def METHOD_NAME(self):
    date_range = pd.date_range("2020-05-01", "2020-05-20")
    all_hrrs = self.hrr_data.hrr.unique()
    loc_index_hrr_data = self.hrr_data.set_index(["hrr", "timestamp"])
    sample_hrrs = nr.choice(all_hrrs, 10)
    for hrr in sample_hrrs:
        sub_data = loc_index_hrr_data.loc[hrr]
        sub_data = sub_data.reindex(date_range, fill_value=0)
        res0 = ClaimsHospIndicator.fit(sub_data, date_range[0], hrr)
        # first value is burn-in
        assert np.min(res0["rate"][1:]) > 0
        assert np.max(res0["rate"][1:]) <= 100
        if np.all(np.isnan(res0["se"])):
            assert res0["incl"].sum() == 0
        else:
            # binomial standard error, hence largest possible value is
            # 100 * (0.5 / sqrt(MIN_DEN))
            assert np.nanmax(res0["se"]) <= 100 * (0.5 / np.sqrt(Config.MIN_DEN))
            assert np.nanmin(res0["se"]) > 0
            assert res0["incl"].sum() > 0