Sentences with phrase «statistical modeling approaches»

Not exact matches

We say, «We take a statistical approach, not a physical - modeling approach — and here are the upsides and the downsides.»

Another promising approach involves combining physics, statistical modeling and computing to derive sound projections for the future of ice sheets.

Sufficient information should be supplied to allow readers to judge whether any assumptions necessary for the validity of statistical approaches (e.g., data are normally distributed, survival data are consistent with proportional hazards in a Cox regression model) have been verified.

But pharmacometrics — the modeling and statistical analysis of drug metabolism data — touts a more tailored, predictive approach to safety and efficacy.

«Our study shows how application of interdisciplinary statistical approaches, coupled with informed models of collective motion can help extract useful biological information about social interactions in schools of fish.

The new study combined a statistical approach implemented in a unique software modeling programme called IONONEST with data from the Kilpisjärvi Atmospheric and Imaging Receiver Array (KAIRA) radio telescope in Finland, which is capable of making sensitive broadband measurements of the absorption caused to the cosmic radio background by the ionsophere.

But even if the growth of the economy is not algorithmic, an algorithmic approach may still be of use in finding statistical features of model economies for comparison to the real one.

To improve on that, Dee and his colleagues used a computerised statistical approach known as Bayesian modelling.

And, because it eschews complex physical climate models for a statistical, data - driven modeling approach, it is relatively «simple and parsimonious,» Kalra said.

These methods make use of modelling of the 3D structures of proteins and their ligands, and of original statistical approaches to the increasing amount of data on structures and interactions (Figure 1).

Dr Louella Vasquez (PhD Physics, WTSI Postdoctoral Fellow) is responsible for the statistical analyses and development of new modeling approaches.

Our fundamental approach to interacting with the world — collecting reproducible large datasets, using state - of - the - art detectors, reconstructing remote phenomena, understanding the world through physical models, and employing sound statistical analyses of significance — are highly congruent with the modus operandi of earth scientists.

Our lab blends computation and theory in close collaboration with experimentalists and clinicians, developing machine learning approaches and statistical models of next - generation sequencing data.

Important topics include design and task specification, planned group comparisons, behavioral performance metrics, imaging details, data pre-processing, intersubject registration, statistical modeling details for both the individual and group level, and statistical inference including approach to multiple comparisons correction.

The need for a systematic approach to tractography validation and for a framework to perform statistical model testing in individual brains has been claimed (Pestilli Nature: Scientific Data 2015).

Here, we present a new statistical approach called oncomix, that models transcriptional heterogeneity in tumor and adjacent normal (i.e. tumor - free) using bimodality to find oncogene candidates.

The most sophisticated approach uses a statistical technique known as a value - added model, which attempts to filter out sources of bias in the test - score growth so as to arrive at an estimate of how much each teacher contributed to student learning.

APA has expertise in a wide range of approaches that we leverage in conducting our work, including: data analysis, cost modeling, statistical modeling, surveys, interviews, focus groups, facilitation, and literature reviews.

It's worth reminding readers that there are two general approaches to modeling — one is the statistical method, and the other is the dynamical method (sometimes called first principles), and there are all manner of blends.

(in general, whether for future projections or historical reconstructions or estimates of climate sensitivity, I tend to be sympathetic to arguments of more rather than less uncertainty because I feel like in general, models and statistical approaches are not exhaustive and it is «plausible» that additional factors could lead to either higher or lower estimates than seen with a single approach.

Individual responses continue to be based on a range of methods: statistical, numerical models, comparison with previous rates of sea ice loss, composites of several approaches, estimates based on various non sea ice datasets and trends, and subjective information (the heuristic category).

Wood, A. W., L. R. Leung, V. Sridhar, and D. P. Lettenmaier, 2004: Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs (link is external).

Wood, A.W., L.R Leung, V. Sridhar, and D.P. Lettenmaier, 2004: Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs.

it is important to recognize that an inherent difficulty of testing null hypotheses is that one can not confirm (statistically) the hypothesis of no effect.While robustness checks (reported in the appendix), as well as p values that never approach standard levels of statistical significance, provide some confidence that the results do not depend on model specification or overly strict requirements for statistical significance, one can not entirely dismiss the possibility of a Type II error.

These approaches include those of Trenberth, Foster & Rahmstorf, Kosaka & Xie, Lean's Statistical Climate Model, Cowtan & Way, and always Hansen in the background.

Well, if the CSALT approach that I use and the NRL statistical climate model espoused by Judith Lean are not good examples of energy budgeting, then Nic Lewis will have to think again on what he is proposing.

Under a logical approach to validation, each prediction that is made by a predictive model is viewed as making a claim about the outcome of a statistical event and this claim is viewed as a logical proposition.

Individual responses were based on a range of methods: statistical, numerical models, comparison with previous observations and rates of ice loss, and composites of several approaches.

The July 2010 Sea Ice Outlook Report is based on a synthesis of 17 individual pan-Arctic estimates using a wide range of methods: statistical, numerical models, comparison with observations and rates of ice loss, composites of several approaches.

The individual responses were based on a range of methods: statistical, numerical models, comparison with previous observations and rates of ice loss, or composites of several approaches; details can be found in the individual outlooks available at the bottom of this page.

The individual responses were based on a range of methods: statistical, numerical models, comparison with previous observations and rates of ice loss, or composites of several approaches; details can be found in the individual outlooks available at the end of this report.

To monitor live forest carbon at broad scales, researchers in the US have developed an empirical approach that mixes field data, statistical modelling, remotely sensed time - series imagery and small - footprint lidar.

As an alternative to this approach, our statistical model is only based on the additivity assumption; the proposed method does not regress observations onto expected response patterns.

In this study, an automated statistical downscaling (ASD) regression - based approach inspired by the SDSM method (statistical downscaling model) developed by Wilby, R.L., Dawson, C.W., Barrow, E.M. [2002.

So by analogy with Kolmogorov what is the problem of the statistical approach to climatology which is the only one possible as I have developped it above with the T = Ta + u model?

There IS a heat source and a physical reality, that requires no forcing to give it super powers as with puny CO2 the palnts gobble up as much as they can get of, in fact.And explains the stable ice age and the Milankovitch linked interglacials, and how that sawtooth between repeated and predicatble limits can be driven using known energy sources, specific heats and masses, plus simple deterministic physics, no statistical models or Piltdown Mann data set approaches.

The potential to make skillful forecasts on these timescales, and the ability to do so, is investigated by means of predictability studies and retrospective forecasts (termed hindcasts) using climate models and statistical approaches.

Statistical downscaling of general circulation model outputs to precipitation, evaporation and temperature using a key station approach

In Sect. 2, we describe the model ensembles and the application of the rank histogram approach, including a description of the statistical method used to define the reliability of model ensembles from the rank histogram, and a method for handling uncertainties in the observations.

Conference topics of emphasis will include dynamics, high performance computing, numerical analysis, cloud systems behavior, data assimilation, dimension reduction, uncertainty quantification, model hierarchy, and statistical approaches.

If I were to dump a billion microscopic ice particles into a tank of hot water (di - hydrogen monoxide), your statistical approach would insist that a few of them may have warmed the water (hydrogen hydroxide), especially using a computer model.

Although climate models work best with the addition of CO2 as a forcing, to statistically determine causation, a cointegration statistical approach is necessary to observe the CO2 signal.

The most effective downscaling approaches use the statistical correlations of local weather to larger scale patterns and use model projections for those patterns to estimate changes in local weather regimes.

The monte carlo approach i was referring to was the one described in the Rahmstorf / Coumou paper whereby one fits a statistical model to a time series with random and deterministic components, then generate a dataset of 1000s's of model runs with the same deterministic component but varying randoms components and then calculate statistics on the dataset see update» PS (27 October)» on the main post for a better explanation.

Our approach is anchored in statistical analysis of time - series datasets using models of growth and diffusion, particularly Lotka - Volterra dynamical systems.

In general, the heuristic approaches forecast a mean September extent around 4.1 million km2, whereas the statistical and dynamical modeling approaches both suggest mean September extent near 5.1 million km2, with the dynamical modeling contributions showing a narrower range.

Fred's primary research interests include social support dynamics in romantic couples, the effects of context on relationships, relationships and health & well - being, issues of the self in relationships, and complex statistical approaches to modeling relationship phenomena.

The metaphor helps intuitively explain a statistical approach that is central in debates about the underlying nature of temperament (intelligence (e.g., «g»), personality (e.g., big 5), and attitudes (e.g., ABC model).

She has technical expertise in a wide range of statistical techniques used in the social sciences, including structural equation modeling, confirmatory factor analysis and MIMIC approaches to measurement, path modeling, regression analysis (e.g., linear, logistic, Poisson), latent class analysis, hierarchical linear models (including growth curve modeling), latent transition analysis, mixture modeling, item response theory, as well as more commonly used techniques drawing from classical test theory (e.g., reliability analysis through Cronbach's alpha, exploratory factor analysis, uni - and multivariate regression, correlation, ANOVA, etc).

Her approach to consulting is to translate researchers» articulated research questions and hypotheses into statistical models and to translate results of these models back into plain English that can be understood by individuals both within and outside academia.