Estimating an unknown cumulative distribution function (cdf) from data, either as a statistical object of interest or as an input to a downstream optimization problem, is fundamental in operations. In practice, however, distribution estimation is often complicated by incomplete knowledge of the distribution's structure and limited, censored data. To address the first complication, we study distributions satisfying failure-rate shape constraints, especially increasing failure rate (IFR), rather than assuming a fully specified parametric family. To address the second, we consider noisy quantile data: at finitely many prespecified knots, each observation records only whether an independent sample lies below or above the knot. This combination arises naturally in pricing, reliability, and healthcare applications. We formulate the IFR-constrained maximum likelihood estimator and show that the original problem is infinite-dimensional and non-convex. We then develop a tractable two-step approach that solves a finite-dimensional convex optimization problem over transformed knot values and reconstructs a full cdf through shape-preserving interpolation. We establish finite-sample error bounds and convergence rates, yielding practical guidance for offline data collection. We also extend the framework to failure-rate-average, new-better-than-used, and generalized-failure-rate properties. Numerical experiments and case studies in revenue management and reliability demonstrate strong goodness-of-fit and improved downstream decision quality.

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