**prediction**8791

The number of undocumented immigrants in the United States: Estimates based on demographic modeling with data from 1990 to 2016

5 days ago by suitable

We apply standard demographic principles of inflows and outflows to estimate the number of undocumented immigrants in the United States, using the best available data, including some that have only recently become available. Our analysis covers the years 1990 to 2016. We develop an estimate of the number of undocumented immigrants based on parameter values that tend to underestimate undocumented immigrant inflows and overstate outflows; we also show the probability distribution for the number of undocumented immigrants based on simulating our model over parameter value ranges. Our conservative estimate is 16.7 million for 2016, nearly fifty percent higher than the most prominent current estimate of 11.3 million, which is based on survey data and thus different sources and methods. The mean estimate based on our simulation analysis is 22.1 million, essentially double the current widely accepted estimate.

demography
modelling
prediction
U.S.A.
Yale
2018
5 days ago by suitable

Prediction Interval for Autoregressive Time Series via Oracally Efficient Estimation of Multi‐Step‐Ahead Innovation Distribution Function - Kong - 2018 - Journal of Time Series Analysis - Wiley Online Library

9 days ago by cshalizi

"A kernel distribution estimator (KDE) is proposed for multi‐step‐ahead prediction error distribution of autoregressive time series, based on prediction residuals. Under general assumptions, the KDE is proved to be oracally efficient as the infeasible KDE and the empirical cumulative distribution function (cdf) based on unobserved prediction errors. Quantile estimator is obtained from the oracally efficient KDE, and prediction interval for multi‐step‐ahead future observation is constructed using the estimated quantiles and shown to achieve asymptotically the nominal confidence levels. Simulation examples corroborate the asymptotic theory."

in_NB
prediction
time_series
statistics
kernel_estimators
9 days ago by cshalizi

Administration of Research in a Research Corporation

1956 bretvictor rand research administration communication prediction behavior applied time institutions corporation university technology ww2 interdisciplinary science preference search philosophy specialization framework contextualmap datavisualization

11 days ago by quarry

1956 bretvictor rand research administration communication prediction behavior applied time institutions corporation university technology ww2 interdisciplinary science preference search philosophy specialization framework contextualmap datavisualization

11 days ago by quarry

RAIM SAPT — Getting Started - FAA

16 days ago by pierredv

Welcome to the web site for the Receiver Autonomous Integrity Monitoring (RAIM) Service Availability Prediction Tool (SAPT).

This website offers a Grid Display Tool and Summary Displays which can be used to graphically view RAIM outage predictions for specific equipment configurations.

FAA
RAIM
receiver
prediction
aviation
GPS
This website offers a Grid Display Tool and Summary Displays which can be used to graphically view RAIM outage predictions for specific equipment configurations.

16 days ago by pierredv

On the Sensitivity of Granger Causality to Errors‐In‐Variables, Linear Transformations and Subsampling - Anderson - - Journal of Time Series Analysis - Wiley Online Library

20 days ago by cshalizi

"This article studies the sensitivity of Granger causality to the addition of noise, the introduction of subsampling, and the application of causal invertible filters to weakly stationary processes. Using canonical spectral factors and Wold decompositions, we give general conditions under which additive noise or filtering distorts Granger‐causal properties by inducing (spurious) Granger causality, as well as conditions under which it does not. For the errors‐in‐variables case, we give a continuity result, which implies that: a ‘small’ noise‐to‐signal ratio entails ‘small’ distortions in Granger causality. On filtering, we give general necessary and sufficient conditions under which ‘spurious’ causal relations between (vector) time series are not induced by linear transformations of the variables involved. This also yields transformations (or filters) which can eliminate Granger causality from one vector to another one. In a number of cases, we clarify results in the existing literature, with a number of calculations streamlining some existing approaches."

to:NB
time_series
prediction
granger_causality
measurement
20 days ago by cshalizi

Scenario planning - Wikipedia

21 days ago by kmt

The part of the overall process which is radically different from most other forms of long-range planning is the central section, the actual production of the scenarios. Even this, though, is relatively simple, at its most basic level. As derived from the approach most commonly used by Shell,[36] it follows six steps:[37]

Decide drivers for change/assumptions

Bring drivers together into a viable framework

Produce 7–9 initial mini-scenarios

Reduce to 2–3 scenarios

Draft the scenarios

Identify the issues arising

prediction
strategy
mentat
methodology
tips-and-tricks
Decide drivers for change/assumptions

Bring drivers together into a viable framework

Produce 7–9 initial mini-scenarios

Reduce to 2–3 scenarios

Draft the scenarios

Identify the issues arising

21 days ago by kmt