Large Sample Methods in Statistics

Abstract-This paper considers distributed massive MIMO networks where a large number of antennas, either collocated or geographically scattered over a region, communicate with mobile users. This communication is impaired by interference from similar transmissions in adjacent regions and by noise. Focusing on zero-forcing (ZF) reception, we derive simple expressions that very accurately approximate the instantaneous signal-to-interference-plus-noise ratio (SINR) and the ergodic spectral efficiency of an arbitrary user. These expressions enable shortcutting any assessment of the network-level performance, either analytical or simulation-based.

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“…For large N , this converges to the mean by the strong law of large numbers for non-identically distributed variables [13]. Thus, for growing N ,…”

Section: B Zf Receivermentioning

confidence: 76%

Analytical handle for ZF reception in distributed massive MIMO

Senanayake

Lozano

Smith

et al. 2016

2016 50th Asilomar Conference on Signals, Systems and Computers

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“…The consistency comes as a result of the consistency of θ,γ,β,λ 0 by the Slutsky Theorem (Sen and Singer, 1993).…”

Section: Maximum Likelihood Estimator and Asymptotic Propertiesmentioning

confidence: 97%

An estimation method of marginal treatment effects on correlated longitudinal and survival outcomes

Pan

2011

Statistics and Its Interface

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This paper concerns treatment effects on correlated longitudinal and time to event processes. The marginal mean of the longitudinal outcome in the presence of event occurrence is often of interest from clinical and epidemiological perspectives. When the probability of the event is treatmentdependent, differences between treatment-specific longitudinal outcome means are usually not constant over time. In this paper, we propose a measure to quantify treatment effects using time-varying differences in longitudinal outcome means, which accounts for the constantly changing population composition due to event occurrences. Generalized linear mixed models (GLMM) and proportional hazards (PH) models are employed to construct the proposed measure. The proposed method is applied to analyze the motivating data arising from the study of weight loss in the Diabetes Prevention Program where weights after diabetes occurrence are systematically different from diabetes-free weights.

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“…, because the jackknife variance estimator is more stable than other variance estimator (Sen and Singer, 1993;Sidak et al, 1999;Krishnaiah and Sen, 1985;Sen, 1977).…”

Section: Proposed Test Statistics and P P P-valuesmentioning

confidence: 99%

Multiple Testing in Genomic Sequences Using Hamming Distance

Kang¹

2012

Communications for Statistical Applications and Methods

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High-dimensional categorical data models with small sample sizes have not been used extensively in genomic sequences that involve count (or discrete) or purely qualitative responses. A basic task is to identify differentially expressed genes (or positions) among a number of genes. It requires an appropriate test statistics and a corresponding multiple testing procedure so that a multivariate analysis of variance should not be feasible. A family wise error rate(FWER) is not appropriate to test thousands of genes simultaneously in a multiple testing procedure. False discovery rate(FDR) is better than FWER in multiple testing problems. The data from the 2002-2003 SARS epidemic shows that a conventional FDR procedure and a proposed test statistic based on a pseudo-marginal approach with Hamming distance performs better.

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Large Sample Methods in Statistics

Cited by 299 publications

References 0 publications

Analytical handle for ZF reception in distributed massive MIMO

Analytical handle for ZF reception in distributed massive MIMO

An estimation method of marginal treatment effects on correlated longitudinal and survival outcomes

Multiple Testing in Genomic Sequences Using Hamming Distance

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