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Serial Dependence (serial + dependence)

Distribution by Scientific Domains
Mathematics and Statistics40%

Selected Abstracts

Fragment-Parallel Composite and Filter

COMPUTER GRAPHICS FORUM, Issue 4 2010
Anjul Patney
We present a strategy for parallelizing the composite and filter operations suitable for an order-independent rendering pipeline implemented on a modern graphics processor. Conventionally, this task is parallelized across pixels/subpixels, but serialized along individual depth layers. However, our technique extends the domain of parallelization to individual fragments (samples), avoiding a serial dependence on the number of depth layers, which can be a constraint for scenes with high depth complexity. As a result, our technique scales with the number of fragments and can sustain a consistent and predictable throughput in scenes with both low and high depth complexity, including those with a high variability of depth complexity within a single frame. We demonstrate composite/filter performance in excess of 50M fragments/sec for scenes with more than 1500 semi-transparent layers. [source]

Debating the greening vs. browning of the North American boreal forest: differences between satellite datasets

GLOBAL CHANGE BIOLOGY, Issue 2 2010
DOMINGO ALCARAZ-SEGURA
Abstract A number of remote sensing studies have evaluated the temporal trends of the normalized difference vegetation index (NDVI or vegetation greenness) in the North American boreal forest during the last two decades, often getting quite different results. To examine the effect that the use of different datasets might be having on the estimated trends, we compared the temporal trends of recently burned and unburned sites of boreal forest in central Canada calculated from two datasets: the Global Inventory, Monitoring, and Modeling Studies (GIMMS), which is the most commonly used 8 km dataset, and a new 1 km dataset developed by the Canadian Centre for Remote Sensing (CCRS). We compared the NDVI trends of both datasets along a fire severity gradient in order to evaluate the variance in regeneration rates. Temporal trends were calculated using the seasonal Mann,Kendall trend test, a rank-based, nonparametric test, which is robust against seasonality, nonnormality, heteroscedasticity, missing values, and serial dependence. The results showed contrasting NDVI trends between the CCRS and the GIMMS datasets. The CCRS dataset showed NDVI increases in all recently burned sites and in 50% of the unburned sites. Surprisingly, the GIMMS dataset did not capture the NDVI recovery in most burned sites and even showed NDVI declines in some burned sites one decade after fire. Between 50% and 75% of GIMMS pixels showed NDVI decreases in the unburned forest compared with <1% of CCRS pixels. Being the most broadly used dataset for monitoring ecosystem and carbon balance changes, the bias towards negative trends in the GIMMS dataset in the North American boreal forest has broad implications for the evaluation of vegetation and carbon dynamics in this region and globally. [source]

Range Unit-Root (RUR) Tests: Robust against Nonlinearities, Error Distributions, Structural Breaks and Outliers

JOURNAL OF TIME SERIES ANALYSIS, Issue 4 2006
Felipe Aparicio
Abstract., Since the seminal paper by Dickey and Fuller in 1979, unit-root tests have conditioned the standard approaches to analysing time series with strong serial dependence in mean behaviour, the focus being placed on the detection of eventual unit roots in an autoregressive model fitted to the series. In this paper, we propose a completely different method to test for the type of long-wave patterns observed not only in unit-root time series but also in series following more complex data-generating mechanisms. To this end, our testing device analyses the unit-root persistence exhibited by the data while imposing very few constraints on the generating mechanism. We call our device the range unit-root (RUR) test since it is constructed from the running ranges of the series from which we derive its limit distribution. These nonparametric statistics endow the test with a number of desirable properties, the invariance to monotonic transformations of the series and the robustness to the presence of important parameter shifts. Moreover, the RUR test outperforms the power of standard unit-root tests on near-unit-root stationary time series; it is invariant with respect to the innovations distribution and asymptotically immune to noise. An extension of the RUR test, called the forward,backward range unit-root (FB-RUR) improves the check in the presence of additive outliers. Finally, we illustrate the performances of both range tests and their discrepancies with the Dickey,Fuller unit-root test on exchange rate series. [source]

Group inspection of dependent binary processes

QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, Issue 2 2009
Christian H. Weiß
Abstract We consider serially dependent binary processes, how they occur in several fields of practice. If such a process cannot be monitored continuously, because of process speed for instance, then one can analyze connected segments instead, where two successive segments have a sufficiently large time-lag. Nevertheless, the serial dependence has to be considered at least within the segments, i.e. the distribution of the segment sums is not binomial anymore. We propose the Markov binomial distribution to approximate the true distribution of the segment sums. Based on this distribution, we develop a Markov np chart and a Markov exponentially weighted moving average (EWMA) chart. We show how average run lengths (ARLs) can be computed exactly for both types of chart. Based on such ARL computations, we derive recommendations for chart design and investigate the out-of-control performance. A real-data example illustrates the application of these charts in practice. Copyright © 2008 John Wiley & Sons, Ltd. [source]

Persistence and Heterogeneity in Habitat Selection Studies Using Radio Telemetry

BIOMETRICS, Issue 2 2003
Fred L. Ramsey
Summary Biologists attach radio transmitters to animals so that the animals' movements through their preferred habitats can be followed. To analyze the resulting sequences of visited habitat classes, McCracken, Manly, and Vander Heyden (1998, Journal of Agricultural, Biological, and Environmental Statistics3(3), 268,279) proposed an independent multinomial selections (IMS) model. Two issues that arise when using this approach are: (i) serial dependence possibly affects measures of uncertainty; and (ii) individual animals from the population studied may exhibit heterogeneity in their selection patterns. We develop two single-parameter extensions of the IMS model to address these issues. A Markov chain model allows for persistence in the habitat class previously visited. Heterogeneity is modeled by assuming the population of animal selection patterns follows a Dirichlet distribution, from which the study animals are a random sample. We show that these persistence and heterogeneity characteristics are present in the study by McCracken et al. (1998) of bear movements. Simulations demonstrate that failure to account for persistence or heterogeneity when either is present can seriously misrepresent measures of uncertainty. [source]