145 research outputs found
Inference for Two-stage Experiments under Covariate-Adaptive Randomization
This paper studies inference in two-stage randomized experiments with
covariate-adaptive randomization. Here, by a two-stage randomized experiment,
we mean one in which clusters (e.g., households, schools, or graph partitions)
are first randomly assigned to different levels of treated fraction and then
units within each treated clusters are randomly assigned to treatment or
control according to its selected treated fraction; by covariate-adaptive
randomization, we mean randomization schemes that first stratify according to
baseline covariates and then assign treatment status so as to achieve
``balance'' within each stratum. We study estimation and inference of this
design under two different asymptotic regimes: ``small strata'' and ``large
strata'', which enable us to study a wide range of commonly used designs from
the empirical literature. We establish conditions under which our estimators
are consistent and asymptotically normal and construct consistent estimators of
their corresponding asymptotic variances. Combining these results establishes
the asymptotic validity of tests based on these estimators. We argue that
ignoring covariate information at the design stage can lead to efficiency loss,
and commonly used inference methods that ignore or improperly use covariate
information can lead to either conservative or invalid inference. Then, we
apply our results to studying optimal use of covariate information in two-stage
designs, and show that a certain generalized matched-pair design achieves
minimum asymptotic variance for each proposed estimator. A simulation study and
empirical application confirm the practical relevance of our theoretical
results
Efficiency and power of minimally nonlinear irreversible heat engines with broken time-reversal symmetry
We study the minimally nonlinear irreversible heat engines in which the
time-reversal symmetry for the systems may b e broken. The expressions for the
power and the efficiency are derived, in which the effects of the nonlinear
terms due to dissipations are included. We show that, as within the linear
responses, the minimally nonlinear irreversible heat engines enable attainment
of Carnot efficiency at positive power. We also find that the Curzon-Ahlborn
limit imposed on the efficiency at maximum power can be overcomed if the
time-reversal symmetry is broken
Read, Watch, and Move: Reinforcement Learning for Temporally Grounding Natural Language Descriptions in Videos
The task of video grounding, which temporally localizes a natural language
description in a video, plays an important role in understanding videos.
Existing studies have adopted strategies of sliding window over the entire
video or exhaustively ranking all possible clip-sentence pairs in a
pre-segmented video, which inevitably suffer from exhaustively enumerated
candidates. To alleviate this problem, we formulate this task as a problem of
sequential decision making by learning an agent which regulates the temporal
grounding boundaries progressively based on its policy. Specifically, we
propose a reinforcement learning based framework improved by multi-task
learning and it shows steady performance gains by considering additional
supervised boundary information during training. Our proposed framework
achieves state-of-the-art performance on ActivityNet'18 DenseCaption dataset
and Charades-STA dataset while observing only 10 or less clips per video.Comment: AAAI 201
Inference for Matched Tuples and Fully Blocked Factorial Designs
This paper studies inference in randomized controlled trials with multiple
treatments, where treatment status is determined according to a "matched
tuples" design. Here, by a matched tuples design, we mean an experimental
design where units are sampled i.i.d. from the population of interest, grouped
into "homogeneous" blocks with cardinality equal to the number of treatments,
and finally, within each block, each treatment is assigned exactly once
uniformly at random. We first study estimation and inference for matched tuples
designs in the general setting where the parameter of interest is a vector of
linear contrasts over the collection of average potential outcomes for each
treatment. Parameters of this form include standard average treatment effects
used to compare one treatment relative to another, but also include parameters
which may be of interest in the analysis of factorial designs. We first
establish conditions under which a sample analogue estimator is asymptotically
normal and construct a consistent estimator of its corresponding asymptotic
variance. Combining these results establishes the asymptotic exactness of tests
based on these estimators. In contrast, we show that, for two common testing
procedures based on t-tests constructed from linear regressions, one test is
generally conservative while the other generally invalid. We go on to apply our
results to study the asymptotic properties of what we call "fully-blocked" 2^K
factorial designs, which are simply matched tuples designs applied to a full
factorial experiment. Leveraging our previous results, we establish that our
estimator achieves a lower asymptotic variance under the fully-blocked design
than that under any stratified factorial design which stratifies the
experimental sample into a finite number of "large" strata. A simulation study
and empirical application illustrate the practical relevance of our results
Numerical Modeling of Submicron Particles for Acoustic Concentration in Gaseous Flow
This paper intends to explore the rationality and feasibility of modeling dispersed submicron particles in air by a kinetic-based method called the unified gas-kinetic scheme (UGKS) and apply it to the simulation of particle concentration under a transverse standing wave. A gas-particle coupling scheme is proposed where the gas phase is modeled by the two-dimensional linearized Euler equations (LEE) and, through the analogous behavior between the rarefied gas molecules and the air-suspended particles, a modified UGKS is adopted to estimate the particle dynamics. The Stokes\u27 drag force and the acoustic radiation force applied on particles are accounted for by introducing a velocity-dependent acceleration term in the UGKS formulation. To validate this methodology, the computed concentration patterns are compared with experimental results in the literature. The comparison shows that the adopted LEE-UGKS coupling scheme could well capture the concentration pattern of suspended submicron particles in a channel. In addition, numerical simulations with varying standing wave amplitudes, different acoustic radiation force to drag force ratios, and mean flow velocities are conducted. Their respective influences on the particle concentration pattern and efficiency are analyzed
On the Efficiency of Finely Stratified Experiments
This paper studies the efficient estimation of a large class of treatment
effect parameters that arise in the analysis of experiments. Here, efficiency
is understood to be with respect to a broad class of treatment assignment
schemes for which the marginal probability that any unit is assigned to
treatment equals a pre-specified value, e.g., one half. Importantly, we do not
require that treatment status is assigned in an i.i.d. fashion, thereby
accommodating complicated treatment assignment schemes that are used in
practice, such as stratified block randomization and matched pairs. The class
of parameters considered are those that can be expressed as the solution to a
restriction on the expectation of a known function of the observed data,
including possibly the pre-specified value for the marginal probability of
treatment assignment. We show that this class of parameters includes, among
other things, average treatment effects, quantile treatment effects, local
average treatment effects as well as the counterparts to these quantities in
experiments in which the unit is itself a cluster. In this setting, we
establish two results. First, we derive a lower bound on the asymptotic
variance of estimators of the parameter of interest in the form of a
convolution theorem. Second, we show that the n\"aive method of moments
estimator achieves this bound on the asymptotic variance quite generally if
treatment is assigned using a "finely stratified" design. By a "finely
stratified" design, we mean experiments in which units are divided into groups
of a fixed size and a proportion within each group is assigned to treatment
uniformly at random so that it respects the restriction on the marginal
probability of treatment assignment. In this sense, "finely stratified"
experiments lead to efficient estimators of treatment effect parameters "by
design" rather than through ex post covariate adjustment
Revisiting the Analysis of Matched-Pair and Stratified Experiments in the Presence of Attrition
In this paper we revisit some common recommendations regarding the analysis
of matched-pair and stratified experimental designs in the presence of
attrition. Our main objective is to clarify a number of well-known claims about
the practice of dropping pairs with an attrited unit when analyzing
matched-pair designs. Contradictory advice appears in the literature about
whether or not dropping pairs is beneficial or harmful, and stratifying into
larger groups has been recommended as a resolution to the issue. To address
these claims, we derive the estimands obtained from the difference-in-means
estimator in a matched-pair design both when the observations from pairs with
an attrited unit are retained and when they are dropped. We find limited
evidence to support the claims that dropping pairs helps recover the average
treatment effect, but we find that it may potentially help in recovering a
convex weighted average of conditional average treatment effects. We report
similar findings for stratified designs when studying the estimands obtained
from a regression of outcomes on treatment with and without strata fixed
effects
Inference in Cluster Randomized Trials with Matched Pairs
This paper considers the problem of inference in cluster randomized trials
where treatment status is determined according to a "matched pairs" design.
Here, by a cluster randomized experiment, we mean one in which treatment is
assigned at the level of the cluster; by a "matched pairs" design we mean that
a sample of clusters is paired according to baseline, cluster-level covariates
and, within each pair, one cluster is selected at random for treatment. We
study the large sample behavior of a weighted difference-in-means estimator and
derive two distinct sets of results depending on if the matching procedure does
or does not match on cluster size. We then propose a variance estimator which
is consistent in either case. We also study the behavior of a randomization
test which permutes the treatment status for clusters within pairs, and
establish its finite sample and asymptotic validity for testing specific null
hypotheses
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