selective enhancement of VEGF receptor-1 signaling pathways, stimulation of PKG-1 pathways, and sustention of hypoxia-inducible factor transcriptional activity. More exciting insights into the regression of pathological cardiac hypertrophy are emerging. The time of translating the concept of regression of pathological cardiac hypertrophy to clinical practice is coming. PMID:22750195
Buffalos milk yield analysis using random regression models
Directory of Open Access Journals (Sweden)
A.S. Schierholt
2010-02-01
Full Text Available Data comprising 1,719 milk yield records from 357 females (predominantly Murrah breed, daughters of 110 sires, with births from 1974 to 2004, obtained from the Programa de Melhoramento Genético de Bubalinos (PROMEBUL and from records of EMBRAPA Amazônia Oriental - EAO herd, located in Belém, Pará, Brazil, were used to compare random regression models for estimating variance components and predicting breeding values of the sires. The data were analyzed by different models using the Legendre’s polynomial functions from second to fourth orders. The random regression models included the effects of herd-year, month of parity date of the control; regression coefficients for age of females (in order to describe the fixed part of the lactation curve and random regression coefficients related to the direct genetic and permanent environment effects. The comparisons among the models were based on the Akaike Infromation Criterion. The random effects regression model using third order Legendre’s polynomials with four classes of the environmental effect were the one that best described the additive genetic variation in milk yield. The heritability estimates varied from 0.08 to 0.40. The genetic correlation between milk yields in younger ages was close to the unit, but in older ages it was low.
Analyzing genotype-by-environment interaction using curvilinear regression
Directory of Open Access Journals (Sweden)
Dulce Gamito Santinhos Pereira
2012-12-01
Full Text Available In the context of multi-environment trials, where a series of experiments is conducted across different environmental conditions, the analysis of the structure of genotype-by-environment interaction is an important topic. This paper presents a generalization of the joint regression analysis for the cases where the response (e.g. yield is not linear across environments and can be written as a second (or higher order polynomial or another non-linear function. After identifying the common form regression function for all genotypes, we propose a selection procedure based on the adaptation of two tests: (i a test for parallelism of regression curves; and (ii a test of coincidence for those regressions. When the hypothesis of parallelism is rejected, subgroups of genotypes where the responses are parallel (or coincident should be identified. The use of the Scheffé multiple comparison method for regression coefficients in second-order polynomials allows to group the genotypes in two types of groups: one with upward-facing concavity (i.e. potential yield growth, and the other with downward-facing concavity (i.e. the yield approaches saturation. Theoretical results for genotype comparison and genotype selection are illustrated with an example of yield from a non-orthogonal series of experiments with winter rye (Secalecereale L.. We have deleted 10 % of that data at random to show that our meteorology is fully applicable to incomplete data sets, often observed in multi-environment trials.
Rastegari, Azam; Haghdoost, Ali Akbar; Baneshi, Mohammad Reza
2013-01-01
Due to the importance of medical studies, researchers of this field should be familiar with various types of statistical analyses to select the most appropriate method based on the characteristics of their data sets. Classification and regression trees (CARTs) can be as complementary to regression models. We compared the performance of a logistic regression model and a CART in predicting drug injection among prisoners. Data of 2720 Iranian prisoners was studied to determine the factors influencing drug injection. The collected data was divided into two groups of training and testing. A logistic regression model and a CART were applied on training data. The performance of the two models was then evaluated on testing data. The regression model and the CART had 8 and 4 significant variables, respectively. Overall, heroin use, history of imprisonment, age at first drug use, and marital status were important factors in determining the history of drug injection. Subjects without the history of heroin use or heroin users with short-term imprisonment were at lower risk of drug injection. Among heroin addicts with long-term imprisonment, individuals with higher age at first drug use and married subjects were at lower risk of drug injection. Although the logistic regression model was more sensitive than the CART, the two models had the same levels of specificity and classification accuracy. In this study, both sensitivity and specificity were important. While the logistic regression model had better performance, the graphical presentation of the CART simplifies the interpretation of the results. In general, a combination of different analytical methods is recommended to explore the effects of variables.
Multicollinearity in Regression Analyses Conducted in Epidemiologic Studies.
Vatcheva, Kristina P; Lee, MinJae; McCormick, Joseph B; Rahbar, Mohammad H
2016-04-01
The adverse impact of ignoring multicollinearity on findings and data interpretation in regression analysis is very well documented in the statistical literature. The failure to identify and report multicollinearity could result in misleading interpretations of the results. A review of epidemiological literature in PubMed from January 2004 to December 2013, illustrated the need for a greater attention to identifying and minimizing the effect of multicollinearity in analysis of data from epidemiologic studies. We used simulated datasets and real life data from the Cameron County Hispanic Cohort to demonstrate the adverse effects of multicollinearity in the regression analysis and encourage researchers to consider the diagnostic for multicollinearity as one of the steps in regression analysis.
Multiple regression for physiological data analysis: the problem of multicollinearity.
Slinker, B K; Glantz, S A
1985-07-01
Multiple linear regression, in which several predictor variables are related to a response variable, is a powerful statistical tool for gaining quantitative insight into complex in vivo physiological systems. For these insights to be correct, all predictor variables must be uncorrelated. However, in many physiological experiments the predictor variables cannot be precisely controlled and thus change in parallel (i.e., they are highly correlated). There is a redundancy of information about the response, a situation called multicollinearity, that leads to numerical problems in estimating the parameters in regression equations; the parameters are often of incorrect magnitude or sign or have large standard errors. Although multicollinearity can be avoided with good experimental design, not all interesting physiological questions can be studied without encountering multicollinearity. In these cases various ad hoc procedures have been proposed to mitigate multicollinearity. Although many of these procedures are controversial, they can be helpful in applying multiple linear regression to some physiological problems.
Analysis of some methods for reduced rank Gaussian process regression
DEFF Research Database (Denmark)
Quinonero-Candela, J.; Rasmussen, Carl Edward
2005-01-01
proliferation of a number of cost-effective approximations to GPs, both for classification and for regression. In this paper we analyze one popular approximation to GPs for regression: the reduced rank approximation. While generally GPs are equivalent to infinite linear models, we show that Reduced Rank......While there is strong motivation for using Gaussian Processes (GPs) due to their excellent performance in regression and classification problems, their computational complexity makes them impractical when the size of the training set exceeds a few thousand cases. This has motivated the recent...... Gaussian Processes (RRGPs) are equivalent to finite sparse linear models. We also introduce the concept of degenerate GPs and show that they correspond to inappropriate priors. We show how to modify the RRGP to prevent it from being degenerate at test time. Training RRGPs consists both in learning...
Spinocerebellar ataxia type 2 presenting with cognitive regression in childhood.
Ramocki, Melissa B; Chapieski, Lynn; McDonald, Ryan O; Fernandez, Fabio; Malphrus, Amy D
2008-09-01
Spinocerebellar ataxia type 2 typically presents in adulthood with progressive ataxia, dysarthria, tremor, and slow saccadic eye movements. Childhood-onset spinocerebellar ataxia type 2 is rare, and only the infantile-onset form has been well characterized clinically. This article describes a girl who met all developmental milestones until age 3(1/2) years, when she experienced cognitive regression that preceded motor regression by 6 months. A diagnosis of spinocerebellar ataxia type 2 was delayed until she presented to the emergency department at age 7 years. This report documents the results of her neuropsychologic evaluation at both time points. This case broadens the spectrum of spinocerebellar ataxia type 2 presentation in childhood, highlights the importance of considering a spinocerebellar ataxia in a child who presents with cognitive regression only, and extends currently available clinical information to help clinicians discuss the prognosis in childhood spinocerebellar ataxia type 2.
Least Squares Adjustment: Linear and Nonlinear Weighted Regression Analysis
DEFF Research Database (Denmark)
Nielsen, Allan Aasbjerg
2007-01-01
This note primarily describes the mathematics of least squares regression analysis as it is often used in geodesy including land surveying and satellite positioning applications. In these fields regression is often termed adjustment. The note also contains a couple of typical land surveying...... and satellite positioning application examples. In these application areas we are typically interested in the parameters in the model typically 2- or 3-D positions and not in predictive modelling which is often the main concern in other regression analysis applications. Adjustment is often used to obtain...... estimates of relevant parameters in an over-determined system of equations which may arise from deliberately carrying out more measurements than actually needed to determine the set of desired parameters. An example may be the determination of a geographical position based on information from a number...
Norming clinical questionnaires with multiple regression: the Pain Cognition List.
Van Breukelen, Gerard J P; Vlaeyen, Johan W S
2005-09-01
Questionnaires for measuring patients' feelings or beliefs are commonly used in clinical settings for diagnostic purposes, clinical decision making, or treatment evaluation. Raw scores of a patient can be evaluated by comparing them with norms based on a reference population. Using the Pain Cognition List (PCL-2003) as an example, this article shows how clinical questionnaires can be normed with multiple regression of raw scores on demographic and other patient variables. Compared with traditional norm tables for subgroups based on age or gender, this approach offers 2 advantages. First, multiple regression allows determination of which patient variables are relevant to the norming and which are not (validity). Second, by using information from the entire sample, multiple regression leads to continuous and more stable norms for any subgroup defined in terms of prognostic variables (reliability).
Omnibus hypothesis testing in dominance-based ordinal multiple regression.
Long, Jeffrey D
2005-09-01
Often quantitative data in the social sciences have only ordinal justification. Problems of interpretation can arise when least squares multiple regression (LSMR) is used with ordinal data. Two ordinal alternatives are discussed, dominance-based ordinal multiple regression (DOMR) and proportional odds multiple regression. The Q2 statistic is introduced for testing the omnibus null hypothesis in DOMR. A simulation study is discussed that examines the actual Type I error rate and power of Q2 in comparison to the LSMR omnibus F test under normality and non-normality. Results suggest that Q2 has favorable sampling properties as long as the sample size-to-predictors ratio is not too small, and Q2 can be a good alternative to the omnibus F test when the response variable is non-normal. Copyright 2005 APA, all rights reserved.
Paredes, B E
2007-11-01
Malignant melanoma is a neoplasm that more often tends to undergo regression. Clinically, variation in color is perhaps the most important hallmark of primary cutaneous melanoma. The change in color to white, off-white, blue-white and gray-white is a sign of (spontaneous) regression in malignant melanoma. Histopathologically the process starts with a dense lichenoid infiltrate of lymphocytes, and ends with fibrosis and/or melanosis within a thickened papillary dermis. The dense infiltrate of lymphocytes permeates the thin melanoma and destroys the atypical melanocytes in the epidermis and the papillary dermis. A key concern is how to define regression in a reproducible way. Using the following definition, a statistically significant risk of metastases can be demonstrated in thin melanomas (50%): "fibroplasia with an absence of epidermal and dermal involvement by melanoma cells, but allowing for (lentiginous) single-cell proliferation of atypical melanocytes along the dermo-epidermal junction".
On computation of solid fuel regression rate in ramjet combustor
Razmyslov, A. V.; Yanovskiy, L. S.; Toktaliev, P. D.
2017-11-01
Development of a solid fuel ramjets requires mathematical modeling of the solid fuel regression inside a combustor with accounting of solid fuel gasification and combustion processes and a numerical method to calculate parameters of these processes. This report presents a quasi-one-dimensional model of processes inside the solid fuel ramjet combustor. The model allows to calculate the solid fuel regression rate and gas flow parameters at the combustor outlet while air flow parameters at the combustor inlet are fixed. The model is based on mass, energy, species and momentum conservation equations and deals with thermochemical processes inside the ramjet combustor. It considers gas flow inside the combustor, gasified fuel combustion, convective heat transfer, solid fuel pyrolysis kinetics. The model is verified by comparison of the numerical results with the experimental data available from other authors. The analysis of the numerical results shows a dependence of the flow structure and thermochemical parameters of a solid fuel employed on the regression rate.
Wavelet regression model in forecasting crude oil price
Hamid, Mohd Helmie; Shabri, Ani
2017-05-01
This study presents the performance of wavelet multiple linear regression (WMLR) technique in daily crude oil forecasting. WMLR model was developed by integrating the discrete wavelet transform (DWT) and multiple linear regression (MLR) model. The original time series was decomposed to sub-time series with different scales by wavelet theory. Correlation analysis was conducted to assist in the selection of optimal decomposed components as inputs for the WMLR model. The daily WTI crude oil price series has been used in this study to test the prediction capability of the proposed model. The forecasting performance of WMLR model were also compared with regular multiple linear regression (MLR), Autoregressive Moving Average (ARIMA) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) using root mean square errors (RMSE) and mean absolute errors (MAE). Based on the experimental results, it appears that the WMLR model performs better than the other forecasting technique tested in this study.
Approaches to Low Fuel Regression Rate in Hybrid Rocket Engines
Directory of Open Access Journals (Sweden)
Dario Pastrone
2012-01-01
Full Text Available Hybrid rocket engines are promising propulsion systems which present appealing features such as safety, low cost, and environmental friendliness. On the other hand, certain issues hamper the development hoped for. The present paper discusses approaches addressing improvements to one of the most important among these issues: low fuel regression rate. To highlight the consequence of such an issue and to better understand the concepts proposed, fundamentals are summarized. Two approaches are presented (multiport grain and high mixture ratio which aim at reducing negative effects without enhancing regression rate. Furthermore, fuel material changes and nonconventional geometries of grain and/or injector are presented as methods to increase fuel regression rate. Although most of these approaches are still at the laboratory or concept scale, many of them are promising.
Analysis of the labor productivity of enterprises via quantile regression
Türkan, Semra
2017-07-01
In this study, we have analyzed the factors that affect the performance of Turkey's Top 500 Industrial Enterprises using quantile regression. The variable about labor productivity of enterprises is considered as dependent variable, the variableabout assets is considered as independent variable. The distribution of labor productivity of enterprises is right-skewed. If the dependent distribution is skewed, linear regression could not catch important aspects of the relationships between the dependent variable and its predictors due to modeling only the conditional mean. Hence, the quantile regression, which allows modelingany quantilesof the dependent distribution, including the median,appears to be useful. It examines whether relationships between dependent and independent variables are different for low, medium, and high percentiles. As a result of analyzing data, the effect of total assets is relatively constant over the entire distribution, except the upper tail. It hasa moderately stronger effect in the upper tail.
Empirical Bayes estimation for additive hazards regression models.
Sinha, Debajyoti; McHenry, M Brent; Lipsitz, Stuart R; Ghosh, Malay
2009-09-01
We develop a novel empirical Bayesian framework for the semiparametric additive hazards regression model. The integrated likelihood, obtained by integration over the unknown prior of the nonparametric baseline cumulative hazard, can be maximized using standard statistical software. Unlike the corresponding full Bayes method, our empirical Bayes estimators of regression parameters, survival curves and their corresponding standard errors have easily computed closed-form expressions and require no elicitation of hyperparameters of the prior. The method guarantees a monotone estimator of the survival function and accommodates time-varying regression coefficients and covariates. To facilitate frequentist-type inference based on large-sample approximation, we present the asymptotic properties of the semiparametric empirical Bayes estimates. We illustrate the implementation and advantages of our methodology with a reanalysis of a survival dataset and a simulation study.
Adrenocorticotropic hormone in the aetiology and regression of neuroblastoma.
Tucker, Graeme R
2002-08-01
Neuroblastoma is predominantly a paediatric neoplasm of the sympathetic nervous system. Despite the aggressive nature of the disease, spontaneous regression is frequently observed in infants diagnosed under the age of 12 months; especially with a specific stage referred to as stage 4s. Discovering the conditions, the elements, the mechanism and the indices behind this regression phenomenon could have therapeutic potential for prevention and cure. A review of the literature has implicated adrenocorticotropin hormone in both the aetiology and spontaneous regression of neuroblastoma. Manipulation of adrenocorticotropin hormone may offer hope for prevention and cure. Ingestible products such as retinoic acid, glycyrrhizic acid, salsolinol and ketoconazole acting in concert, could represent instrumental tools in a therapeutic manipulation process.
Dynamic Endothelial Cell Rearrangements Drive Developmental Vessel Regression
Franco, Claudio A.; Jones, Martin L.; Bernabeu, Miguel O.; Geudens, Ilse; Mathivet, Thomas; Rosa, Andre; Lopes, Felicia M.; Lima, Aida P.; Ragab, Anan; Collins, Russell T.; Phng, Li-Kun; Coveney, Peter V.; Gerhardt, Holger
2015-01-01
Patterning of functional blood vessel networks is achieved by pruning of superfluous connections. The cellular and molecular principles of vessel regression are poorly understood. Here we show that regression is mediated by dynamic and polarized migration of endothelial cells, representing anastomosis in reverse. Establishing and analyzing the first axial polarity map of all endothelial cells in a remodeling vascular network, we propose that balanced movement of cells maintains the primitive plexus under low shear conditions in a metastable dynamic state. We predict that flow-induced polarized migration of endothelial cells breaks symmetry and leads to stabilization of high flow/shear segments and regression of adjacent low flow/shear segments. PMID:25884288
FBH1 Catalyzes Regression of Stalled Replication Forks
Directory of Open Access Journals (Sweden)
Kasper Fugger
2015-03-01
Full Text Available DNA replication fork perturbation is a major challenge to the maintenance of genome integrity. It has been suggested that processing of stalled forks might involve fork regression, in which the fork reverses and the two nascent DNA strands anneal. Here, we show that FBH1 catalyzes regression of a model replication fork in vitro and promotes fork regression in vivo in response to replication perturbation. Cells respond to fork stalling by activating checkpoint responses requiring signaling through stress-activated protein kinases. Importantly, we show that FBH1, through its helicase activity, is required for early phosphorylation of ATM substrates such as CHK2 and CtIP as well as hyperphosphorylation of RPA. These phosphorylations occur prior to apparent DNA double-strand break formation. Furthermore, FBH1-dependent signaling promotes checkpoint control and preserves genome integrity. We propose a model whereby FBH1 promotes early checkpoint signaling by remodeling of stalled DNA replication forks.
Nonparametric instrumental regression with non-convex constraints
Grasmair, M.; Scherzer, O.; Vanhems, A.
2013-03-01
This paper considers the nonparametric regression model with an additive error that is dependent on the explanatory variables. As is common in empirical studies in epidemiology and economics, it also supposes that valid instrumental variables are observed. A classical example in microeconomics considers the consumer demand function as a function of the price of goods and the income, both variables often considered as endogenous. In this framework, the economic theory also imposes shape restrictions on the demand function, such as integrability conditions. Motivated by this illustration in microeconomics, we study an estimator of a nonparametric constrained regression function using instrumental variables by means of Tikhonov regularization. We derive rates of convergence for the regularized model both in a deterministic and stochastic setting under the assumption that the true regression function satisfies a projected source condition including, because of the non-convexity of the imposed constraints, an additional smallness condition.
Joint regression analysis and AMMI model applied to oat improvement
Oliveira, A.; Oliveira, T. A.; Mejza, S.
2012-09-01
In our work we present an application of some biometrical methods useful in genotype stability evaluation, namely AMMI model, Joint Regression Analysis (JRA) and multiple comparison tests. A genotype stability analysis of oat (Avena Sativa L.) grain yield was carried out using data of the Portuguese Plant Breeding Board, sample of the 22 different genotypes during the years 2002, 2003 and 2004 in six locations. In Ferreira et al. (2006) the authors state the relevance of the regression models and of the Additive Main Effects and Multiplicative Interactions (AMMI) model, to study and to estimate phenotypic stability effects. As computational techniques we use the Zigzag algorithm to estimate the regression coefficients and the agricolae-package available in R software for AMMI model analysis.
Sparse Regression by Projection and Sparse Discriminant Analysis
Qi, Xin
2015-04-03
© 2015, © American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America. Recent years have seen active developments of various penalized regression methods, such as LASSO and elastic net, to analyze high-dimensional data. In these approaches, the direction and length of the regression coefficients are determined simultaneously. Due to the introduction of penalties, the length of the estimates can be far from being optimal for accurate predictions. We introduce a new framework, regression by projection, and its sparse version to analyze high-dimensional data. The unique nature of this framework is that the directions of the regression coefficients are inferred first, and the lengths and the tuning parameters are determined by a cross-validation procedure to achieve the largest prediction accuracy. We provide a theoretical result for simultaneous model selection consistency and parameter estimation consistency of our method in high dimension. This new framework is then generalized such that it can be applied to principal components analysis, partial least squares, and canonical correlation analysis. We also adapt this framework for discriminant analysis. Compared with the existing methods, where there is relatively little control of the dependency among the sparse components, our method can control the relationships among the components. We present efficient algorithms and related theory for solving the sparse regression by projection problem. Based on extensive simulations and real data analysis, we demonstrate that our method achieves good predictive performance and variable selection in the regression setting, and the ability to control relationships between the sparse components leads to more accurate classification. In supplementary materials available online, the details of the algorithms and theoretical proofs, and R codes for all simulation studies are provided.
Robust regression and its application in absolute gravimeters
Hu, M.; Zhang, W. M.; Zhong, M.
2017-05-01
Maximum-likelihood estimation of robust regression is employed to process absolute gravimeter data because this method is insensitive to outliers. Gravity values obtained by both ordinary least squares and different approaches for robust regression are compared with the results obtained by the g9 processing software for the FG5 gravimeter. The results show that the accuracy of gravity data can be improved by rejecting certain outliers via adjustments to a suitable weighting function of time-distance pairs. A convenient method of identifying those outliers by their weight is also presented.
Regression modeling of consumption or exposure variables classified by type.
Dorfman, A; Kimball, A W; Friedman, L A
1985-12-01
Consumption or exposure variables, as potential risk factors, are commonly measured and related to health effects. The measurements may be continuous or discrete, may be grouped into categories and may, in addition, be classified by type. Data analyses utilizing regression methods for the assessment of these risk factors present many problems of modeling and interpretation. Various models are proposed and evaluated, and recommendations are made. Use of the models is illustrated with Cox regression analyses of coronary heart disease mortality after 24 years of follow-up of subjects in the Framingham Study, with the focus being on alcohol consumption among these subjects.
Determinants of Non-Performing Assets in India - Panel Regression
Directory of Open Access Journals (Sweden)
Saikat Ghosh Roy
2014-12-01
Full Text Available It is well known that level of banks‟ credit plays an important role in economic developments. Indian banking sector has played a seminal role in supporting economic growth in India. Recently, Indian banks are experiencing consistent increase in non-performing assets (NPA. In this perspective, this paper investigates the trends in NPA in Indian banks and its determinants. The panel regressions, fixed effect allows evaluating the impact of selected macroeconomic variables on the NPA. The Panel regression result indicates that the GDP growth, change in exchange rate and global volatility have major effects on the NPA level of Indian banking sector.
Geographically weighted regression and multicollinearity: dispelling the myth
Fotheringham, A. Stewart; Oshan, Taylor M.
2016-10-01
Geographically weighted regression (GWR) extends the familiar regression framework by estimating a set of parameters for any number of locations within a study area, rather than producing a single parameter estimate for each relationship specified in the model. Recent literature has suggested that GWR is highly susceptible to the effects of multicollinearity between explanatory variables and has proposed a series of local measures of multicollinearity as an indicator of potential problems. In this paper, we employ a controlled simulation to demonstrate that GWR is in fact very robust to the effects of multicollinearity. Consequently, the contention that GWR is highly susceptible to multicollinearity issues needs rethinking.
Cardiovascular Response Identification Based on Nonlinear Support Vector Regression
Wang, Lu; Su, Steven W.; Chan, Gregory S. H.; Celler, Branko G.; Cheng, Teddy M.; Savkin, Andrey V.
This study experimentally investigates the relationships between central cardiovascular variables and oxygen uptake based on nonlinear analysis and modeling. Ten healthy subjects were studied using cycle-ergometry exercise tests with constant workloads ranging from 25 Watt to 125 Watt. Breath by breath gas exchange, heart rate, cardiac output, stroke volume and blood pressure were measured at each stage. The modeling results proved that the nonlinear modeling method (Support Vector Regression) outperforms traditional regression method (reducing Estimation Error between 59% and 80%, reducing Testing Error between 53% and 72%) and is the ideal approach in the modeling of physiological data, especially with small training data set.
A switching regression analysis of urban population densities.
Brueckner, J K
1986-03-01
An application of the switching regression technique in the field of urban economics is presented. The technique is applied to the study of urban population density functions, which recent research has suggested are inherently discontinuous. The method of switching regression developed by Quandt is used to estimate density functions for selected U.S. urban areas. The results show that population density contours are highly irregular, and also that the model selection approach can be used to select the number of regimes in a switching model when this number is unknown
Sugarcane Land Classification with Satellite Imagery using Logistic Regression Model
Henry, F.; Herwindiati, D. E.; Mulyono, S.; Hendryli, J.
2017-03-01
This paper discusses the classification of sugarcane plantation area from Landsat-8 satellite imagery. The classification process uses binary logistic regression method with time series data of normalized difference vegetation index as input. The process is divided into two steps: training and classification. The purpose of training step is to identify the best parameter of the regression model using gradient descent algorithm. The best fit of the model can be utilized to classify sugarcane and non-sugarcane area. The experiment shows high accuracy and successfully maps the sugarcane plantation area which obtained best result of Cohen’s Kappa value 0.7833 (strong) with 89.167% accuracy.
A fitter use of Monte Carlo simulations in regression models
Directory of Open Access Journals (Sweden)
Alessandro Ferrarini
2011-12-01
Full Text Available In this article, I focus on the use of Monte Carlo simulations (MCS within regression models, being this application very frequent in biology, ecology and economy as well. I'm interested in enhancing a typical fault in this application of MCS, i.e. the inner correlations among independent variables are not used when generating random numbers that fit their distributions. By means of an illustrative example, I provide proof that the misuse of MCS in regression models produces misleading results. Furthermore, I also provide a solution for this topic.
Tax Evasion, Information Reporting, and the Regressive Bias Prediction
DEFF Research Database (Denmark)
Boserup, Simon Halphen; Pinje, Jori Veng
2013-01-01
Models of rational tax evasion and optimal enforcement invariably predict a regressive bias in the effective tax system, which reduces redistribution in the economy. Using Danish administrative data, we show that a calibrated structural model of this type replicates moments and correlations of tax...... evasion and audit probabilities once we account for information reporting in the tax compliance game. When conditioning on information reporting, we find that both reduced-form evidence and simulations exhibit the predicted regressive bias. However, in the overall economy, this bias is negated by the tax...... agency's use of information reports and revenue-maximizing disposition of audit resources....
[Regression of coronary arteriosclerosis with hypolipidemic treatment, myth or reality?].
Lahoz, C; Monereo, A; Mostaza, J M; de Oya, M
1992-11-01
Coronary atherosclerosis regression with hypolipemiant treatment is a well known fact in the animal model since years ago. In humans, during these last years, several clinical trials have been performed to ellucidate the truth to this fact. All of these clinical trials have in common the evolutive study of the coronary lesions with angiographies, in patients following treatment with diet, surgery or drugs, to reduce plasmatic cholesterol. Clinical, analytical and angiographic results of said studies are reviewed. We conclude that the bigger the lowering in plasmatic cholesterol levels, smaller is the progression of these coronary lesions and more probable is finding patients with partial regression of the lesions.
Online and Batch Supervised Background Estimation via L1 Regression
Dutta, Aritra
2017-11-23
We propose a surprisingly simple model for supervised video background estimation. Our model is based on $\\\\ell_1$ regression. As existing methods for $\\\\ell_1$ regression do not scale to high-resolution videos, we propose several simple and scalable methods for solving the problem, including iteratively reweighted least squares, a homotopy method, and stochastic gradient descent. We show through extensive experiments that our model and methods match or outperform the state-of-the-art online and batch methods in virtually all quantitative and qualitative measures.
Using the Regression Model in multivariate data analysis
Directory of Open Access Journals (Sweden)
Constantin Cristinel
2017-07-01
Full Text Available This paper is about an instrumental research regarding the using of Linear Regression Model for data analysis. The research uses a model based on real data and stress the necessity of a correct utilisation of such models in order to obtain accurate information for the decision makers. The main scope is to help practitioners and researchers in their efforts to build prediction models based on linear regressions. The conclusion reveals the necessity to use quantitative data for a correct model specification and to validate the model according to the assumptions of the least squares method.
Henrard, S; Speybroeck, N; Hermans, C
2015-11-01
Haemophilia is a rare genetic haemorrhagic disease characterized by partial or complete deficiency of coagulation factor VIII, for haemophilia A, or IX, for haemophilia B. As in any other medical research domain, the field of haemophilia research is increasingly concerned with finding factors associated with binary or continuous outcomes through multivariable models. Traditional models include multiple logistic regressions, for binary outcomes, and multiple linear regressions for continuous outcomes. Yet these regression models are at times difficult to implement, especially for non-statisticians, and can be difficult to interpret. The present paper sought to didactically explain how, why, and when to use classification and regression tree (CART) analysis for haemophilia research. The CART method is non-parametric and non-linear, based on the repeated partitioning of a sample into subgroups based on a certain criterion. Breiman developed this method in 1984. Classification trees (CTs) are used to analyse categorical outcomes and regression trees (RTs) to analyse continuous ones. The CART methodology has become increasingly popular in the medical field, yet only a few examples of studies using this methodology specifically in haemophilia have to date been published. Two examples using CART analysis and previously published in this field are didactically explained in details. There is increasing interest in using CART analysis in the health domain, primarily due to its ease of implementation, use, and interpretation, thus facilitating medical decision-making. This method should be promoted for analysing continuous or categorical outcomes in haemophilia, when applicable. © 2015 John Wiley & Sons Ltd.
Soh, Chang-Heok; Harrington, David P; Zaslavsky, Alan M
2008-03-01
When variable selection with stepwise regression and model fitting are conducted on the same data set, competition for inclusion in the model induces a selection bias in coefficient estimators away from zero. In proportional hazards regression with right-censored data, selection bias inflates the absolute value of parameter estimate of selected parameters, while the omission of other variables may shrink coefficients toward zero. This paper explores the extent of the bias in parameter estimates from stepwise proportional hazards regression and proposes a bootstrap method, similar to those proposed by Miller (Subset Selection in Regression, 2nd edn. Chapman & Hall/CRC, 2002) for linear regression, to correct for selection bias. We also use bootstrap methods to estimate the standard error of the adjusted estimators. Simulation results show that substantial biases could be present in uncorrected stepwise estimators and, for binary covariates, could exceed 250% of the true parameter value. The simulations also show that the conditional mean of the proposed bootstrap bias-corrected parameter estimator, given that a variable is selected, is moved closer to the unconditional mean of the standard partial likelihood estimator in the chosen model, and to the population value of the parameter. We also explore the effect of the adjustment on estimates of log relative risk, given the values of the covariates in a selected model. The proposed method is illustrated with data sets in primary biliary cirrhosis and in multiple myeloma from the Eastern Cooperative Oncology Group.
DEFF Research Database (Denmark)
Bjerrum, Kirsten Birgitte; Azzarolo, A.M.
1995-01-01
Ophthalmology, hypophysectomi, rats regression, lacrimal galnds, restoration, dihydrotestosterone, prolactin......Ophthalmology, hypophysectomi, rats regression, lacrimal galnds, restoration, dihydrotestosterone, prolactin...
Khoshravesh, Mojtaba; Sefidkouhi, Mohammad Ali Gholami; Valipour, Mohammad
2017-07-01
The proper evaluation of evapotranspiration is essential in food security investigation, farm management, pollution detection, irrigation scheduling, nutrient flows, carbon balance as well as hydrologic modeling, especially in arid environments. To achieve sustainable development and to ensure water supply, especially in arid environments, irrigation experts need tools to estimate reference evapotranspiration on a large scale. In this study, the monthly reference evapotranspiration was estimated by three different regression models including the multivariate fractional polynomial (MFP), robust regression, and Bayesian regression in Ardestan, Esfahan, and Kashan. The results were compared with Food and Agriculture Organization (FAO)-Penman-Monteith (FAO-PM) to select the best model. The results show that at a monthly scale, all models provided a closer agreement with the calculated values for FAO-PM ( R 2 > 0.95 and RMSE < 12.07 mm month-1). However, the MFP model gives better estimates than the other two models for estimating reference evapotranspiration at all stations.
DYNA3D/ParaDyn Regression Test Suite Inventory
Energy Technology Data Exchange (ETDEWEB)
Lin, Jerry I. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
2016-09-01
The following table constitutes an initial assessment of feature coverage across the regression test suite used for DYNA3D and ParaDyn. It documents the regression test suite at the time of preliminary release 16.1 in September 2016. The columns of the table represent groupings of functionalities, e.g., material models. Each problem in the test suite is represented by a row in the table. All features exercised by the problem are denoted by a check mark (√) in the corresponding column. The definition of “feature” has not been subdivided to its smallest unit of user input, e.g., algorithmic parameters specific to a particular type of contact surface. This represents a judgment to provide code developers and users a reasonable impression of feature coverage without expanding the width of the table by several multiples. All regression testing is run in parallel, typically with eight processors, except problems involving features only available in serial mode. Many are strictly regression tests acting as a check that the codes continue to produce adequately repeatable results as development unfolds; compilers change and platforms are replaced. A subset of the tests represents true verification problems that have been checked against analytical or other benchmark solutions. Users are welcomed to submit documented problems for inclusion in the test suite, especially if they are heavily exercising, and dependent upon, features that are currently underrepresented.
Generalized and synthetic regression estimators for randomized branch sampling
David L. R. Affleck; Timothy G. Gregoire
2015-01-01
In felled-tree studies, ratio and regression estimators are commonly used to convert more readily measured branch characteristics to dry crown mass estimates. In some cases, data from multiple trees are pooled to form these estimates. This research evaluates the utility of both tactics in the estimation of crown biomass following randomized branch sampling (...
Robust regression with CUDA and its application to plasma reflectometry.
Ferreira, Diogo R; Carvalho, Pedro J; Fernandes, Horácio
2015-11-01
In many applications, especially those involving scientific instrumentation data with a large experimental error, it is often necessary to carry out linear regression in the presence of severe outliers which may adversely affect the results. Robust regression methods do exist, but they are much more computationally intensive, making it difficult to apply them in real-time scenarios. In this work, we resort to graphics processing unit (GPU)-based computing to carry out robust regression in a time-sensitive application. We illustrate the results and the performance gains obtained by parallelizing one of the most common robust regression methods, namely, least median of squares. Although the method has a complexity of O(n(3)logn), with GPU computing, it is possible to accelerate it to the point that it becomes usable within the required time frame. In our experiments, the input data come from a plasma diagnostic system installed at Joint European Torus, the largest fusion experiment in Europe, but the approach can be easily transferred to other applications.
Entrepreneurship programs in developing countries : a meta regression analysis
Cho, Yoonyoung; Honorati, Maddalena
2013-01-01
This paper provides a synthetic and systematic review on the effectiveness of various entrepreneurship programs in developing countries. It adopts a meta-regression analysis using 37 impact evaluation studies that were in the public domain by March 2012, and draws out several lessons on the design of the programs. The paper observes wide variation in program effectiveness across different ...
Variable selection in multiple linear regression: The influence of ...
African Journals Online (AJOL)
Akaike's information criterion, influential data cases, Mallows' Cp criterion, multiple linear regression, variable ... may be modified, and hopefully improved, by making use of the influence measures. Four practical ..... information to any practitioner who analyses the fuel data, will surely be helpful in the decision as to whether ...
Bilinear regression model with Kronecker and linear structures for ...
African Journals Online (AJOL)
On the basis of n independent observations from a matrix normal distribution, estimating equations in a flip-flop relation are established and the consistency of estimators is studied. Keywords: Bilinear regression; Estimating equations; Flip- flop algorithm; Kronecker product structure; Linear structured covariance matrix; ...
A Solution to Separation and Multicollinearity in Multiple Logistic Regression.
Shen, Jianzhao; Gao, Sujuan
2008-10-01
In dementia screening tests, item selection for shortening an existing screening test can be achieved using multiple logistic regression. However, maximum likelihood estimates for such logistic regression models often experience serious bias or even non-existence because of separation and multicollinearity problems resulting from a large number of highly correlated items. Firth (1993, Biometrika, 80(1), 27-38) proposed a penalized likelihood estimator for generalized linear models and it was shown to reduce bias and the non-existence problems. The ridge regression has been used in logistic regression to stabilize the estimates in cases of multicollinearity. However, neither solves the problems for each other. In this paper, we propose a double penalized maximum likelihood estimator combining Firth's penalized likelihood equation with a ridge parameter. We present a simulation study evaluating the empirical performance of the double penalized likelihood estimator in small to moderate sample sizes. We demonstrate the proposed approach using a current screening data from a community-based dementia study.
On concurvity in nonlinear and nonparametric regression models
Directory of Open Access Journals (Sweden)
Sonia Amodio
2014-12-01
Full Text Available When data are affected by multicollinearity in the linear regression framework, then concurvity will be present in fitting a generalized additive model (GAM. The term concurvity describes nonlinear dependencies among the predictor variables. As collinearity results in inflated variance of the estimated regression coefficients in the linear regression model, the result of the presence of concurvity leads to instability of the estimated coefficients in GAMs. Even if the backfitting algorithm will always converge to a solution, in case of concurvity the final solution of the backfitting procedure in fitting a GAM is influenced by the starting functions. While exact concurvity is highly unlikely, approximate concurvity, the analogue of multicollinearity, is of practical concern as it can lead to upwardly biased estimates of the parameters and to underestimation of their standard errors, increasing the risk of committing type I error. We compare the existing approaches to detect concurvity, pointing out their advantages and drawbacks, using simulated and real data sets. As a result, this paper will provide a general criterion to detect concurvity in nonlinear and non parametric regression models.
Complete regression of transmissible venereal tumor (TVT)in ...
African Journals Online (AJOL)
Intravenous administration of 0.025mg/kg body weight vincristine sulphate, in normal saline, in four weekly doses led to complete regression of lesions, within 35 days, in 4 mongrel dogs and 6 bitches with histologically diagnosed transmissible venereal tumour (TVT). Early side effects observed in the dogs, such as ...
Caudal Regression Syndrome/neurogenic bladder presented as ...
African Journals Online (AJOL)
Burhan M. Edrees
Caudal Regression Syndrome/neurogenic bladder presented as recurrent urinary tract infection. Burhan M. Edrees. Department of Pediatrics, Medical College, Umm Al-Qura University, Saudi .... The pulse rate was 129 per minute, respiratory rate was 40 per ... strated abnormalities in the structures and functions of the renal.
Evaluation of random forest regression for prediction of breeding ...
Indian Academy of Sciences (India)
cation of the random forest (RF), a model-free ensemble learning method, is not widely used for prediction. In this study, the ... [Sarkar R. K., Rao A. R., Meher P. K., Nepolean T. and Mohapatra T. 2015 Evaluation of random forest regression for prediction of breeding value from .... Ten-fold cross validation technique (Stone.
CICAAR - Convolutive ICA with an Auto-Regressive Inverse Model
DEFF Research Database (Denmark)
Dyrholm, Mads; Hansen, Lars Kai
2004-01-01
We invoke an auto-regressive IIR inverse model for convolutive ICA and derive expressions for the likelihood and its gradient. We argue that optimization will give a stable inverse. When there are more sensors than sources the mixing model parameters are estimated in a second step by least squares...
Illustrating bayesian evaluation of informative hypotheses for regression models.
Kluytmans, A.; Schoot, R. van de; Mulder, J.; Hoijtink, H.
2012-01-01
In the present article we illustrate a Bayesian method of evaluating informative hypotheses for regression models. Our main aim is to make this method accessible to psychological researchers without a mathematical or Bayesian background. The use of informative hypotheses is illustrated using two
Spontaneous regression of lumbar herniated disc Case presentation
Directory of Open Access Journals (Sweden)
Chiriac A.
2015-12-01
Full Text Available Intervertebral disc herniation is a common disease that usually requires surgical intervention. However, in some cases, neurological symptoms may improve with conservative treatment. In this article, we present a case with spontaneous regression of extruded lumbar herniated disc correlated with clinical improvement and documented with follow up MRI studies.
Estimation of Students’ Graduation Using Multiple Linear Regression Method
Directory of Open Access Journals (Sweden)
Bintang Dewi Fajar Kurniatullah
2017-04-01
Full Text Available Utilization of students’ academic data to produce information used by management in monitoring students’ study period on Information System Department. Multiple linier regression method will produce multiple linier regression equation used for estimating students’ graduation equipped with prototype. According to analysis carried out by using nine variable SKS1, SKS2, SKS3, SKS4, IPS1, IPS2, IPS3, IPS4, and the number of repeated courses of 2008 to 2012 the multiple linier regression equation is Y = 13.49 + 0.099 X1 + (-0.068 X2 + 0.025 X3 + (-0.059 X4 + (-0.585 X5 + (-0.443 X6 + (-0.155 X7 + (-0.368 X8 + (-0.082 X9. From the equation there is an error of MSE and RMSE that is equal to 0.1168 and 0.3418. The prototype uses a PHP-based program using sublime text and XAMPP. The prototype monitoring the students’ study time in this research is very helpful if supported by management. Keywords: Data mining, multiple linear regression, estimation, monitoring, study time
Are Tobacco Taxes Really Regressive? : Evidence from Chile
Meneses, Francisco; Fuchs, Alan
2017-01-01
Tobacco taxes are deemed regressive, because the poorest families tend to allocate larger shares of their budget to purchase tobacco. However, as taxes also discourage tobacco use, some of the most adverse effects, including higher medical expenses, lower life expectancy at birth, added years of disability among smokers, and reduction in the quality of life, among others, would be reduced....
Bayesian Adaptive Lasso for Ordinal Regression with Latent Variables
Feng, Xiang-Nan; Wu, Hao-Tian; Song, Xin-Yuan
2017-01-01
We consider an ordinal regression model with latent variables to investigate the effects of observable and latent explanatory variables on the ordinal responses of interest. Each latent variable is characterized by correlated observed variables through a confirmatory factor analysis model. We develop a Bayesian adaptive lasso procedure to conduct…
Multiple Regression Analyses in Clinical Child and Adolescent Psychology
Jaccard, James; Guilamo-Ramos, Vincent; Johansson, Margaret; Bouris, Alida
2006-01-01
A major form of data analysis in clinical child and adolescent psychology is multiple regression. This article reviews issues in the application of such methods in light of the research designs typical of this field. Issues addressed include controlling covariates, evaluation of predictor relevance, comparing predictors, analysis of moderation,…